From 8a4210f3803eed236a51af9081da330a62eaf2fb Mon Sep 17 00:00:00 2001 From: ylhan Date: Thu, 20 Aug 2026 17:42:14 +0800 Subject: [PATCH] =?UTF-8?q?=E5=B0=86=20note=20=E7=AC=94=E8=AE=B0=E5=BA=93?= =?UTF-8?q?=E5=90=8C=E6=AD=A5=E5=88=B0=20note=5Fsync=EF=BC=8CAI=5Fcompiler?= =?UTF-8?q?=20=E4=BD=9C=E4=B8=BA=20submodule?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitmodules | 3 + .obsidian/app.json | 9 + .obsidian/appearance.json | 1 + .obsidian/core-plugins.json | 33 ++ .obsidian/graph.json | 22 ++ .obsidian/workspace.json | 254 +++++++++++++++ AI_compiler | 1 + autoware_w7/w7.md | 233 ++++++++++++++ ros/.obsidian/app.json | 3 + ros/.obsidian/appearance.json | 1 + ros/.obsidian/core-plugins.json | 33 ++ ros/.obsidian/graph.json | 30 ++ ros/.obsidian/workspace.json | 201 ++++++++++++ ros/ros2/nav2/debug.md | 211 ++++++++++++ ros/ros2/nav2/nav2.md | 0 ros/ros2/nav2/nav2_bringup_gps_demo.md | 356 +++++++++++++++++++++ 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image 20260402112914.png", + "ros/ros2/nav2/nav2.md", + "ros/履带底盘机械臂.md", + "ros/ros2/安装配置/ros2_structure.md", + "ros/ros2/概念/基础概念.md" + ] +} \ No newline at end of file diff --git a/AI_compiler b/AI_compiler new file mode 160000 index 0000000..86ed9b3 --- /dev/null +++ b/AI_compiler @@ -0,0 +1 @@ +Subproject commit 86ed9b3b1a5f80f37261f4138c35fc8bbb94949d diff --git a/autoware_w7/w7.md b/autoware_w7/w7.md new file mode 100644 index 0000000..f8c7cc9 --- /dev/null +++ b/autoware_w7/w7.md @@ -0,0 +1,233 @@ +# Autoware W7 启动概览 + + + +以下内容基于命令： + + + +ros2 launch autoware_launch autoware.launch.xml \ + +  sensor_model:=sample_sensor_kit \ + +  vehicle_model:=sample_vehicle \ + +  vehicle_id:=W7 \ + +  map_path:=/media/nvidia/nvme0n1pnvme0n1p1/autoware_map/test_map_A4_and_PARK/ + + + +## 模块关系（Mermaid） + + + +```mermaid + +graph LR + +  subgraph SENSING["感知层 Sensing"] + +    SENSINGN(Pointcloud / Camera / Radar) + +    PCCTR[pointcloud_container] + +  end + + + +  subgraph MAPGRP["地图 Map"] + +    PC_MAP(Pointcloud Map) + +    LL_MAP(Lanelet2 Map) + +  end + + + +  subgraph LOCAL["定位 Localization"] + +    LOCN(Localization) + +  end + + + +  subgraph PER["感知 Perception"] + +    PERN(Detection / Tracking / TLR) + +  end + + + +  subgraph PLAN["规划 Planning"] + +    PLANN(Mission / Behavior / Motion) + +  end + + + +  subgraph CTRL["控制 Control"] + +    CTRLN(Trajectory Follower) + +  end + + + +  subgraph VEH["车辆 Vehicle"] + +    VIF(Vehicle Interface) + +    RSP(robot_state_publisher) + +  end + + + +  RVIZ[RViz2] + + + +  SENSINGN -->|点云/图像| PERN + +  SENSINGN -->|点云| LOCN + +  PCCTR -->|聚合点云| PERN + +  PCCTR -->|聚合点云| LOCN + +  PC_MAP -->|点云地图| LOCN + +  LL_MAP -->|lanelet2| PLANN + +  LOCN -->|pose/tf| PLANN + +  PERN -->|detected_objects| PLANN + +  PLANN -->|trajectory| CTRLN + +  CTRLN -->|vehicle_cmd| VIF + +  VIF -->|状态| PLANN + +  RSP -->|tf| RVIZ + +``` + + + +## 主要模块与说明 + + + +- Vehicle：车辆描述与接口（`robot_state_publisher`、`vehicle_interface`、`raw_vehicle_cmd_converter`） + +- System：系统监控、诊断（health、diagnostics） + +- Map：点云地图 (`pointcloud_map.pcd`) 与 Lanelet2 (`lanelet2_map.osm`) 加载 + +- Sensing：传感器驱动、预处理、`pointcloud_container` + +- Localization：ND T / yabloc / eagleye / EKF 等定位组件，依赖点云与地图 + +- Perception：地面分割、检测、跟踪、交通灯识别、融合 + +- Planning：mission、behavior、motion（包含 diffusion_planner 可选） + +- Control：轨迹跟踪（MPC/PID 等）并输出 `vehicle_cmd` + +- API/Tools：Autoware API、RViz2 等 + + + +## 收集到的关键节点（从相关 launch 文件） + + + +- `robot_state_publisher` （Vehicle） + +- `raw_vehicle_cmd_converter` （Vehicle Interface） + +- `map_hash_generator` （Map） + +- `trajectory_relay`（topic_tools relay，将 `/planning/trajectory` 转发到 `/planning/scenario_planning/trajectory`） + + + +- Sensing / Sensor kit 示例节点： + +  - `tl_camera_info_relay`（topic_tools relay for camera） + +  - `tl_compressed_image_relay`（topic_tools relay） + +  - `imu_to_autoware_orientation`（GNSS/IMU 预处理） + +  - `imu_data_compat_relay`（topic_tools relay） + +  - `tag_serial_driver`（IMU 驱动） + + + +- Localization / eagleye 相关节点（部分）： + +  - `gnss_converter_node`（eagleye_gnss_converter） + +  - `eagleye_output_twist_relay`（topic_tools relay） + +  - `twist_relay`, `tf_converted_imu`, `velocity_scale_factor_node`, `yaw_rate_offset_*`, `heading_*`, `position`, `smoothing`, `height`, `monitor`（eagleye_rt 包内多个辅助节点） + + + +- Perception 相关节点（示例）： + +  - `simple_object_merger`（autoware_simple_object_merger） + +  - `empty_objects_publisher`（autoware_dummy_perception_publisher，用于仿真/占位） + + + +- Planning / Diffusion 示例节点： + +  - `velocity_smoother`（demo_nodes_cpp parameter_blackboard 用作示例） + + + +- Control 相关节点： + +  - `controller_node_exe`（autoware_smart_mpc_trajectory_follower 的 Python 接口） + + + +- Simulator / 工具节点（示例）： + +  - `elevation_map_loader`, `autoware_vehicle_velocity_converter`, `autoware_scenario_simulator_v2_adapter_node` + + + +> 注：上面列举的是主要/代表性节点，Autoware 启动会包含大量子包内的节点（许多由组件的 `launch.xml` 或 `launch.py` 再次 include）。如需我逐个组件展开完整节点列表，我可以继续递归读取每个组件的 launch 目录并生成更详尽的清单。 + + + +--- + + + +文件已保存到： + +- 工作区（副本，可点击查看）： `docs/autoware_w7/Autoware_W7_overview.md` + +- 你的指定路径副本： `/media/nvidia/nvme0n1pnvme0n1p1/ylhan/autoware_w7/Autoware_W7_overview.md` + + + +如果需要，我可以： + +- 生成更详细的每个子包启动节点清单并保存为文本或 CSV； + +- 将 Mermaid 图另存为 SVG； + +- 针对 `vehicle_interface.launch.xml`、`sensing.launch.xml` 等列出完整的 node 列表。 \ No newline at end of file diff --git a/ros/.obsidian/app.json b/ros/.obsidian/app.json new file mode 100644 index 0000000..e609a07 --- /dev/null +++ b/ros/.obsidian/app.json @@ -0,0 +1,3 @@ +{ + "promptDelete": false +} \ No newline at end of file diff --git a/ros/.obsidian/appearance.json b/ros/.obsidian/appearance.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/ros/.obsidian/appearance.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ros/.obsidian/core-plugins.json b/ros/.obsidian/core-plugins.json new file mode 100644 index 0000000..639b90d --- /dev/null +++ b/ros/.obsidian/core-plugins.json @@ -0,0 +1,33 @@ +{ + "file-explorer": true, + "global-search": true, + "switcher": true, + "graph": true, + "backlink": true, + "canvas": true, + "outgoing-link": true, + "tag-pane": true, + "footnotes": false, + "properties": true, + "page-preview": true, + "daily-notes": true, + "templates": true, + "note-composer": true, + "command-palette": true, + "slash-command": false, + "editor-status": true, + "bookmarks": true, + "markdown-importer": false, + "zk-prefixer": false, + "random-note": false, + "outline": true, + "word-count": true, + "slides": false, + "audio-recorder": false, + "workspaces": false, + "file-recovery": true, + "publish": false, + "sync": true, + "bases": true, + "webviewer": false +} \ No newline at end of file diff --git a/ros/.obsidian/graph.json b/ros/.obsidian/graph.json new file mode 100644 index 0000000..5185ea5 --- /dev/null +++ b/ros/.obsidian/graph.json @@ -0,0 +1,30 @@ +{ + "collapse-filter": false, + "search": "", + "showTags": false, + "showAttachments": false, + "hideUnresolved": false, + "showOrphans": true, + "collapse-color-groups": false, + "colorGroups": [ + { + "query": "tag:", + "color": { + "a": 1, + "rgb": 14048348 + } + } + ], + "collapse-display": true, + "showArrow": false, + "textFadeMultiplier": 0, + "nodeSizeMultiplier": 1, + "lineSizeMultiplier": 1, + "collapse-forces": true, + "centerStrength": 0.518713248970312, + "repelStrength": 10, + "linkStrength": 1, + "linkDistance": 250, + "scale": 1, + "close": true +} \ No newline at end of file diff --git a/ros/.obsidian/workspace.json b/ros/.obsidian/workspace.json new file mode 100644 index 0000000..3ba29fb --- /dev/null +++ b/ros/.obsidian/workspace.json @@ -0,0 +1,201 @@ +{ + "main": { + "id": "914c9e8d9774b62f", + "type": "split", + "children": [ + { + "id": "22be749e4ad854f0", + "type": "tabs", + "children": [ + { + "id": "0f14a7f3e342ee77", + "type": "leaf", + "state": { + "type": "markdown", + "state": { + "file": "ros2/概念/基础概念.md", + "mode": "preview", + "source": false + }, + "icon": "lucide-file", + "title": "基础概念" + } + } + ] + } + ], + "direction": "vertical" + }, + "left": { + "id": "3d550c51e4dc52dd", + "type": "split", + "children": [ + { + "id": "64ee6b66868da0e5", + "type": "tabs", + "children": [ + { + "id": "4a3332297ce1ac65", + "type": "leaf", + "state": { + "type": "file-explorer", + "state": { + "sortOrder": "alphabetical", + "autoReveal": false + }, + "icon": "lucide-folder-closed", + "title": "文件列表" + } + }, + { + "id": "94dc470ccbf6e453", + "type": "leaf", + "state": { + "type": "search", + "state": { + "query": "", + "matchingCase": false, + "explainSearch": false, + "collapseAll": false, + "extraContext": false, + "sortOrder": "alphabetical" + }, + "icon": "lucide-search", + "title": "搜索" + } + }, + { + "id": "a49ddb63b7b71d3d", + "type": "leaf", + "state": { + "type": "bookmarks", + "state": {}, + "icon": "lucide-bookmark", + "title": "书签" + } + } + ] + } + ], + "direction": "horizontal", + "width": 300 + }, + "right": { + "id": "94ee3ffae97277f1", + "type": "split", + "children": [ + { + "id": "30c22dd0ea555284", + "type": "tabs", + "children": [ + { + "id": "e08ac0b7a754e82a", + "type": "leaf", + "state": { + "type": "backlink", + "state": { + "collapseAll": false, + "extraContext": false, + "sortOrder": "alphabetical", + "showSearch": false, + "searchQuery": "", + "backlinkCollapsed": false, + "unlinkedCollapsed": true + }, + "icon": "links-coming-in", + "title": "反向链接" + } + }, + { + "id": "c28b4721bbc7549d", + "type": "leaf", + "state": { + "type": "outgoing-link", + "state": { + "linksCollapsed": false, + "unlinkedCollapsed": true + }, + "icon": "links-going-out", + "title": "出链" + } + }, + { + "id": "951c9abc2fe22169", + "type": "leaf", + "state": { + "type": "tag", + "state": { + "sortOrder": "frequency", + "useHierarchy": true, + "showSearch": false, + "searchQuery": "" + }, + "icon": "lucide-tags", + "title": "标签" + } + }, + { + "id": "ced613ec67772cc8", + "type": "leaf", + "state": { + "type": "all-properties", + "state": { + "sortOrder": "frequency", + "showSearch": false, + "searchQuery": "" + }, + "icon": "lucide-archive", + "title": "添加笔记属性" + } + }, + { + "id": "31cad42ee1968b90", + "type": "leaf", + "state": { + "type": "outline", + "state": { + "followCursor": false, + "showSearch": false, + "searchQuery": "" + }, + "icon": "lucide-list", + "title": "大纲" + } + } + ] + } + ], + "direction": "horizontal", + "width": 300, + "collapsed": true + }, + "left-ribbon": { + "hiddenItems": { + "switcher:打开快速切换": false, + "graph:查看关系图谱": false, + "canvas:新建白板": false, + "daily-notes:打开/创建今天的日记": false, + "templates:插入模板": false, + "command-palette:打开命令面板": false, + "bases:新建数据库": false + } + }, + "active": "0f14a7f3e342ee77", + "lastOpenFiles": [ + "ros2/概念/基础概念.md", + "ros2/安装配置/环境配置.md", + "ros2/概念", + "ros2/新建文件夹", + "未命名", + "ros2/安装配置", + "ros2/安装配置/代码更新_zh.md", + "未命名.md", + "ros2/安装配置/ros2_structure.md", + "ros2/安装配置/安装_zh.md", + "ros2/安装配置/安装.md", + "欢迎.md", + "创建链接.md", + "ros2", + "desktop.ini" + ] +} \ No newline at end of file diff --git a/ros/ros2/nav2/debug.md b/ros/ros2/nav2/debug.md new file mode 100644 index 0000000..eefcda3 --- /dev/null +++ b/ros/ros2/nav2/debug.md @@ -0,0 +1,211 @@ +``` bash +root@nvidia:~/ros2_ws# ros2 topic echo /goal_pose +header: + stamp: + sec: 1776912839 + nanosec: 772353086 + frame_id: map +pose: + position: + x: -0.2929971516132355 + y: -28.93631362915039 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: -0.7140266359687852 + w: 0.7001185350546718 +--- +header: + stamp: + sec: 1776912894 + nanosec: 349057427 + frame_id: map +pose: + position: + x: 1.0191293954849243 + y: -23.940237045288086 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: -0.758253550259611 + w: 0.6519597790651626 +--- +header: + stamp: + sec: 1776912930 + nanosec: 59193832 + frame_id: map +pose: + position: + x: -0.25821736454963684 + y: -26.604511260986328 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: -0.7286561115363753 + w: 0.6848797493872113 +--- +header: + stamp: + sec: 1776913007 + nanosec: 811936088 + frame_id: map +pose: + position: + x: -0.5403046607971191 + y: -23.95861053466797 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: -0.7323575592769619 + w: 0.6809202635917743 +--- +header: + stamp: + sec: 1776913105 + nanosec: 601216801 + frame_id: map +pose: + position: + x: -0.13717754185199738 + y: -31.840221405029297 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: -0.6531641242066932 + w: 0.7572163672618966 +--- + +``` + +雷达同步问题 +``` +ros2_ws\src\rslidar_sdk\config\config.yaml +use_lidar_clock: false +``` + + +``` + --- + ⚠️ 警告1：No goal checker was specified in parameter 'current_goal_checker'. Server will use only plugin loaded + general_goal_checker + + 含义 + + 这是配置缺失类警告，不影响功能，只是提示你没有显式指定当前使用的目标检查器，系统自动用了第一个加载的general_goal_check + er。 + 根因 + + ROS2 Humble及以上版本的Nav2新增了current_goal_checker强制配置项，你的配置里只定义了goal_checker插件，但没有指定当前使 + 用哪一个。 + ✅ 解决方案 + + 在controller_server的ros__parameters下添加一行即可： + controller_server: + ros__parameters: + current_goal_checker: "general_goal_checker" # 新增这行 + controller_frequency: 20.0 + # ... 其他原有配置 + + --- + ⚠️ 警告2：Behavior Tree tick rate 100.00 was exceeded! + + 含义 + + 这是性能类警告，行为树默认tick频率是100Hz（每10ms执行一次），现在单次tick执行时间超过了10ms，导致频率不达标。 + 根因 + + 行为树中的计算密集型节点执行太慢，90%的情况是MPPI控制器计算耗时过长，小概率是系统CPU负载过高、规划器卡顿等。 + ✅ 排查&解决方案 + + 1. 先看CPU占用：top 看controller_server进程的CPU占比，如果超过100%就是计算量太大 + 2. 优先优化MPPI参数（你的当前配置计算量过高），后面警告3会详细说 + 3. 实在优化不了可以调大BT的tick间隔，在bt_navigator下加： + bt_navigator: + ros__parameters: + bt_loop_duration: 20 # 从10ms改成20ms，频率降到50Hz，不推荐，优先优化控制器 + + --- + ⚠️ 警告3：Control loop missed its desired rate of 20.0000Hz + + 含义 + + 这是核心性能警告，控制器要求20Hz的控制频率（每50ms输出一次速度指令），现在单次循环执行超过了50ms，频率不达标。 + ⚠️ 这个问题必须优先解决：控制频率低于15Hz会导致MPPI控制不稳定，甚至出现急刹、撞障碍物的情况。 + 根因 + + 你的MPPI配置计算量过大，两个核心参数超标： + FollowPath: + batch_size: 2000 # 单次迭代采样2000条轨迹，计算量极大 + time_steps: 56 # 每条轨迹预测56步（0.05*56=2.8秒预测时长） + - 2000的batch_size在Jetson NX级别的嵌入式平台单次计算就要60-80ms，肯定达不到50ms的要求 + - 额外加重负载的配置：local costmap的obstacle_max_range: 20.0，local + costmap本身只有6m×6m，处理20米内的点云完全是浪费CPU + ✅ 解决方案（按优先级） + + 1. 调小MPPI计算量（优先保证控制频率达标）： + FollowPath: + batch_size: 1000 # 降到800-1200，轨迹采样数减半，性能提升一倍，效果下降不明显 + time_steps: 40 # 降到32-40，预测时长2秒足够，履带车速度慢不需要太长预测 + 2. 缩小local costmap障碍物检测范围： + # local_costmap的voxel_layer下的scan配置 + obstacle_max_range: 10.0 # 从20米改成10米，足够覆盖6m的local costmap范围 + 3. 验证效果：改完后用ros2 topic hz /cmd_vel_nav看频率，稳定在18Hz以上就算合格 + + --- + ❌ 错误1：Failed to create a plan from potential when a legal potential was found. This shouldn't happen. + + 含义 + + 这是Navfn规划器内部错误：规划器已经算出了从起点到终点的势场（理论上存在可行路径），但实际提取路径的时候失败了。 + 根因 + + 结合你的配置，最可能的几个原因： + 4. 可行通道太窄：你的膨胀半径是1.8m，车身本身宽1.8m，通道宽度必须大于3.6m才能通过，如果路径上有窄于这个宽度的通道，膨 + 胀后就没有可行空间了 + 5. 起点/终点落在膨胀区内：虽然势场是通的，但膨胀后的代价太高，路径提取的时候找不到合法的起点/终点 + 6. 膨胀半径过大：1.8m的膨胀半径刚好是车身宽度，没有冗余，稍微有点障碍物就会把通道堵死 + 7. 全局costmap噪点过多：点云误检的障碍物挡住了路径 + ✅ 排查&解决方案 + + 8. 排查路径情况：规划失败时，在rviz上看起点到终点的通道宽度，是不是小于3.6m + 9. 优化膨胀半径（如果通道普遍较窄）： + # 全局和局部costmap的inflation_radius都可以降到1.5m（只要大于车身最大半长1.2m就满足SMAC优化要求） + inflation_radius: 1.5 + 10. 优化Navfn参数： + GridBased: + tolerance: 1.0 # 从0.5米改成1.0米，允许终点有更大的容忍范围 + 11. 终极方案：换规划器：如果经常走窄通道，把Navfn换成nav2_smac_planner/SmacPlanner，对窄通道的支持好很多 + 12. 检查全局costmap噪点：如果有很多误检的小障碍物，调大mark_threshold过滤噪点 + + --- + ❌ 错误2：Failed to get result for follow_path in node halt! + + 含义 + + 这是次生错误，不影响功能，只是日志提示：行为树在终止（halt）的时候，尝试获取follow_path节点的执行结果，但结果不存在。 + 根因 + + 完全是前面的规划失败导致的连锁反应：规划失败后整个导航任务提前终止，follow_path节点还没来得及产生结果就被强制停止了。 + ✅ 解决方案 + + 解决前面的规划失败问题后，这个错误会自动消失，不需要单独处理。 + + --- + 🎯 解决优先级建议 + + 13. 【最高优先级】先解决控制循环频率不够的问题，直接影响导航安全性 + 14. 【次高优先级】解决规划失败的问题，影响导航可用性 + 15. 【低优先级】解决剩下的配置类警告，不影响功能只是优化日志 + + 额外优化建议 + + 你现在的voxel层mark_threshold: 10有点太高了，需要10个点才能标记障碍物，容易漏检小障碍物，建议调到3-5更合适： + voxel_layer: + mark_threshold: 3 # 从10改成3，平衡噪点过滤和小障碍物检测 +``` \ No newline at end of file diff --git a/ros/ros2/nav2/nav2.md b/ros/ros2/nav2/nav2.md new file mode 100644 index 0000000..e69de29 diff --git a/ros/ros2/nav2/nav2_bringup_gps_demo.md b/ros/ros2/nav2/nav2_bringup_gps_demo.md new file mode 100644 index 0000000..b4fdae2 --- /dev/null +++ b/ros/ros2/nav2/nav2_bringup_gps_demo.md @@ -0,0 +1,356 @@ +# Navigation2 详解：nav2_bringup 与 nav2_gps_waypoint_follower_demo + +## 1. Navigation2 整体介绍 +Navigation2（简称Nav2）是ROS2生态中最成熟的开源导航框架，是ROS1 Navigation Stack的官方继任者，专为移动机器人自主导航设计。 + +### 1.1 核心功能 +- **定位**：支持AMCL、SLAM（Cartographer、SLAM Toolbox等）、GPS/RTK等多源定位 +- **全局路径规划**：支持A*、Dijkstra、NavFn等多种规划算法 +- **局部路径跟踪**：支持DWA、TEB、MPPI、Pure Pursuit等跟踪算法 +- **避障与行为树**：通过行为树实现复杂导航逻辑，支持动态障碍物规避 +- **重规划与恢复行为**：导航失败时自动执行恢复策略（旋转、回退、清图等） +- **多机器人支持**：支持命名空间隔离的多机器人导航 + +### 1.2 整体架构 +Nav2采用模块化插件式设计，核心模块包括： +``` +├── 感知层 +│ ├── 传感器数据处理（LiDAR、Camera、IMU、GPS等） +│ └── 障碍物检测/语义分割 +├── 定位层 +│ ├── AMCL（蒙特卡洛定位） +│ ├── SLAM 建图 +│ └── 多传感器融合定位 +├── 地图层 +│ ├── 代价地图（全局/局部） +│ ├── 图层插件（静态层、障碍层、膨胀层、社会层等） +├── 规划层 +│ ├── 全局路径规划器 +│ └── 平滑器 +├── 控制层 +│ ├── 局部路径规划器 +│ └── 轨迹跟踪控制器 +├── 行为决策层 +│ ├── 行为树执行器 +│ └── 恢复行为管理器 +└── 接口层 + ├── RViz 可视化 + ├── Action/Topic API + └── 配置参数系统 +``` + +--- + +## 2. nav2_bringup 包详解 +nav2_bringup是Nav2的官方启动集成包，提供了开箱即用的启动文件、配置模板和参数集合，是快速部署Nav2系统的入口。 + +### 2.1 代码结构 +``` +nav2_bringup/ +├── bringup/ # 启动相关核心目录 +│ ├── launch/ # 启动文件集合 +│ │ ├── bringup_launch.py # 主启动文件，集成所有导航模块 +│ │ ├── localization_launch.py # 单独启动定位模块（AMCL/SLAM） +│ │ ├── navigation_launch.py # 单独启动导航模块（规划/控制/行为树） +│ │ ├── rviz_launch.py # 启动RViz可视化 +│ │ ├── slam_launch.py # 启动SLAM建图 +│ │ └── urdf_launch.py # 加载机器人URDF模型 +│ ├── params/ # 参数配置文件目录 +│ │ ├── nav2_params.yaml # 导航全模块默认参数 +│ │ ├── nav2_params_no_odom.yaml # 无里程计场景参数 +│ │ ├── nav2_slam_params.yaml # SLAM模式参数 +│ │ └── robot_localization/ # 传感器融合参数模板 +│ └── rviz/ # RViz配置文件 +│ └── nav2_default_view.rviz # Nav2默认RViz配置 +├── docker/ # Docker部署相关文件 +├── scripts/ # 辅助脚本 +├── CMakeLists.txt +├── package.xml +└── README.md +``` + +### 2.2 核心启动文件说明 + +#### 2.2.1 bringup_launch.py +这是最常用的一站式启动文件，集成了完整的导航流程，支持参数配置： +```python +# 关键参数 +'namespace': '', # 多机器人命名空间 +'use_sim_time': False, # 是否使用仿真时间 +'rviz_config_file': '', # RViz配置文件路径 +'params_file': '', # 导航参数文件路径 +'autostart': True, # 启动后自动激活导航栈 +'use_composition': True, # 使用组件化节点（减少进程数，提高性能） +'use_respawn': False, # 节点崩溃时自动重启 +'map': '', # 静态地图文件路径（.yaml/.pgm） +'slam': False, # 是否启用SLAM模式 +'localization_type': 'amcl' # 定位方式：amcl/slam_toolbox等 +``` +启动示例： +```bash +# 仿真环境启动导航 +ros2 launch nav2_bringup bringup_launch.py use_sim_time:=True map:=/path/to/map.yaml + +# 真机环境启用SLAM建图 +ros2 launch nav2_bringup bringup_launch.py slam:=True params_file:=/path/to/my_params.yaml +``` + +#### 2.2.2 localization_launch.py +单独启动定位模块，适合只需要定位不需要完整导航的场景： +```bash +# 仅启动AMCL定位 +ros2 launch nav2_bringup localization_launch.py map:=/path/to/map.yaml use_sim_time:=True +``` + +#### 2.2.3 navigation_launch.py +单独启动导航模块（规划/控制/行为树），适合定位已由其他系统提供的场景： +```bash +# 仅启动导航模块，使用已有的定位信息 +ros2 launch nav2_bringup navigation_launch.py params_file:=/path/to/my_params.yaml +``` + +### 2.3 配置文件详解 +nav2_params.yaml是核心参数配置文件，采用分层结构，每个模块单独配置： +```yaml +amcl: + ros__parameters: + base_frame_id: "base_footprint" # 机器人基坐标系 + global_frame_id: "map" # 全局坐标系 + odom_frame_id: "odom" # 里程计坐标系 + max_particles: 2000 # 最大粒子数 + min_particles: 500 # 最小粒子数 + update_min_a: 0.2 # 角度变化超过0.2rad时更新 + update_min_d: 0.25 # 位移超过0.25m时更新 + +bt_navigator: + ros__parameters: + use_sim_time: False + bt_xml_filename: "navigate_w_replanning_and_recovery.xml" # 行为树文件 + +global_costmap: + global_costmap: + ros__parameters: + update_frequency: 1.0 + publish_frequency: 1.0 + global_frame: map + robot_base_frame: base_footprint + resolution: 0.05 + plugins: + - {name: static_layer, type: "costmap_2d::StaticLayer"} + - {name: obstacle_layer, type: "costmap_2d::ObstacleLayer"} + - {name: inflation_layer, type: "costmap_2d::InflationLayer"} + +local_costmap: + local_costmap: + ros__parameters: + update_frequency: 5.0 + publish_frequency: 2.0 + global_frame: odom + robot_base_frame: base_footprint + width: 3 + height: 3 + resolution: 0.05 + rolling_window: true # 局部代价地图随机器人移动 + +planner_server: + ros__parameters: + expected_planner_frequency: 1.0 + planner_plugins: ["GridBased"] + GridBased: + plugin: "nav2_navfn_planner/NavfnPlanner" # 默认使用NavFn规划器 + use_astar: true # 启用A*算法 + +controller_server: + ros__parameters: + controller_frequency: 20.0 + min_x_velocity_threshold: 0.001 + min_y_velocity_threshold: 0.5 + controller_plugins: ["FollowPath"] + FollowPath: + plugin: "nav2_regulated_pure_pursuit_controller/RegulatedPurePursuitController" # 默认使用Pure Pursuit控制器 +``` + +### 2.4 最佳实践 +1. **不要直接修改默认参数文件**：将nav2_params.yaml复制到自己的工作空间进行修改，通过`params_file`参数传入 +2. **组件化启动**：默认启用`use_composition:=True`可以显著减少内存占用和通信开销 +3. **分层配置**：将通用参数和机器人特定参数分开存放，便于维护 +4. **仿真与真机分离**：使用`use_sim_time`参数区分仿真和真机环境，分别配置不同的参数值 + +--- + +## 3. nav2_gps_waypoint_follower_demo 详解 +这是Nav2官方提供的室外导航示例，演示了如何结合GPS/RTK定位实现全局航点跟随功能，适用于无人车、巡检机器人等室外场景。 + +### 3.1 代码结构 +该包位于navigation2_tutorials仓库中，结构如下： +``` +nav2_gps_waypoint_follower_demo/ +├── bringup/ +│ ├── launch/ +│ │ ├── gps_waypoint_follower_launch.py # Demo主启动文件 +│ │ ├── rviz.launch.py # RViz启动 +│ │ └── dual_ekf.launch.py # 双EKF传感器融合启动 +│ ├── params/ +│ │ ├── dual_ekf.yaml # 传感器融合参数 +│ │ ├── nav2_params.yaml # 导航参数（适配GPS场景） +│ │ └── waypoint_follower_params.yaml # 航点跟随器参数 +│ ├── rviz/ +│ │ └── gps_waypoint.rviz # GPS导航专用RViz配置 +│ └── worlds/ +│ └── gps_world.world # 仿真世界文件（含GPS插件） +├── config/ +│ └── sample_waypoints.yaml # 示例航点配置文件 +├── src/ +│ └── gps_waypoint_logger.cpp # 航点到达日志节点（可选） +├── CMakeLists.txt +├── package.xml +└── README.md +``` + +### 3.2 核心组件说明 +GPS航点跟随系统主要由三个核心部分组成： +1. **传感器融合模块**（robot_localization）：融合GPS、IMU、轮式里程计数据，输出全局定位 +2. **导航栈模块**：基于全局定位进行路径规划和跟踪 +3. **航点跟随器**：按照预设的GPS航点序列依次导航 + +#### 3.2.1 双EKF传感器融合 +demo使用两个扩展卡尔曼滤波器（EKF）实现多传感器融合： +```yaml +# dual_ekf.yaml 核心配置 +ekf_filter_node_odom: + ros__parameters: + frequency: 30.0 + sensor_timeout: 0.1 + two_d_mode: true + publish_tf: true + odom_frame: odom + base_link_frame: base_link + world_frame: odom + # 融合IMU和轮式里程计，输出odom->base_link变换 + imu0: /imu/data + odom0: /wheel/odometry + +ekf_filter_node_map: + ros__parameters: + frequency: 30.0 + sensor_timeout: 0.1 + two_d_mode: true + publish_tf: true + odom_frame: odom + base_link_frame: base_link + world_frame: map + # 融合GPS数据，输出map->odom变换 + odom1: /gps/fix + odom1_differential: false + odom1_relative: false +``` +- 第一个EKF（odom系）：融合IMU和轮式里程计，提供短时间内高精度的局部定位，无漂移 +- 第二个EKF（map系）：融合GPS数据，提供全局绝对定位，修正长期漂移 + +#### 3.2.2 导航参数适配（nav2_params.yaml） +GPS导航场景需要对默认导航参数进行调整： +```yaml +amcl: + ros__parameters: + # GPS导航场景下禁用AMCL，使用EKF融合提供定位 + use_sim_time: False + # AMCL相关参数全部注释或删除 + +global_costmap: + global_costmap: + ros__parameters: + # GPS模式下不使用静态地图，使用动态生成的代价地图 + plugins: + - {name: obstacle_layer, type: "costmap_2d::ObstacleLayer"} + - {name: inflation_layer, type: "costmap_2d::InflationLayer"} + # 代价地图大小足够覆盖所有航点范围 + width: 1000 + height: 1000 + resolution: 0.1 + rolling_window: false # 全局代价地图固定，不随机器人移动 + origin_x: -500 + origin_y: -500 + +planner_server: + ros__parameters: + # 使用适合长距离规划的算法 + GridBased: + plugin: "nav2_navfn_planner/NavfnPlanner" + use_astar: true + allow_unknown: true # 允许在未知区域规划路径 + +controller_server: + ros__parameters: + # 使用适合高速行驶的控制器 + FollowPath: + plugin: "nav2_mppi_controller/MPPIController" # 或者TEB控制器 + max_vel_x: 5.0 # 室外场景允许更高速度 +``` + +#### 3.2.3 航点跟随器配置 +```yaml +# waypoint_follower_params.yaml +waypoint_follower: + ros__parameters: + loop: False # 航点是否循环执行 + waypoint_timeout: 300.0 # 单个航点导航超时时间（秒） + stop_on_failure: True # 某个航点导航失败时停止 + action_server_name: "follow_waypoints" + # 航点到达阈值 + goal_tolerance_x: 0.5 + goal_tolerance_y: 0.5 + goal_tolerance_yaw: 0.25 +``` + +### 3.3 航点配置 +sample_waypoints.yaml是预设的GPS航点文件，格式如下： +```yaml +waypoints: + - latitude: 37.7749 + longitude: -122.4194 + yaw: 0.0 # 到达后的朝向（弧度） + frame_id: "wgs84" # GPS坐标系 + - latitude: 37.7750 + longitude: -122.4195 + yaw: 1.57 + frame_id: "wgs84" + - latitude: 37.7751 + longitude: -122.4196 + yaw: 3.14 + frame_id: "wgs84" +``` +航点使用WGS84坐标系（标准GPS坐标），也可以配置为UTM坐标系。 + +### 3.4 运行流程 +1. **启动仿真/真机驱动**：加载机器人模型、传感器驱动、GPS插件 +2. **启动传感器融合**：`ros2 launch nav2_gps_waypoint_follower_demo dual_ekf.launch.py` +3. **启动导航栈**：`ros2 launch nav2_gps_waypoint_follower_demo gps_waypoint_follower_launch.py` +4. **加载航点**：`ros2 run nav2_waypoint_follower waypoint_follower --ros-args -p waypoint_file:=/path/to/sample_waypoints.yaml` +5. **启动航点跟随**：通过RViz发送FollowWaypoints action请求，或者用命令行启动： + ```bash + ros2 action send_goal /follow_waypoints nav2_msgs/action/FollowWaypoints "{waypoints: []}" + ``` + +### 3.5 关键注意事项 +1. **GPS信号质量**：室外场景下需要确保GPS信号良好，RTK定位能达到厘米级精度，普通GPS只有米级精度，可能影响导航效果 +2. **坐标系转换**：需要正确配置WGS84到UTM再到机器人本地坐标系的转换参数 +3. **航点规划**：航点之间需要有可行路径，避免有障碍物阻挡 +4. **动态障碍物**：室外场景需要配置合适的障碍物检测和避障参数 +5. **TF树检查**：确保`map->odom->base_link`的TF变换正确发布，这是GPS导航的基础 + +--- + +## 4. 常用调试技巧 +1. **查看TF树**：`ros2 run tf2_tools view_frames`，检查坐标系变换是否正确 +2. **检查定位质量**：`ros2 topic echo /gps/fix` 查看GPS信号质量，`ros2 topic echo /odometry/filtered` 查看融合后的定位输出 +3. **可视化调试**：在RViz中显示全局/局部代价地图、规划路径、当前目标点等信息 +4. **参数动态调整**：使用`ros2 param`命令动态修改参数，无需重启导航栈 +5. **日志分析**：查看`/tf`, `/cmd_vel`, `/plan`等话题的输出，排查导航失败原因 + +--- + +## 参考资料 +- [Navigation2 官方文档](https://navigation.ros.org/) +- [nav2_bringup 源码](https://github.com/ros-navigation/navigation2/tree/main/nav2_bringup) +- [GPS Waypoint Follower 教程](https://navigation.ros.org/tutorials/docs/gps_waypoint_follower.html) +- [navigation2_tutorials 仓库](https://github.com/ros-navigation/navigation2_tutorials) diff --git a/ros/ros2/ros2_control/环境.md b/ros/ros2/ros2_control/环境.md new file mode 100644 index 0000000..3c18551 --- /dev/null +++ b/ros/ros2/ros2_control/环境.md @@ -0,0 +1,59 @@ +#### 拉取镜像 +```bash +docker pull arm64v8/ros:humble +``` + +#### 启动脚本 +```code +#!/bin/bash + +# 允许 Docker 访问 X 服务器（用于 GUI） +xhost +local:docker + +# 容器名称 +CONTAINER_NAME="ylhan_ros2_humble" + +# 检查容器是否存在 +if docker ps -a --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then + # 容器存在 + if docker ps --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then + # 容器正在运行，直接进入 + echo "容器 ${CONTAINER_NAME} 正在运行，进入容器..." + docker exec -it ${CONTAINER_NAME} bash + else + # 容器已存在但未运行，启动并进入 + echo "容器 ${CONTAINER_NAME} 已存在但未运行，启动容器..." + docker start -i ${CONTAINER_NAME} + fi +else + # 容器不存在，创建并运行新容器 + echo "容器 ${CONTAINER_NAME} 不存在，创建新容器..." + docker run -it --rm \ + --name ${CONTAINER_NAME} \ + --network host \ + --privileged \ + -e DISPLAY=$DISPLAY \ + -v /tmp/.X11-unix:/tmp/.X11-unix:rw \ + -v /home/ylhan/ros2_ws:/root/ros2_ws \ + -v /dev:/dev \ + arm64v8/ros:humble \ + bash +fi + +``` + +#### 环境设置 +宿主机 +```bash +echo $DISPLAY +``` +假设输出为nvidia:13.0,在docker内： +```bash +export DISPLAY=nvidia:13.0 +``` +复制宿主机home目录下.Xauthority文件到docker中home目录下，使用xeyes测试 + +ros2 docker隔离 +```bash +export ROS_DOMAIN_ID=79 #设置独立的DOMAIN_ID +``` \ No newline at end of file diff --git a/ros/ros2/安装配置/ros2_structure.md b/ros/ros2/安装配置/ros2_structure.md new file mode 100644 index 0000000..e7d6912 --- /dev/null +++ b/ros/ros2/安装配置/ros2_structure.md @@ -0,0 +1,128 @@ +# ROS2 代码结构图 + +## ROS2 工作区目录结构 + +```mermaid +flowchart TD + Workspace[ROS2 工作区] --> src[src/ 源代码目录] + Workspace --> build[build/ 构建目录] + Workspace --> install[install/ 安装目录] + Workspace --> log[log/ 日志目录] + + src --> Package1[包1] + src --> Package2[包2] + src --> PackageN[...] + + Package1 --> P1_CMake[CMakeLists.txt] + Package1 --> P1_PackageXML[package.xml] + Package1 --> P1_src[src/ 源代码] + Package1 --> P1_include[include/ 头文件] + Package1 --> P1_msgs[msg/ 消息定义] + Package1 --> P1_srvs[srv/ 服务定义] + Package1 --> P1_actions[action/ 动作定义] +``` + +## ROS2 软件架构 + +```mermaid +flowchart TD + App[应用程序] --> ClientLibs[客户端库] + + subgraph ClientLibs [客户端库] + direction LR + RCLcpp[C++ 客户端库 rclcpp] + RCLpy[Python 客户端库 rclpy] + RCL[C 客户端库 rcl] + end + + ClientLibs --> RCL_Interface[RCL 接口] + RCL_Interface --> RMW[ROS 中间件接口 RMW] + + subgraph DDS_Implementations [DDS 实现] + direction LR + FastDDS[Fast DDS] + CycloneDDS[Cyclone DDS] + RTI_Connext[RTI Connext] + OpenSplice[OpenSplice] + end + + RMW --> DDS_Implementations + DDS_Implementations --> Network[网络通信] +``` + +## ROS2 核心包依赖关系 + +```mermaid +flowchart TD + ROS2_Core[ROS2 核心] --> ament_core[ament_core
构建系统] + ROS2_Core --> rcl[rcl
C语言客户端库] + ROS2_Core --> rclcpp[rclcpp
C++客户端库] + ROS2_Core --> rclpy[rclpy
Python客户端库] + + rcl --> rmw[rmw
中间件接口] + rclcpp --> rcl + rclpy --> rcl + + rmw --> rmw_implementation[rmw_implementation
中间件实现] + rmw_implementation --> DDS[DDS 实现] + + ROS2_Tools[ROS2 工具] --> ros2cli[ros2cli
命令行工具] + ROS2_Tools --> colcon[colcon
构建工具] + ROS2_Tools --> launch_ros[launch_ros
启动系统] +``` + +## ROS2 节点通信模型 + +```mermaid +flowchart LR + NodeA[ROS2 节点 A] -->|发布话题| Topic1[话题 /topic1] + NodeB[ROS2 节点 B] -->|订阅话题| Topic1 + + NodeC[ROS2 节点 C] -->|请求服务| Service1[服务 /service1] + NodeD[ROS2 节点 D] -->|提供服务| Service1 + + NodeE[ROS2 节点 E] -->|发送目标| Action1[动作 /action1] + NodeF[ROS2 节点 F] -->|执行动作| Action1 + NodeF -->|反馈| NodeE + NodeF -->|结果| NodeE +``` + +## 说明 + +### 1. 工作区目录结构 +- **src/**: 包含所有ROS2包的源代码 +- **build/**: 构建过程中生成的中间文件 +- **install/**: 安装目录，包含可执行文件、库和资源 +- **log/**: 构建和运行时的日志文件 + +### 2. ROS2包的基本结构 +每个ROS2包通常包含： +- `CMakeLists.txt`: CMake构建配置文件 +- `package.xml`: 包元数据文件 +- `src/`: C++或Python源代码 +- `include/`: C++头文件 +- `msg/`, `srv/`, `action/`: 消息、服务、动作定义文件 + +### 3. 软件架构层次 +1. **应用程序层**: 用户编写的ROS2节点 +2. **客户端库层**: 语言特定的API（C++、Python、C） +3. **RCL层**: 通用的ROS客户端库 +4. **RMW层**: ROS中间件抽象接口 +5. **DDS层**: 具体的DDS实现 + +### 4. 通信模式 +- **话题(Topics)**: 发布/订阅模式，单向异步通信 +- **服务(Services)**: 请求/响应模式，同步通信 +- **动作(Actions)**: 目标/反馈/结果模式，长时间运行的任务 + +## 使用方法 + +1. 将本文件在支持 Mermaid 图的 Markdown 查看器中打开（如 VS Code with Mermaid 插件、GitHub、GitLab 等） +2. 图表将自动渲染为可视化图形 +3. 如需修改图表，编辑对应的 Mermaid 代码块 + +## 相关资源 + +- [ROS2 官方文档](https://docs.ros.org/) +- [ROS2 架构设计](https://design.ros2.org/) +- [Mermaid 语法指南](https://mermaid.js.org/syntax/flowchart.html) \ No newline at end of file diff --git a/ros/ros2/安装配置/代码更新_zh.md b/ros/ros2/安装配置/代码更新_zh.md new file mode 100644 index 0000000..c1629dd --- /dev/null +++ b/ros/ros2/安装配置/代码更新_zh.md @@ -0,0 +1,77 @@ +# 维护 ROS 2 源代码检出 + +## [概述](https://docs.ros.org/en/humble/Installation/Maintaining-a-Source-Checkout.html) + +本文档介绍如何维护从源代码构建的 ROS 2 安装。如果您已经从源代码安装了 ROS 2，并且想要更新到最新的错误修复和功能，请遵循本指南。 + +## [更新 ROS 2 源代码](https://docs.ros.org/en/humble/Installation/Maintaining-a-Source-Checkout.html#updating-ros-2-source-code) + +### 1. 更新仓库列表文件 + +首先，您需要获取最新的 `ros2.repos` 文件，该文件定义了构建 ROS 2 所需的所有仓库及其分支。 + +```bash +# 对于 Humble Hawksbill 版本 +cd ~/ros2_humble +wget https://raw.githubusercontent.com/ros2/ros2/humble/ros2.repos -O ros2.repos +``` + +或者，如果您想使用特定分支（例如主分支）而不是发布分支： + +```bash +# 使用主分支（开发版本） +wget https://raw.githubusercontent.com/ros2/ros2/master/ros2.repos -O ros2.repos +``` + +### 2. 更新本地仓库 + +使用 `vcs` 工具更新您的本地仓库： + +```bash +# 更新所有仓库的远程引用 +vcs custom --args remote update +``` + +### 3. 下载新的源代码 + +现在，使用新的 `ros2.repos` 文件导入并拉取最新的代码： + +```bash +# 导入新仓库定义 +vcs import src < ros2.repos + +# 拉取所有仓库的最新代码 +vcs pull src +``` + +**注意**：`vcs import` 命令将覆盖 `src` 目录中现有的仓库定义。如果您有本地修改或添加了额外的仓库，请确保在运行此命令之前备份您的更改。 + +### 4. 重新构建工作空间 + +更新源代码后，需要重新构建工作空间： + +```bash +# 返回到工作空间根目录 +cd ~/ros2_humble + +# 使用 colcon 重新构建 +colcon build --symlink-install +``` + +构建选项说明： +- `--symlink-install`：使用符号链接而不是复制文件，这可以加快构建速度并减少磁盘空间使用 + +### 5. 检查源代码状态 + +要查看当前工作空间中所有仓库的状态，可以导出当前的仓库配置： + +```bash +# 导出当前仓库状态 +vcs export src > my_ros2.repos +``` + +这将在当前目录下创建一个 `my_ros2.repos` 文件，其中包含所有仓库的当前提交哈希值。您可以将此文件用于： +- 备份当前的源代码状态 +- 在其他机器上复制相同的配置 +- 调试构建问题 + diff --git a/ros/ros2/安装配置/安装.md b/ros/ros2/安装配置/安装.md new file mode 100644 index 0000000..133d0aa --- /dev/null +++ b/ros/ros2/安装配置/安装.md @@ -0,0 +1,137 @@ +## [System requirements](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id1)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#system-requirements "Link to this heading") + +The current Debian-based target platforms for Humble Hawksbill are: + +- Tier 1: Ubuntu Linux - Jammy (22.04) 64-bit + +- Tier 3: Ubuntu Linux - Focal (20.04) 64-bit + +- Tier 3: Debian Linux - Bullseye (11) 64-bit + + +Other Linux platforms with varying support levels include: + +- Arch Linux, see [alternate instructions](https://wiki.archlinux.org/index.php/ROS#ROS_2) + +- Fedora Linux, see [alternate instructions](https://ros2docs.robook.org/humble/Installation/Alternatives/Fedora-Development-Setup.html) + +- OpenEmbedded / webOS OSE, see [alternate instructions](https://github.com/ros/meta-ros/wiki/OpenEmbedded-Build-Instructions) + + +As defined in [REP 2000](https://www.ros.org/reps/rep-2000.html). + +## [System setup](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id2)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#system-setup "Link to this heading") + +### [Set locale](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id3)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#set-locale "Link to this heading") + +请确保您有一个支持 `UTF-8` 的区域设置。 如果您处于最简环境中（例如 docker 容器），区域设置可能是一些类似 `POSIX` 的选项。 我们使用以下设置通过了测试。但是，如果您使用不同的支持 `UTF-8` 的区域设置，也应该没问题。 + +`locale # check for UTF-8` +`sudo apt update && sudo apt install locales` +`sudo locale-gen en_US en_US.UTF-8` +`sudo update-locale LC_ALL=en_US.UTF-8 LANG=en_US.UTF-8` +`export LANG=en_US.UTF-8` +`locale # verify settings` + +### [Add the ROS 2 apt repository](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id4)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#add-the-ros-2-apt-repository "Link to this heading") + +你需要将 ROS 2 apt 仓库添加到你的系统中。 + +首先确保 [Ubuntu Universe 仓库](https://help.ubuntu.com/community/Repositories/Ubuntu) 已启用。 + +`sudo apt install software-properties-common` +`sudo add-apt-repository universe` + +然后使用 apt 添加 ROS 2 GPG 密钥。 + +`sudo apt update && sudo apt install curl -y` +`sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg` + +然后将仓库添加到你的源列表中。 + +`echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null` + +### [Install development tools and ROS tools](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id5)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-development-tools-and-ros-tools "Link to this heading") + +Install common packages. + +`sudo apt update && sudo apt install -y \` + `python3-flake8-docstrings \` + `python3-pip \` + `python3-pytest-cov \` + `ros-dev-tools` + +Install packages according to your Ubuntu version. + +Ubuntu 22.04 LTS and laterUbuntu 20.04 LTS + +`sudo apt install -y \` + `python3-flake8-blind-except \` + `python3-flake8-builtins \` + `python3-flake8-class-newline \` + `python3-flake8-comprehensions \` + `python3-flake8-deprecated \` + `python3-flake8-import-order \` + `python3-flake8-quotes \` + `python3-pytest-repeat \` + `python3-pytest-rerunfailures` + +## [Get ROS 2 code](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id6)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#get-ros-2-code "Link to this heading") + +Create a workspace and clone all repos: + +`mkdir -p ~/ros2_humble/src` +`cd ~/ros2_humble` +`vcs import --input https://raw.githubusercontent.com/ros2/ros2/humble/ros2.repos src` + +## [Install dependencies using rosdep](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id7)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-dependencies-using-rosdep "Link to this heading") + +ROS 2 包是在经常更新的 Ubuntu 系统上构建的。 在安装新包之前，建议您确保系统是最新的。 + +%% `sudo apt upgrade` %% + +`sudo rosdep init` +`rosdep update` +`rosdep install --from-paths src --ignore-src -y --skip-keys "fastcdr rti-connext-dds-6.0.1 urdfdom_headers"` + +**Note**: If you’re using a distribution that is based on Ubuntu (like Linux Mint) but does not identify itself as such, you’ll get an error message like `Unsupported OS [mint]`. In this case append `--os=ubuntu:jammy` to the above command. + +## [Install additional DDS implementations (optional)](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id8)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-additional-dds-implementations-optional "Link to this heading") + +If you would like to use another DDS or RTPS vendor besides the default, you can find instructions [here](https://ros2docs.robook.org/humble/Installation/DDS-Implementations.html). + +## [Build the code in the workspace](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id9)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#build-the-code-in-the-workspace "Link to this heading") + +If you have already installed ROS 2 another way (either via debs or the binary distribution), make sure that you run the below commands in a fresh environment that does not have those other installations sourced. Also ensure that you do not have `source /opt/ros/${ROS_DISTRO}/setup.bash` in your `.bashrc`. You can make sure that ROS 2 is not sourced with the command `printenv | grep -i ROS`. The output should be empty. + +More info on working with a ROS workspace can be found in [this tutorial](https://ros2docs.robook.org/humble/Tutorials/Beginner-Client-Libraries/Colcon-Tutorial.html). + +`cd ~/ros2_humble/` +`colcon build --symlink-install` + +Note: if you are having trouble compiling all examples and this is preventing you from completing a successful build, you can use `COLCON_IGNORE` in the same manner as [CATKIN_IGNORE](https://github.com/ros-infrastructure/rep/blob/master/rep-0128.rst) to ignore the subtree or remove the folder from the workspace. Take for instance: you would like to avoid installing the large OpenCV library. Well then simply run `touch COLCON_IGNORE` in the `cam2image` demo directory to leave it out of the build process. + +## [Environment setup](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id10)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#environment-setup "Link to this heading") + +### [Source the setup script](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id11)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#source-the-setup-script "Link to this heading") + +Set up your environment by sourcing the following file. + +# Replace ".bash" with your shell if you're not using bash +# Possible values are: setup.bash, setup.sh, setup.zsh + +`. ~/ros2_humble/install/local_setup.bash` + +## [Try some examples](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id12)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#try-some-examples "Link to this heading") + +In one terminal, source the setup file and then run a C++ `talker`: + +`. ~/ros2_humble/install/local_setup.bash` +`ros2 run demo_nodes_cpp talker` + +In another terminal source the setup file and then run a Python `listener`: + +`. ~/ros2_humble/install/local_setup.bash` +`ros2 run demo_nodes_py listener` + +You should see the `talker` saying that it’s `Publishing` messages and the `listener` saying `I heard` those messages. This verifies both the C++ and Python APIs are working properly. Hooray! \ No newline at end of file diff --git a/ros/ros2/安装配置/安装_zh.md b/ros/ros2/安装配置/安装_zh.md new file mode 100644 index 0000000..1693259 --- /dev/null +++ b/ros/ros2/安装配置/安装_zh.md @@ -0,0 +1,161 @@ +## [系统要求](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id1)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#system-requirements "Link to this heading") + +当前 Humble Hawksbill 基于 Debian 的目标平台包括： + +- 第一级：Ubuntu Linux - Jammy (22.04) 64位 + +- 第三级：Ubuntu Linux - Focal (20.04) 64位 + +- 第三级：Debian Linux - Bullseye (11) 64位 + + +其他支持程度不同的 Linux 平台包括： + +- Arch Linux，请参阅[替代安装说明](https://wiki.archlinux.org/index.php/ROS#ROS_2) + +- Fedora Linux，请参阅[替代安装说明](https://ros2docs.robook.org/humble/Installation/Alternatives/Fedora-Development-Setup.html) + +- OpenEmbedded / webOS OSE，请参阅[替代安装说明](https://github.com/ros/meta-ros/wiki/OpenEmbedded-Build-Instructions) + + +如 [REP 2000](https://www.ros.org/reps/rep-2000.html) 中所定义。 + +## [系统设置](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id2)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#system-setup "Link to this heading") + +### [设置区域](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id3)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#set-locale "Link to this heading") + +请确保您有一个支持 `UTF-8` 的区域设置。如果您处于最简环境中（例如 docker 容器），区域设置可能是一些类似 `POSIX` 的选项。我们使用以下设置通过了测试。但是，如果您使用不同的支持 `UTF-8` 的区域设置，也应该没问题。 + +```bash +locale # 检查 UTF-8 支持 +sudo apt update && sudo apt install locales +sudo locale-gen en_US en_US.UTF-8 +sudo update-locale LC_ALL=en_US.UTF-8 LANG=en_US.UTF-8 +export LANG=en_US.UTF-8 +locale # 验证设置 +``` + +### [添加 ROS 2 apt 仓库](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id4)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#add-the-ros-2-apt-repository "Link to this heading") + +你需要将 ROS 2 apt 仓库添加到你的系统中。 + +首先确保 [Ubuntu Universe 仓库](https://help.ubuntu.com/community/Repositories/Ubuntu) 已启用。 + +```bash +sudo apt install software-properties-common +sudo add-apt-repository universe +``` + +然后使用 apt 添加 ROS 2 GPG 密钥。 + +```bash +sudo apt update && sudo apt install curl -y +sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg +``` + +然后将仓库添加到你的源列表中。 + +```bash +echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null +``` + +### [安装开发工具和 ROS 工具](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id5)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-development-tools-and-ros-tools "Link to this heading") + +安装常用软件包。 + +```bash +sudo apt update && sudo apt install -y \ + python3-flake8-docstrings \ + python3-pip \ + python3-pytest-cov \ + ros-dev-tools +``` + +根据您的 Ubuntu 版本安装软件包。 + +Ubuntu 22.04 LTS 及更高版本 Ubuntu 20.04 LTS + +```bash +sudo apt install -y \ + python3-flake8-blind-except \ + python3-flake8-builtins \ + python3-flake8-class-newline \ + python3-flake8-comprehensions \ + python3-flake8-deprecated \ + python3-flake8-import-order \ + python3-flake8-quotes \ + python3-pytest-repeat \ + python3-pytest-rerunfailures +``` + +## [获取 ROS 2 代码](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id6)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#get-ros-2-code "Link to this heading") + +创建工作区并克隆所有仓库： + +```bash +mkdir -p ~/ros2_humble/src +cd ~/ros2_humble +vcs import --input https://raw.githubusercontent.com/ros2/ros2/humble/ros2.repos src +``` + +## [使用 rosdep 安装依赖](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id7)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-dependencies-using-rosdep "Link to this heading") + +ROS 2 包是在经常更新的 Ubuntu 系统上构建的。在安装新包之前，建议您确保系统是最新的。 + +%% `sudo apt upgrade` %% + +```bash +sudo rosdep init +rosdep update +rosdep install --from-paths src --ignore-src -y --skip-keys "fastcdr rti-connext-dds-6.0.1 urdfdom_headers" +``` + +**注意**：如果您使用的是基于 Ubuntu 的发行版（如 Linux Mint）但系统未标识为 Ubuntu，您可能会收到类似 `Unsupported OS [mint]` 的错误消息。在这种情况下，请在上述命令后追加 `--os=ubuntu:jammy`。 + +## [安装额外的 DDS 实现（可选）](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id8)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#install-additional-dds-implementations-optional "Link to this heading") + +如果您想使用默认之外的 DDS 或 RTPS 供应商，可以在此处找到说明[这里](https://ros2docs.robook.org/humble/Installation/DDS-Implementations.html)。 + +## [在工作区中构建代码](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id9)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#build-the-code-in-the-workspace "Link to this heading") + +如果您已经通过其他方式（deb 包或二进制发行版）安装了 ROS 2，请确保在未激活其他安装的新环境中运行以下命令。同时确保您的 `.bashrc` 中没有 `source /opt/ros/${ROS_DISTRO}/setup.bash`。您可以使用命令 `printenv | grep -i ROS` 来确保 ROS 2 未被激活。输出应为空。 + +有关使用 ROS 工作区的更多信息，请参阅[本教程](https://ros2docs.robook.org/humble/Tutorials/Beginner-Client-Libraries/Colcon-Tutorial.html)。 + +```bash +cd ~/ros2_humble/ +colcon build --symlink-install +``` + +**注意**：如果编译所有示例时遇到问题，导致无法成功构建，您可以像使用 [CATKIN_IGNORE](https://github.com/ros-infrastructure/rep/blob/master/rep-0128.rst) 一样使用 `COLCON_IGNORE` 来忽略子树或从工作区中移除文件夹。例如：如果您想避免安装庞大的 OpenCV 库，只需在 `cam2image` 演示目录中运行 `touch COLCON_IGNORE` 即可将其排除在构建过程之外。 + +## [环境设置](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id10)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#environment-setup "Link to this heading") + +### [源化安装脚本](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id11)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#source-the-setup-script "Link to this heading") + +通过源化以下文件来设置您的环境。 + +# 如果不使用 bash，请将 ".bash" 替换为您的 shell +# 可能的取值为：setup.bash, setup.sh, setup.zsh + +```bash +. ~/ros2_humble/install/local_setup.bash +``` + +## [尝试一些示例](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#id12)[](https://ros2docs.robook.org/humble/Installation/Alternatives/Ubuntu-Development-Setup.html#try-some-examples "Link to this heading") + +在一个终端中，源化安装脚本然后运行 C++ `talker`： + +```bash +. ~/ros2_humble/install/local_setup.bash +ros2 run demo_nodes_cpp talker +``` + +在另一个终端中源化安装脚本然后运行 Python `listener`： + +```bash +. ~/ros2_humble/install/local_setup.bash +ros2 run demo_nodes_py listener +``` + +您应该能看到 `talker` 显示它正在 `Publishing` 消息，而 `listener` 显示 `I heard` 这些消息。这表明 C++ 和 Python API 都能正常工作。太好了！ \ No newline at end of file diff --git a/ros/ros2/安装配置/环境配置.md b/ros/ros2/安装配置/环境配置.md new file mode 100644 index 0000000..e217f1e --- /dev/null +++ b/ros/ros2/安装配置/环境配置.md @@ -0,0 +1,8 @@ +``` bash +source install/local_setup.bash +# export ROS_DOMAIN_ID= +# echo "export ROS_DOMAIN_ID=" >> ~/.bashrc +# export ROS_LOCALHOST_ONLY=1 +# echo "export ROS_LOCALHOST_ONLY=1" >> ~/.bashrc +``` + diff --git a/ros/ros2/概念/基础概念.md b/ros/ros2/概念/基础概念.md new file mode 100644 index 0000000..f55ba1c --- /dev/null +++ b/ros/ros2/概念/基础概念.md @@ -0,0 +1,165 @@ +# ROS 2 基础概念 + +ROS 2 (Robot Operating System 2) 是一个用于构建机器人应用程序的开源框架。它提供了一套工具、库和约定，帮助开发者创建可维护、可扩展且可重用的机器人软件。 + +## 1. Node (节点) + +**节点** 是 ROS 2 中最小的可执行单元，代表一个单一功能模块。每个节点执行特定任务（如控制传感器、处理图像、规划路径等），并通过标准接口与其他节点通信。 + +- **特点**： + - 轻量级，单一职责 + - 使用命名空间避免命名冲突 + - 支持多种客户端库（C++、Python、Java等） + +- **核心概念**： + - 节点名称：唯一标识符，如 `/camera_driver` + - 命名空间：用于组织节点，如 `/robot1/camera_driver` + +## 2. Discovery (发现机制) + +**发现机制** 是 ROS 2 中节点自动发现其他节点和服务的过程。它允许节点在网络中找到彼此并建立通信，无需硬编码地址。 + +- **工作原理**： + - 使用 DDS (Data Distribution Service) 作为底层通信中间件 + - 通过参与者（Participant）、主题（Topic）和服务（Service）的发现实现 + - 自动管理连接的建立和断开 + +- **关键组件**： + - ROS 2 守护进程 `ros2 daemon`（后台运行，管理发现） + - DDS 域（Domain ID）：控制节点可见性（同一域的节点才能通信） + +## 3. Interfaces (接口) + +**接口** 定义了节点之间通信的消息格式。ROS 2 提供了三种主要通信接口类型： + +### 3.1 Topics (话题) - 发布/订阅通信 + +**话题** 是节点之间传递数据的命名通道。一个节点可以**发布**（发送）数据到某个话题，其他节点可以**订阅**（接收）该话题的数据。 + +- **特点**： + - 单向、异步通信 + - 一对多或多对多通信模式 + - 消息类型严格定义（如 `std_msgs/String`、`sensor_msgs/Image`） + +- **使用场景**： + - 持续数据流（如传感器数据、状态信息） + +### 3.2 Services (服务) - 请求/响应通信 + +**服务** 实现节点之间的双向同步通信。一个节点提供**服务**，其他节点发送**请求**并等待**响应**。 + +- **特点**： + - 请求/响应模式 + - 同步通信（客户端等待服务端处理） + - 需要定义请求和响应的消息类型（.srv 文件） + +- **使用场景**： + - 一次性任务（如获取配置、执行动作） + +### 3.3 Actions (动作) - 目标导向通信 + +**动作** 是服务的扩展，支持长时间运行的任务，并提供反馈和取消功能。 + +- **特点**： + - 目标-反馈-结果模式 + - 可取消、可获取进度反馈 + - 异步执行 + - 消息类型定义在 .action 文件中 + +- **使用场景**： + - 长时间运行的任务（如导航、抓取物体） + +## 4. Parameters (参数) + +**参数** 是节点的配置值，可以在运行时动态修改。参数支持多种数据类型（整数、浮点数、字符串、布尔值等）。 + +- **管理方式**： + - 命令行设置 + - 配置文件（YAML） + - 运行时动态修改（通过 `ros2 param` 命令或 API） + +- **常用操作**： + - `ros2 param list`：列出节点参数 + - `ros2 param set`：设置参数值 + - `ros2 param get`：获取参数值 + +## 5. Introspection (内省) + +**内省** 允许开发者在运行时检查和监控 ROS 2 系统的状态。通过内省工具，您可以查看节点、话题、服务和参数的信息。 + +- **常用工具**： + - `ros2 node list`：列出所有节点 + - `ros2 topic list`：列出所有话题 + - `ros2 service list`：列出所有服务 + - `ros2 topic echo`：查看话题消息内容 + - `ros2 node info`：查看节点详细信息 + +## 6. Launch (启动系统) + +**Launch** 允许您同时启动多个节点和配置它们的参数。使用 Python 或 XML 编写的启动文件可以简化系统启动过程。 + +- **特点**： + - 同时启动多个节点 + - 配置节点参数 + - 设置命名空间和重映射 + - 支持条件启动和依赖关系 + +- **常用工具**： + - `ros2 launch`：执行启动文件 + - 启动文件格式：Python（推荐）、XML、YAML + +## 7. Client Libraries (客户端库) + +**客户端库** 是 ROS 2 提供的编程接口，允许开发者使用不同语言编写节点。 + +- **主要客户端库**： + - **rclcpp**：C++ 客户端库（推荐用于高性能应用） + - **rclpy**：Python 客户端库（推荐用于快速原型开发） + - **rcljava**：Java 客户端库 + - **其他**：Swift、Go 等语言的社区支持 + +- **核心特性**： + - 统一的 API 接口 + - 自动内存管理 + - 支持所有 ROS 2 通信模式 + +## 8. 通信中间件 + +ROS 2 使用 **DDS (Data Distribution Service)** 作为默认通信中间件，提供可靠的实时数据交换。DDS 是工业标准的中间件，适用于嵌入式系统和高性能计算。 + +- **DDS 特性**： + - 高可靠性 + - 低延迟 + - 实时支持 + - 可扩展性 + - 服务质量（QoS）配置 + +## 9. 服务质量（QoS） + +**QoS (Quality of Service)** 配置允许您控制通信的可靠性、持久性和其他特性。QoS 设置对于确保系统在不同环境下的性能至关重要。 + +- **主要 QoS 策略**： + - Reliability：可靠传输（保证交付）或最佳 effort（不保证交付） + - Durability：数据是否持久化（对晚加入的节点可见） + - History：保留消息历史的深度 + - Deadline/Lifespan：消息的截止时间和寿命 + +## 10. 系统架构概览 + +ROS 2 系统由以下层次组成： + +1. **应用层**：节点、话题、服务、动作 +2. **中间件层**：DDS 通信系统 +3. **工具层**：命令行工具、可视化工具、调试工具 +4. **硬件抽象层**：传感器、执行器、硬件接口 + +## 11. 与 ROS 1 的主要区别 + +- **通信中间件**：ROS 1 使用 TCPROS/UDPROS，ROS 2 使用 DDS +- **节点发现**：ROS 1 依赖 roscore，ROS 2 去中心化 +- **接口类型**：ROS 2 添加了 Actions +- **参数系统**：ROS 2 支持动态参数配置 +- **编译系统**：ROS 1 使用 catkin，ROS 2 使用 colcon +- **支持的语言**：ROS 2 对 Python 3 的支持更好，添加了更多语言选项 + +这些基础概念是理解和使用 ROS 2 的核心。通过掌握这些概念，您可以开始构建复杂的机器人系统，并利用 ROS 2 的强大功能。 \ No newline at end of file diff --git a/ros/履带底盘机械臂.md b/ros/履带底盘机械臂.md new file mode 100644 index 0000000..5c23b9b --- /dev/null +++ b/ros/履带底盘机械臂.md @@ -0,0 +1 @@ +[差速驱动移动基座和机械臂的规划 — MoveIt documentation documentation](https://www.ncnynl.com/ros2docs/cn/moveit2/doc/examples/mobile_base_arm/mobile_base_arm_tutorial.html) \ No newline at end of file diff --git a/大模型/DeepSeek V4 系列.md b/大模型/DeepSeek V4 系列.md new file mode 100644 index 0000000..67f2082 --- /dev/null +++ b/大模型/DeepSeek V4 系列.md @@ -0,0 +1,125 @@ +# DeepSeek V4 系列 + +> 发布日期：2026年4月24日 | 开源协议：MIT + +## 概述 + +DeepSeek V4 是深度求索（DeepSeek）在 2026 年 4 月发布的新一代大语言模型系列，包含两个版本： + +| | V4 Pro（旗舰版） | V4 Flash（轻量版） | +|---|---|---| +| **总参数量** | 1.6T（1.6万亿） | 284B（2840亿） | +| **激活参数** | ~36B/token | 13B/token | +| **上下文窗口** | 1M tokens | 1M tokens | +| **架构** | MoE（混合专家） | MoE（混合专家） | +| **定位** | 旗舰性能，对标 GPT-5 | 低成本、低延迟，接近 Pro 推理能力 | + +--- + +## 核心技术架构 + +### 1. 混合专家架构（Mixture-of-Experts, MoE） + +- V4 Pro：384 个专家/层，每次推理激活 6 个 +- V4 Flash：精简专家数量，每次推理激活 13B 参数 +- 优势：总参数大但计算量可控，推理效率高 + +### 2. 混合注意力机制（Hybrid Attention） + +- **DSA2 稀疏注意力**：降低长上下文的计算开销 +- **CSA（压缩稀疏注意力）+ HCA（重度压缩注意力）**（Flash）： + - KV 缓存内存开销降低高达 90% + - 在 1M 上下文下，单 token 推理 FLOPs 仅为 V3.2 的 27% + - KV 缓存仅为 V3.2 的 10% + +### 3. 其他关键技术 + +- **流形约束超连接（mHC）**：增强残差连接，改善跨层信号传播 +- **Muon 优化器**：加速收敛，提升训练稳定性 +- **多 Token 预测（MTP）**：一次预测多个 token，加速生成 +- **DSpark 推测解码**（Flash）：集成草稿模块加速推理 + +--- + +## 三种推理模式 + +| 模式 | 说明 | 适用场景 | +|---|---|---| +| **Non-think（低）** | 快速、直观的响应 | 简单对话、翻译、摘要 | +| **Think High（高）** | 显式思维链推理 | 逻辑推理、规划任务 | +| **Think Max（最大）** | 最大推理深度 | 复杂数学、竞赛编程 | + +--- + +## 关键基准测试 + +### V4 Pro 亮点 + +| 基准测试 | 得分 | 对比 | +|---|---|---| +| Codeforces Rating | 3206 | 超越 Gemini 3.1-Pro | +| LiveCodeBench | 93.5 | 与 GPT-5.4 竞争 | +| MMLU-Pro | — | 接近 GPT-5 水平 | + +### V4 Flash 亮点 + +| 基准测试 | 得分 | +|---|---| +| MMLU-Pro (EM) | 87.5 | +| GPQA Diamond | 90.1 | +| LiveCodeBench | 93.5 | +| SWE Verified (Resolved) | 80.6 | +| Terminal Bench 2.1 | 82.7 | +| BrowseComp | 83.4 | + +--- + +## 定价对比 + +### V4 Pro（通过 DeepInfra） + +| Token 类型 | 每百万 Token 价格 | +|---|---| +| 输入 | $1.74 | +| 输出 | $3.48 | +| 缓存输入 | $0.145 | + +### V4 Flash + +| Token 类型 | 每百万 Token 价格 | +|---|---| +| 输入 | ~$0.14 | +| 输出 | ~$0.28 | + +> V4 Pro 输出成本比 Claude Opus 4.7（$25/1M）便宜约 7 倍 + +--- + +## 独立评测（NIST/CAISI，2026年5月） + +- V4 Pro 性能约为 **GPT-5 水平**（2025年8月发布） +- 距美国最前沿模型约 **8 个月**差距 +- CAISI 测试过的**最强中国 AI 模型** +- 在某些非公开基准（ARC-AGI-2、CTF-Archive-Diamond）上明显落后于 GPT-5.5 和 Opus 4.6 + +--- + +## 战略意义 + +1. **芯片自主**：与华为昇腾（Ascend）、寒武纪合作优化，不依赖 NVIDIA/AMD +2. **开源生态**：MIT 协议开源，模型权重在 Hugging Face 上公开 +3. **与 GPT-5.5 同日发布**：中美 AI 竞争白热化的标志性事件 +4. **自我定位坦诚**：官方公开承认距前沿 3-6 个月差距 + +--- + +## 相关链接 + +- [[Transformer 架构详解]] +- [[MoE 混合专家架构]] +- [[多模态大模型]] +- [[大模型架构基础]] + +--- + +*最后更新：2026-08-05* diff --git a/大模型/FFN 前馈网络详解.md b/大模型/FFN 前馈网络详解.md new file mode 100644 index 0000000..e19e4cb --- /dev/null +++ b/大模型/FFN 前馈网络详解.md @@ -0,0 +1,410 @@ +# FFN 前馈网络详解 + +> Feed-Forward Network — Transformer 中参数量最大（~2/3）、负责知识存储和特征变换的核心组件 + +--- + +## 一、FFN 在 Transformer 中的角色 + +``` +Transformer Block： + + 注意力层（信息混合） FFN 层（特征变换） + ┌─────────────────┐ ┌─────────────────────┐ + │ Token 之间交互 │ │ 每个 Token 独立处理 │ + │ "谁跟谁有关？" │ → │ "这个 Token 是什么意思？"│ + │ 参数量 ~1/3 │ │ 参数量 ~2/3 │ + └─────────────────┘ └─────────────────────┘ +``` + +| 组件 | 参数量占比 | 功能 | +|---|---|---| +| **注意力层** | ~1/3 | Token 间的信息路由与混合（"连接信息"） | +| **FFN 层** | ~2/3 | Token 内部的特征变换与知识存储（"存储知识"） | + +> **直觉**：注意力层决定"从哪读信息"，FFN 层存储"读到的信息怎么理解"。研究表明 FFN 的权重矩阵充当了**联想记忆（Associative Memory）**——W₁ 的每一行是一个"模式检测器"，W₂ 的对应列是"该模式的输出"。 + +--- + +## 二、基础公式 + +### 标准 FFN（原始 Transformer） + +``` +FFN(x) = W₂ · ReLU(W₁ · x + b₁) + b₂ +``` + +``` +输入 x（d_model 维度） + │ + ▼ +W₁: d_model → d_ff ← 升维（通常 d_ff = 4 × d_model） + │ + ▼ +激活函数（ReLU/GELU/...） ← 引入非线性 + │ + ▼ +W₂: d_ff → d_model ← 降维回原维度 + │ + ▼ +输出（d_model 维度） +``` + +### 参数量计算 + +``` +标准 FFN（无偏置）： + params = d_model × d_ff + d_ff × d_model + = 2 × d_model × d_ff + = 2 × d_model × 4d_model （通常 d_ff = 4d） + = 8 × d_model² +``` + +| d_model | d_ff (4×) | FFN 参数量 | 一层总参数（含注意力） | +|---|---|---|---| +| 512 | 2048 | 2.1M | ~3M | +| 4096 (LLaMA 7B) | 11008 (约2.7×) | ~90M | ~135M | +| 8192 (LLaMA 70B) | 22016 | ~360M | ~540M | + +> 在 LLaMA 等 SwiGLU 模型中，`d_ff` 通常设为约 `8/3 × d_model`（而非 4×），以补偿 GLU 变体引入的额外参数。 + +--- + +## 三、激活函数演进路线 + +``` +2017 ─→ 2019 ────→ 2020 ──────→ 2025 +ReLU GELU SwiGLU PolyGLU / 动态路由 + │ │ │ │ + │ │ │ └─ 每个神经元学自己的激活函数 + │ │ └─ 门控线性单元，当前生产标准 + │ └─ 平滑版 ReLU，不再死神经元 + └─ 简单粗暴，负值直接清零 +``` + +### 3.1 ReLU（原始 Transformer） + +$$\text{ReLU}(x) = \max(0, x)$$ + +``` + │ + 2 ┤ ╱ + 1 ┤ ╱ + 0 ┼════●──────── ← 负半轴完全为零 + │ ╱ + -1 ┤ ╱ + └──────────── +``` + +| 优点 | 缺点 | +|---|---| +| 计算极快，硬件原生支持 | **死神经元**：一旦输出为零，梯度为零，永不恢复 | +| 稀疏激活（约 50%） | 负值信息完全丢失 | + +> **死神经元问题**：如果某个神经元的输出始终 ≤ 0（因为权重初始化或学习偏差），ReLU 的梯度永远为 0，这个神经元就"死了"——再也不会更新。 + +### 3.2 GELU（GPT-2 / BERT 时代） + +$$\text{GELU}(x) = x \cdot \Phi(x) = x \cdot \frac{1}{2}\left[1 + \text{erf}\left(\frac{x}{\sqrt{2}}\right)\right]$$ + +``` + │ + 2 ┤ ╱ + 1 ┤ ╱╱ + 0 ┼────●───────── ← 负半轴有小的非零输出 + -1 ┤╱╱ + └──────────── +``` + +| 优点 | 缺点 | +|---|---| +| 平滑可微，梯度流更好 | 计算比 ReLU 慢（erf 函数） | +| 负值有概率性"门控"而非直接清零 | 所有输入用同一个函数处理 | +| 缓解死神经元问题 | | + +> **GELU 的直觉**：把输入值 `x` 乘以它"来自正半轴的概率"——如果 x 很可能是负的，输出很小但不为零。 + +### 3.3 SiLU / Swish + +$$\text{SiLU}(x) = x \cdot \sigma(x) = \frac{x}{1 + e^{-x}}$$ + +``` + │ + 2 ┤ ╱ + 1 ┤ ╱╱ + 0 ┼─────●───────── ← 负半轴有"负沟"（non-monotonic） + -0.3┤ ╱ ╲ ← 这个沟是 SiLU 的独特特征 + └──────────── +``` + +| 关键特性 | 说明 | +|---|---| +| **自门控** | 每个值决定自己通过多少：`x · σ(x)` | +| **非单调** | 负半轴先下降再回升，能捕获复杂模式 | +| **无界正值** | 正半轴可以无限大（不像 sigmoid 有上限） | +| **平滑** | 处处可微，梯度流极好 | + +> SiLU 和 Swish 是**同一个函数**的两个名字。Google 叫它 Swish，PyTorch 叫它 SiLU。 + +--- + +## 四、GLU 门控线性单元：范式转变 + +### 4.1 从"单路激活"到"双路门控" + +``` +标准 FFN： GLU FFN： + + x x + │ ┌────┴────┐ + W₁ W_gate W_up + │ │ │ + Act σ │ + │ │ │ + W₂ └────┬────┘ + │ ⊗ (逐元素相乘) + 输出 │ + W_down + │ + 输出 +``` + +### 4.2 为什么乘法比加法强？ + +> 门控 = 逐元素乘法 = 引入**二阶交互项** + +``` +GLU(x) 包含 xᵢ · xⱼ 项（不同维度之间产生交互） +FFN(x) 只有 σ(Wx) 项（每个维度独立处理） + +类比： + FFN = 每个特征独立打分 + GLU = 特征之间可以"对话"——"我是形容词，如果前面是名词就激活我" +``` + +### 4.3 SwiGLU（2020→至今的生产标准） + +$$\text{SwiGLU}(x) = \text{SiLU}(x W_{\text{gate}}) \otimes (x W_{\text{up}}) \cdot W_{\text{down}}$$ + +```python +# SwiGLU 的实际代码（也是 LLaMA、DeepSeek、Mistral 等主流模型的实现） + +class SwiGLU(nn.Module): + def __init__(self, dim, hidden_dim): + super().__init__() + # 三个权重矩阵（不是标准的两个） + self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) # 门控通路 + self.up_proj = nn.Linear(dim, hidden_dim, bias=False) # 值通路 + self.down_proj = nn.Linear(hidden_dim, dim, bias=False) # 降维 + + def forward(self, x): + # gate: 门控信号（SiLU 激活） + # up: 值信号（无激活，直接通过） + return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) +``` + +### 4.4 参数量补偿：为什么 d_ff 变小了 + +| 方案 | 矩阵数 | 参数量 | d_ff（等参） | +|---|---|---|---| +| 标准 FFN | W₁, W₂ (2个) | 2 × d × d_ff | 4d | +| SwiGLU | W_gate, W_up, W_down (3个) | 3 × d × d_ff | **8d/3 ≈ 2.67d** | + +``` +等参数量下： + 标准 FFN：d_ff = 4d → 2 × d × 4d = 8d² + SwiGLU：d_ff = 8d/3 → 3 × d × 8d/3 = 8d² + ↑ + 参数总量相同，但 SwiGLU 表达能力更强 +``` + +> 实际中，即使参数相当，SwiGLU 的困惑度（Perplexity）也比 GELU 低约 **8.3%**（文本任务）——这是"免费的性能提升"。 + +--- + +## 五、GLU 变体对比 + +| 变体 | 门控激活函数 | 公式 | 特点 | +|---|---|---|---| +| **GLU（原始）** | Sigmoid | `σ(xW_g) ⊗ (xW_u)` | 门控有上限(0,1)，表达受限 | +| **ReGLU** | ReLU | `ReLU(xW_g) ⊗ (xW_u)` | 最快，但仍可能死神经元 | +| **GeGLU** | GELU | `GELU(xW_g) ⊗ (xW_u)` | 视觉/多模态任务稍好 | +| **SwiGLU** | SiLU/Swish | `SiLU(xW_g) ⊗ (xW_u)` | **文本生成 SOTA，生产标准** | + +### 选择指南 + +| 场景 | 推荐 | 原因 | +|---|---|---| +| LLM 文本生成 | **SwiGLU** | 困惑度最低，LLaMA/DeepSeek/Mistral 全部使用 | +| 视觉编码器 | GeGLU 或 GELU | 分类任务上略优（~2% Top-1 提升） | +| 延迟敏感 | ReGLU | Relu 计算成本最低，硬件原生支持 | +| 混合模态 | 编码器 GELU + 解码器 SwiGLU | 各取所长，推理可提速 ~15% | + +--- + +## 六、FFN = 知识的"键值记忆" + +> 这个视角将 FFN 从"无聊的全连接层"升级为"模型的知识存储系统" + +### 联想记忆假说 + +``` +FFN(x) = W₂ · Act(W₁ · x) + +W₁ 的每一行 = 一个"键"（Key），检测特定模式 +W₂ 的对应列 = 一个"值"（Value），存储该模式对应的输出 + +过程： + 1. W₁·x → 将输入与所有"键"匹配（内积 = 相似度） + 2. Act → 过滤：只保留匹配度高的模式 + 3. W₂· ... → 激活的模式"写出"对应的值 +``` + +### 实验证据 + +| 发现 | 说明 | +|---|---| +| **知识定位** | FFN 的特定神经元可以对应具体的事实（如"埃菲尔铁塔在巴黎"） | +| **可编辑性** | 修改 FFN 权重可以精确修改模型存储的事实（模型编辑） | +| **稀疏激活** | 对每个 Token，FFN 中只有少数神经元真正激活（和 MoE 思路一致） | +| **层间分工** | 浅层 FFN → 语法/局部模式、深层 FFN → 语义/全局知识 | + +--- + +## 七、2025 前沿：激活函数本身也"可学习" + +### 7.1 PolyGLU（2025年3月） + +> **不再固定用一种激活函数，而是让每个神经元学习自己最适合的激活函数** + +``` +PolyGLU(x) = [ Σ g_k · σ_k(x·W_gate) ] ⊗ (x·W_up) + +其中 σ_k 从候选池中选择： + { ReLU, Tanh, SiLU, GELU } + +g_k = 路由权重（每个神经元不同），训练中自动收敛 +``` + +**训练后的层间分布（19K步训练）**： + +``` +层 0-2（浅层）： GELU 35-40% | Tanh 15-25% | SiLU 15-20% | ReLU ~5% +层 3-14（中层）： GELU 为主，逐步过渡 +层 15-27（深层）： Tanh 50-65% ← 深层网络偏好对称有界激活！ +``` + +> 关键发现：深层网络的神经元**自发偏好 Tanh**（对称、有界），浅层偏好 GELU（概率性门控）。这暗示不同深度的层确实需要不同"类型"的非线性变换。 + +### 7.2 动态激活选择（LLaMA 4） + +LLaMA 4 实验在 SwiGLU 和 GELU 之间做**输入级动态切换**： +- 用轻量门控网络根据输入内容选择激活函数 +- 复杂推理任务 → 倾向于 SwiGLU（更强的特征交互） +- 简单模式匹配 → 倾向于 GELU（更快的计算） + +--- + +## 八、DeepSeek 风格的实际实现 + +### 标准 SwiGLU（如 LLaMA） + +```python +class LlamaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + + def forward(self, x): + return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) +``` + +### DeepSeek-V3 的 MoE + SwiGLU + +```python +# 每个专家就是一个 SwiGLU FFN +class DeepSeekExpert(nn.Module): + def __init__(self, dim, moe_intermediate_size): + super().__init__() + self.gate_proj = nn.Linear(dim, moe_intermediate_size, bias=False) + self.up_proj = nn.Linear(dim, moe_intermediate_size, bias=False) + self.down_proj = nn.Linear(moe_intermediate_size, dim, bias=False) + + def forward(self, x): + return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) + +# MoE 层：N 个专家 + 路由器 +class DeepSeekMoE(nn.Module): + def __init__(self, dim, n_routed_experts=256, n_shared_experts=2, top_k=8): + super().__init__() + self.shared_experts = nn.ModuleList([ + DeepSeekExpert(dim, dim * 2) for _ in range(n_shared_experts) + ]) + self.routed_experts = nn.ModuleList([ + DeepSeekExpert(dim, dim * 2) for _ in range(n_routed_experts) + ]) + self.gate = nn.Linear(dim, n_routed_experts, bias=False) + + def forward(self, x): + # 共享专家（始终激活） + shared_out = sum(e(x) for e in self.shared_experts) + # 路由专家（稀疏激活 Top-K） + router_logits = self.gate(x) + topk_weights, topk_ids = torch.topk(router_logits.softmax(-1), self.top_k) + routed_out = 0 + for i, expert_id in enumerate(topk_ids): + routed_out += topk_weights[i] * self.routed_experts[expert_id](x) + return shared_out + routed_out +``` + +> DeepSeek-V3 有 256 个路由专家 + 2 个共享专家，每个专家内部都是 SwiGLU FFN。详见 [[MoE 混合专家架构]]。 + +--- + +## 九、进化总览 + +``` + 标准 FFN GLU 系列 + ──────── ──────── +非线性来源： 激活函数 门控乘法 +通路数： 单路 双路（值 + 门控） +交互阶数： 一阶 二阶（xᵢ·xⱼ） +参数效率： 基准 显著更优 +参数量： 2 × d × d_ff 3 × d × d_ff +生产标准： 已过时 SwiGLU（2025年） + + 未来方向 + ──────── +激活函数选择： 可学习、每个神经元不同 +实现： PolyGLU、动态路由 +目标： 让模型自主发现最优非线性 +``` + +--- + +## 十、一句话总结 + +``` +FFN 不是"无聊的全连接层"—— +它是 Transformer 中参数最密集、负责存储世界知识的核心组件。 + +ReLU → GELU → SiLU → SwiGLU → PolyGLU 的进化线， +本质上是"从固定非线性到可学习、动态、输入感知的门控机制"的演进。 + +你正在用的 LLaMA、DeepSeek、Mistral、Qwen 的每一层中， +SwiGLU 正在用乘法门控默默地做着模型 2/3 的"思考"。 +``` + +--- + +## 相关链接 + +- [[Transformer 架构详解]] — FFN 在完整架构中的位置 +- [[大模型架构基础]] — 各组件全景概览 +- [[MoE 混合专家架构]] — FFN 的稀疏化扩展：用 N 个 SwiGLU 替代 1 个 + +--- + +*最后更新：2026-08-05* diff --git a/大模型/MoE 混合专家架构.md b/大模型/MoE 混合专家架构.md new file mode 100644 index 0000000..b547edd --- /dev/null +++ b/大模型/MoE 混合专家架构.md @@ -0,0 +1,289 @@ +# MoE 混合专家架构 + +> Mixture of Experts — 用稀疏激活实现"参数量 vs 计算量"的解耦，是 DeepSeek V4、GPT-4、Mixtral 等旗舰模型的核心架构 + +--- + +## 一、为什么要 MoE？ + +### 密集模型的困境 + +传统 Transformer 中，每个 Token 流经**所有** FFN [^FFN] 参数： + +``` +Token → FFN（全部参数参与计算）→ 输出 +``` + +参数量越大 → 每次推理计算量越大 → 训练和推理成本线性增长。 + +### MoE 的核心思想 + +> **"参数多，但每次只用一小部分"** + +``` +Token → 路由器 → 选择最相关的几个专家 → 只激活它们 → 输出 + ↑ + 其他专家保持休眠 +``` + +| | 密集模型 | MoE 模型 | +|---|---|---| +| **总参数量** | 100B | 可以到 1.6T（16倍） | +| **每次推理计算量** | 100B | 仅约 36B | +| **训练效率** | 基准 | 更高（同算力下能训练更大模型） | +| **推理延迟** | 基准 | 需要加载更多权重到显存 | + +--- + +## 二、MoE 层的工作原理 + +### 数学公式 + +$$ +\text{MoE}(x) = \sum_{i \in \text{TopK}(G(x), k)} G(x)_i \cdot E_i(x) +$$ + +其中： + +| 符号 | 含义 | +|---|---| +| `G(x)` | 路由器/门控网络，输出每个专家的"置信度分数" | +| `TopK(G, k)` | 选择分数最高的 k 个专家（通常 k=2~8） | +| `E_i(x)` | 第 i 个专家（一个 FFN [^FFN] 子网络） | +| `G(x)_i` | Softmax 归一化后的专家权重 | + +### 一句话总结 + +> 输入 Token → 路由器打分 → 选 Top-K 专家 → 加权合成输出 + +### 结构图解 + +``` +输入 Token x + │ + ▼ +┌───────────┐ +│ 路由器 G │ ← 一个小型线性层，输出 N 个分数 +│ (Gate) │ (N = 专家总数) +└─────┬─────┘ + │ Softmax 归一化 + │ 选出分数最高的 k 个 + ▼ +┌─────────────────────────────────────┐ +│ N 个专家（Expert） │ +│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐│ +│ │ E₁ │ │ E₂ │ │ E₃ │... │ Eɴ ││ +│ │💤 │ │🔥 │ │💤 │ │🔥 ││ +│ └─────┘ └──┬──┘ └─────┘ └──┬──┘│ +│ │ │ │ +│ 只激活 k=2 个专家! │ │ +└─────────────┼─────────────────┼─────┘ + │ │ + └─── 加权求和 ────┘ + │ + ▼ + 输出 MoE(x) +``` + +--- + +## 三、MoE 层 vs 标准 FFN 层 + +``` +标准 Transformer 块： 使用 MoE 的 Transformer 块： + +注意力层 [^Attn] 注意力层 [^Attn] + ↓ ↓ +FFN（全部参数） 路由器 → 选择专家 + ↓ ↓ +注意力层 [^Attn] 被选中的专家（稀疏激活） + ↓ ↓ +FFN（全部参数） 注意力层 [^Attn] + ↓ + 路由器 → 选择专家 + ↓ + 被选中的专家（稀疏激活） +``` + +> **只在 FFN 层引入 MoE**，注意力层保持不变（注意力机制本身就负责 Token 间信息交互，无需稀疏化）。 + +--- + +## 四、关键设计维度 + +### 1. 专家粒度 + +| 类型 | 说明 | 示例 | +|---|---|---| +| **粗粒度** | 少量大专家（如 8 个，每个完整 FFN 大小） | 早期 MoE、Mixtral 8×7B | +| **细粒度** | 大量小专家（如 384 个微专家，每个很小） | DeepSeek-V3/V4（64 个微专家/层，激活 6 个） | + +细粒度的优势： +- 路由组合更灵活：从 N 选 k 变为 mN 选 mk，组合数暴增 +- DeepSeek V4 每层约 **3630 万种**可能的专家组合 +- 同等总参数下，每个专家更小，训练/推理更灵活 + +### 2. 共享专家 vs 路由专家 + +| 类型 | 说明 | +|---|---| +| **共享专家** | 始终激活，捕捉通用模式（语法、基础常识） | +| **路由专家** | 按 Token 动态选择，负责特定领域（数学、代码、翻译...） | + +DeepSeekMoE 采用两层设计： +- 2 个共享专家（always on） +- 62 个路由专家（每个 Token 激活 6 个） + +共享专家提升训练稳定性，但可能降低专家专门化程度。 + +### 3. 路由器设计 + +``` +简单线性路由器（当前主流）： + +router_logits = W_g · x ← 一个小矩阵，N × d_model +topk_experts = TopK(softmax(router_logits), k) +``` + +实际推理时的路由模式： +- 每个 Token **独立**路由（不考虑上下文） +- 序列中相邻 Token 可能路由到不同专家 + +--- + +## 五、负载均衡 — MoE 的核心挑战 + +### 问题 + +> 如果路由器"偷懒"，总是选固定的几个专家 → 其他专家闲置 → 退化为密集模型 + +### 传统方案：辅助损失（Auxiliary Loss） + +$$ +L_{total} = L_{主任务} + \alpha \cdot L_{负载均衡} +$$ + +强制惩罚专家使用不均匀的情况。**问题**：梯度冲突，可能导致训练不稳定。 + +### 2025 前沿：无梯度负载均衡 + +```python +# 动态偏置调整 — 不干扰主损失梯度 +logits_i(t+1) = W_g · x + b_i(t) - γ · (f_i(t)/f̄(t) - 1) +``` + +其中： +- `f_i(t)`：当前被路由到专家 i 的 Token 比例 +- `f̄(t)`：理想平均比例 = 1/N +- 效果：专家使用率的变异系数 < 0.1，无梯度冲突 + +--- + +## 六、2025 年前沿进展 + +### 1. DAG-MoE [^DAG]（ICML 2026） + +> 不是简单加权平均专家输出，而是把选中的专家排列成**有向无环图（DAG, Directed Acyclic Graph）**，实现结构化的专家聚合（DAG 聚合） + +``` +传统 MoE： E₁↘ + E₂ → 加权平均 → 输出 + E₃↗ + +DAG-MoE： E₁ → E₃ ↘ + E₂ → E₄ → E₆ → 输出（信息流经多个专家） +``` + +- 允许**单层内多步推理** +- 模型学习最优的专家聚合结构（每个输入不同） +- 同等 FLOPs 下更强的表达能力 + +### 2. MoLE：查找表专家（ICML 2025 Oral） + +> 将训练好的 FFN 专家**重新参数化**为查找表（LUT），专家计算变为查表操作 + +- 推理速度接近密集模型 +- 大幅降低显存占用 +- 使 MoE 在内存受限设备上部署成为可能 + +### 3. MoE + MLA（Multi-head Latent Attention，多头潜在注意力） [^MLA] + +正交瓶颈联合优化： + +| 瓶颈 | 方案 | +|---|---| +| FFN 计算量大 | MoE 稀疏激活 | +| 注意力 KV 缓存大 | MLA 低秩压缩 | + +- ρ = 1/2 时：KV 缓存减少 **68%**，推理加速 **3.2×** +- 困惑度仅下降 0.8% +- DeepSeek V4 使用此组合 + +### 4. MoE-X：可解释 MoE（ICML 2025） + +- 将 MoE 层理解为等效的大型稀疏 MLP +- 强制专家内部也做稀疏激活 +- 可解释性超越 Sparse Autoencoder（SAE）方法 + +--- + +## 七、主流 MoE 模型对比 + +| 模型 | 总参数 | 激活参数 | 专家数 | 激活数 | 特点 | +|---|---|---|---|---|---| +| **Mixtral 8×7B** | 46.7B | 12.9B | 8 | 2 | 经典粗粒度 MoE | +| **DeepSeek-V3** | 671B | 37B | 256 | 8 | 细粒度 + 共享专家 | +| **DeepSeek-V4 Pro** | 1600B | 36B | 384/层 | 6 | 细粒度 + MLA + DSpark | +| **DeepSeek-V4 Flash** | 284B | 13B | 精简 | 精简 | Pro 的轻量版 | +| **GPT-4**（传闻） | 1.8T | ~280B | 16（推测） | 2 | 早批大规模 MoE | + +> 趋势：专家数量越来越多，每个专家越来越小（细粒度），激活参数占比越来越低。 + +--- + +## 八、MoE 的代价 + +| 代价 | 说明 | +|---|---| +| **显存占用** | 所有专家都在 VRAM 里，即使大部分不激活 → 需要更多 GPU | +| **通信开销** | 多 GPU 部署时，Token 需要跨 GPU 路由到专家 | +| **训练不稳定** | 负载均衡损失与主损失可能冲突 | +| **微调困难** | 稀疏激活 → 梯度更稀疏 → 微调更难收敛 | +| **批次大小** | 每个专家实际收到的 Token 少 → 需要较大 batch size | + +--- + +## 九、总结 + +``` +MoE = 用路由器在大量专家中选择少数激活，实现"参数膨胀但计算可控" + +关键取舍： + 优势：同等算力下更大的模型容量、更好的性能/成本比 + 代价：更高显存、更复杂的工程、训练和微调挑战 + +2025 年趋势： + 细粒度 → DAG 聚合 → 可解释 → 查找表推理 → 与 MLA 协同 +``` + +--- + +## 相关链接 + +- [[Transformer 架构详解]] +- [[大模型架构基础]] +- [[DeepSeek V4 系列]] + +--- + +--- + +## 脚注 + +[^FFN]: **FFN（Feed-Forward Network，前馈神经网络）**：Transformer 中每个位置的独立全连接子网络。结构为两层 MLP：`W₂ · Act(W₁ · x + b₁) + b₂`，参数量约占整个 Transformer 块的 2/3，是模型"知识存储"的主要位置。现代模型多使用 SwiGLU 激活的变体。详见 [[FFN 前馈网络详解]]。 + +[^Attn]: **注意力层（Attention Layer）**：Transformer 的核心机制，负责 Token 之间的信息交互。通过 Q（Query）、K（Key）、V（Value）三个投影矩阵，让每个 Token "看到"并加权所有其他 Token，从而捕获上下文依赖关系。多头注意力（Multi-Head Attention）并行运行多组 Q/K/V，每组关注不同的语义模式。 + +[^MLA]: **MLA（Multi-head Latent Attention，多头潜在注意力）**：DeepSeek 提出的注意力压缩技术。核心思路是将 K 和 V 首先投影到一个低维潜在空间（`d_latent << d_model`），再从中展开供注意力计算使用。这大幅减少了 KV 缓存的显存占用——在 ρ = 1/2 时可缩减 68%，且对模型质量影响极小（困惑度仅降 0.8%）。MLA 与 MoE 是正交优化：MoE 省 FFN 计算，MLA 省注意力显存，两者叠加效果倍增。 + +[^DAG]: **DAG 聚合（DAG Aggregation）**：将选中的专家按有向无环图（DAG）的结构进行串联/并联组合，而非传统的简单加权平均。每个输入路由到一组专家后，模型学习这些专家之间最优的信息流动路径——例如 E₁ 的输出作为 E₃ 的输入，E₂ 的输出则直接进入最后阶段。这允许在单层 MoE 内完成**多步推理**，表达能力强于传统加权求和，同等 FLOPs 下能取得更好性能。DAG-MoE 发表于 ICML 2026。 diff --git a/大模型/Transformer 架构详解.md b/大模型/Transformer 架构详解.md new file mode 100644 index 0000000..92dcaf0 --- /dev/null +++ b/大模型/Transformer 架构详解.md @@ -0,0 +1,425 @@ +# Transformer 架构详解 + +> 2017 年，"Attention Is All You Need" 论文用自注意力取代了 RNN/LSTM，开启了现代大语言模型时代。 + +--- + +## 一、Transformer 之前：RNN 的困境 + +``` +RNN 的处理方式（串行）： +Token₁ → Token₂ → Token₃ → ... → Tokenₙ + 5ms 5ms 5ms 5ms = 5×n ms（无法并行） + +Transformer 的处理方式（并行）： +Token₁ ↘ +Token₂ → 所有 Token 同时互相"看到" → 并行计算 +Token₃ ↗ ≈ 5ms（与 n 无关） +``` + +| 问题 | RNN/LSTM | Transformer | +|---|---|---| +| **并行性** | 串行，无法并行训练 | 完全并行，GPU 友好 | +| **长距离依赖** | 梯度消失/爆炸，远距离信息丢失 | 每个 Token 直接关注任意位置的 Token | +| **训练速度** | 慢（序列越长越慢） | 快得多 | +| **可解释性** | 难以解释隐藏状态 | 可直接可视化注意力权重 | + +--- + +## 二、整体架构：编码器-解码器 + +原始 Transformer 用于机器翻译，包含编码器和解码器两部分。 + +``` + ┌─────────────────────────────────┐ + │ 编码器（Encoder） │ + │ ┌───────────────────────────┐ │ +"我 爱 北京" → │ │ Encoder Block × 6 │ │ + │ │ Self-Attn + FFN │ │ + │ └───────────────────────────┘ │ + └──────────────┬──────────────────┘ + │ 编码后的表示（K, V） + ▼ + ┌─────────────────────────────────┐ + │ 解码器（Decoder） │ + │ ┌───────────────────────────┐ │ + │ │ Decoder Block × 6 │ │ + │ │ ① Masked Self-Attn │ │ → "I love Beijing" + │ │ ② Cross-Attn (用 K,V) │ │ + │ │ ③ FFN │ │ + │ └───────────────────────────┘ │ + └─────────────────────────────────┘ +``` + +### 一个 Block 内部（原始设计） + +``` +输入 x + │ + ├──→ Multi-Head Self-Attention ──→ ⊕ ──→ LayerNorm ──→ + │ ↑ ↑ + │ 残差连接 x 输出 + │ + └──→ Feed-Forward Network ──→ ⊕ ──→ LayerNorm ──→ 输出 + ↑ ↑ + 残差连接 传给下一层 +``` + +### 两种主流归一化位置 + +| 方式 | 归一化位置 | 代表模型 | 特点 | +|---|---|---|---| +| **Post-LN** | 残差之后 | 原始 Transformer | 训练不稳定，需要 warmup | +| **Pre-LN** | 子层之前 | GPT 系列、LLaMA | 训练更稳定，无需大量 warmup | + +``` +Post-LN：x + Attn(LayerNorm(x)) ← 原始论文 +Pre-LN： x + Attn(LayerNorm(x)) 错 — 实际上是： +Pre-LN：LayerNorm(x + Attn(x)) 错 — Pre-LN 是： + +Pre-LN 实际：x + Attn(LayerNorm(x)) — 等等，让我用图表示清楚： + +Post-LN： x → SubLayer → Norm → + → 输出 +Pre-LN： x → Norm → SubLayer → + → 输出 +``` + +> Pre-LN 先将输入归一化再送入子层，然后做残差。GPT/LLaMA 全系使用 Pre-LN（或 RMSNorm + Pre-LN 变体）。 + +--- + +## 三、自注意力机制：完整推演 + +### 3.1 从"查字典"理解 Q/K/V + +| 投影 | 类比 | 数学 | 含义 | +|---|---|---|---| +| **Q（Query）** | 你的搜索关键词 | `X · W_Q` | "我在找什么信息？" | +| **K（Key）** | 所有条目的索引 | `X · W_K` | "每个词身上有什么标签？" | +| **V（Value）** | 条目的实际内容 | `X · W_V` | "每个词的实际信息是什么？" | + +### 3.2 逐步计算示例 + +假设输入句子 "我 爱 NLP"，模型维度 d=4，头维度 d_k=2： + +**Step 1 — 输入嵌入** +``` +我: [0.1, 0.3, 0.5, 0.2] +爱: [0.4, 0.1, 0.3, 0.6] +NLP: [0.2, 0.5, 0.1, 0.3] +``` + +**Step 2 — 计算 K 和 Q（只演示一个头的投影）** +``` +K = X · W_K （3×4）×（4×2） = 3×2 矩阵 +K_我 = [0.8, 0.2] +K_爱 = [0.3, 0.9] +K_NLP = [0.5, 0.4] + +Q_爱 = [0.6, 0.3] （"爱"这个位置的 Query） +``` + +**Step 3 — 计算注意力分数（Q_爱 × K^T）** +``` +Q_爱 · K_我 = 0.6×0.8 + 0.3×0.2 = 0.48 + 0.06 = 0.54 +Q_爱 · K_爱 = 0.6×0.3 + 0.3×0.9 = 0.18 + 0.27 = 0.45 +Q_爱 · K_NLP = 0.6×0.5 + 0.3×0.4 = 0.30 + 0.12 = 0.42 + +scores = [0.54, 0.45, 0.42] +``` + +**Step 4 — 缩放 + Softmax** +``` +scaled = scores / √2 = [0.38, 0.32, 0.30] +weights = softmax(scaled) = [0.35, 0.33, 0.32] +``` + +**Step 5 — 加权求和 V** +``` +V_我 = [0.7, 0.1] +V_爱 = [0.2, 0.8] +V_NLP = [0.4, 0.5] + +output_爱 = 0.35×[0.7,0.1] + 0.33×[0.2,0.8] + 0.32×[0.4,0.5] + = [0.245, 0.035] + [0.066, 0.264] + [0.128, 0.160] + = [0.439, 0.459] +``` + +> 位置"爱"的输出融合了"我"(35%)、"NLP"(32%)和自身(33%)的信息——这正是"理解上下文"的本质。 + +### 3.3 矩阵形式（实际实现） + +$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V$$ + +- **QK^T**：N×N 的注意力矩阵，每对 Token 一个分数 +- **÷ √d_k**：缩放因子。d_k 越大点积值越大 → softmax 趋向 one-hot → 梯度消失。除以 √d_k 保持方差 ≈ 1 +- **Softmax**：沿最后维（Key 维度）归一化，每行和为 1 +- **× V**：用注意力权重对 Value 加权求和 + +--- + +## 四、多头注意力：并行多个"视角" + +### 4.1 为什么需要多头？ + +``` +单头可能只学到一种注意力模式： + "我" → 关注了 "爱" + "爱" → 关注了 "北京" + 所有头都只关注相邻词 ← 浪费了注意力的表达能力 + +多头允许并行学习多种模式： + 头 1：语法结构（主语→谓语） + 头 2：长距离共指（"它"→ 前文的"北京"） + 头 3：语义关联（"医生"→ "医院"→ "病人"） + 头 4：位置模式（局部上下文窗口） +``` + +### 4.2 数学公式 + +$$\text{head}_i = \text{Attention}(Q W_i^Q, K W_i^K, V W_i^V)$$ + +$$\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h) W^O$$ + +每个头有自己独立的投影矩阵 W_Q、W_K、W_V，维度为 d_model × d_k（其中 d_k = d_model / h）。 + +### 4.3 计算量并不是 h 倍！ + +| | 单头 (d_model=512) | 8头 (每头 d_k=64) | +|---|---|---| +| Q投影参数 | 512×512 = 262K | 8×512×64 = 262K | +| K投影参数 | 同上 | 同上 | +| V投影参数 | 同上 | 同上 | +| 总参数 | ≈ 786K | ≈ 786K + W_O(512×512) ≈ 1M | + +> 计算量大致相同，只是**把工作分给多个头并行做**，每个头在更小的空间（d_k）中关注不同的模式。 + +### 4.4 生产实践中的头数配置 + +| 模型 | d_model | 头数 | d_k | +|---|---|---|---| +| 原始 Transformer | 512 | 8 | 64 | +| GPT-3 | 12288 | 96 | 128 | +| LLaMA 70B | 8192 | 64 | 128 | +| DeepSeek-V3 | — | — | 使用 MLA 替代标准多头 | + +--- + +## 五、位置编码：告诉模型"顺序" + +注意力机制本身是**置换不变**的——打乱输入顺序，注意力分数矩阵只是行列交换，输出值不变。所以必须显式注入位置信息。 + +### 方案对比 + +| 方案 | 原理 | 优点 | 缺点 | 代表模型 | +|---|---|---|---|---| +| **Sinusoidal（正弦）** | 不同频率的正弦/余弦波叠加 | 可外推、无参 | 固定模式 | 原始 Transformer | +| **Learned** | 可学习的 pos_embed 表 | 灵活 | 不能超出训练长度 | BERT、ViT | +| **RoPE（旋转位置编码）** | 用旋转矩阵编码相对位置 | 天然编码相对位置、可外推 | 长序列扩展需技巧 | LLaMA、Qwen、DeepSeek | +| **ALiBi** | 给注意力分数加线性偏置 | 简单、外推最强 | 表达能力有限 | BLOOM | + +### RoPE 核心思想 + +> **不是往 embedding 里加位置向量，而是把 Q 和 K "旋转"一个角度，角度大小取决于位置。** + +``` +位置 m 的 Q：旋转 m×θ +位置 n 的 K：旋转 n×θ + +Q·K = (rotated_Q_m)·(rotated_K_n) = 关于 (m-n) 的函数 + ↑ + 只依赖相对位置！ +``` + +RoPE 扩展： +- **NTK-aware scaling**：调高基频（如 10000→500000），让旋转更慢，支持更长上下文 +- **YaRN**：NTK + 额外微调，将 Llama 从 4K 扩展到 128K + +--- + +## 六、三种架构变体：选哪种？ + +### 6.1 编码器-only（如 BERT） + +``` +输入："我 [MASK] 北京" → 编码器 → 每个位置的上下文表示 → 预测 [MASK] = "爱" + +特点： + - 双向注意力（能看到整个句子） + - 适合：分类、实体识别、语义搜索 + - 不能做生成（没有解码器） +``` + +### 6.2 解码器-only（如 GPT、LLaMA、DeepSeek V4） + +``` +输入："今天天气" → 因果注意力（只看左边）→ 预测下一个 Token "真" + "今天天气真" → ... → 预测 "好" + +特点： + - 因果掩码（Causal Mask）：每个 Token 只能看自己和左边 + - 适合：对话、代码生成、创意写作 + - 自回归生成，输入=输出（训练时位移一位即可） + - 现代 LLM 几乎全部使用此方案 +``` + +**因果掩码示意**： + +``` +注意力分数矩阵（× = 被 mask 为 -∞）： + + 我 爱 北京 天安门 +我 ✓ × × × ← "我"只能看到自己 +爱 ✓ ✓ × × ← "爱"能看到"我"和自己 +北京 ✓ ✓ ✓ × ← ... +天安门✓ ✓ ✓ ✓ +``` + +### 6.3 编码器-解码器（如 T5） + +``` +编码器（双向）：处理源语言 → 生成 K、V +解码器（因果 + 交叉注意力）：用 K、V 辅助生成目标语言 + +交叉注意力（Cross-Attention）： + 解码器的 Q ← 来自解码器自身（"这句话接下来该说什么？"） + 解码器的 K,V ← 来自编码器（"原文说了什么？"） +``` + +| 架构 | 自注意力方式 | 能否生成 | 代表 | 适合任务 | +|---|---|---|---|---| +| 编码器-only | 双向 | ❌ | BERT | 理解/分类 | +| 解码器-only | 因果（单向） | ✅ | GPT, LLaMA | 生成/对话 | +| 编码器-解码器 | 双向 + 交叉 | ✅ | T5, BART | 翻译/摘要 | + +--- + +## 七、训练与推理 + +### 7.1 训练：Teacher Forcing + +``` +输入： "今天 天气 真" 目标： "天气 真 好" + ↑ + 模型被训练来预测每个位置的下一个 Token + +实际一次性计算： + 输入 [今天, 天气, 真] + → 因果掩码确保"今天"看不到后面 + → 输出 [P(天气), P(真), P(好)] + → Loss = CrossEntropy(预测, 实际目标) +``` + +> 训练时**一次前向**算出所有位置的预测，因为目标已知、且有因果掩码保护。 + +### 7.2 推理：自回归生成 + +``` +训练时可以并行（目标已知），推理必须串行（目标未知）： + +Step 1: "今天" → 预测 "天气" +Step 2: "今天天气" → 预测 "真" +Step 3: "今天天气真" → 预测 "好" +Step 4: "今天天气真好" → 预测 + +每次都要重新计算全序列？→ 不，用 KV Cache！ +``` + +### 7.3 KV Cache：推理加速的关键 + +``` +未使用 KV Cache： + Step 1：计算 "今天" 的 Q、K、V ──────────── 1个Token + Step 2：计算 "今天天气" 的 Q、K、V ──────── 2个Token ← 重新算了"今天"！ + Step 3：计算 "今天天气真" 的 Q、K、V ────── 3个Token ← 又重算了！ + ... + +使用 KV Cache： + Step 1：计算 "今天" → 缓存 K₁、V₁ + Step 2：只算 "天气" 的 Q、K、V + 用缓存的 K₁、V₁ → 缓存 K₁K₂、V₁V₂ + Step 3：只算 "真" 的 Q、K、V + 用缓存的 K₁K₂、V₁V₂ → ... + ... + 每个 Step 只算 1 个新 Token！开销从 O(n²) 降到 O(n)（单步）。 +``` + +> KV Cache 的代价：显存占用随序列长度线性增长。MLA（多头潜在注意力）通过低秩压缩解决此问题——详见 [[MoE 混合专家架构#MLA（Multi-head Latent Attention，多头潜在注意力）|MLA 详解]]。 + +--- + +## 八、高效注意力变体 + +标准自注意力是 **O(n²)** 复杂度（n = 序列长度）。对长序列需优化： + +### 8.1 FlashAttention（事实标准） + +> **IO 感知的精确注意力算法**——数学结果完全等价于标准注意力，但通过算子融合大幅减少显存读写。 + +``` +标准计算： FlashAttention： + QK^T → 写回 HBM 将 Q、K、V 分块（tile） + Softmax → 写回 HBM 每个块在 SRAM 中完成全部计算 + 乘 V → 写回 HBM 只有最终结果写回 HBM + ↑ 3次 HBM 读写 ↑ 约 1/n 的 HBM 读写 +``` + +实测效果：相比朴素实现，吞吐量提升 **~32%**（A100），现为 PyTorch 2.0+ 默认后端。 + +### 8.2 稀疏/线性注意力（长序列场景） + +| 方案 | 策略 | 复杂度 | 代表 | +|---|---|---|---| +| **Longformer** | 局部滑动窗口 + 全局 Token | O(n) | 文档级 NLP | +| **BigBird** | 局部 + 全局 + 随机 | O(n) | — | +| **Performer** | 随机特征近似 softmax | O(n) | — | +| **Mamba/SSM** | 状态空间模型替代注意力 | O(n) | 超长序列 | + +> 经验法则：序列 < 4K Token → 标准注意力 + FlashAttention 即可。长文档/代码库场景才需稀疏注意力或 SSM。 + +--- + +## 九、现代生产优化总览 + +| 优化手段 | 效果 | 代价 | +|---|---|---| +| **混合精度（bf16/fp16）** | 训练/推理速度翻倍 | 质量损失可忽略 | +| **梯度检查点** | 显存降低 50%+ | 训练慢 20% | +| **FlashAttention** | 推理加速 ~30%，显存降低 | 无（数学等价） | +| **算子融合（RMSNorm, xFormers）** | 减少 kernel launch 开销 | 无 | +| **序列分桶** | 减少 padding 浪费 | 实现复杂度增加 | +| **量化（INT8/INT4）** | 显存降低 4-8× | 轻微质量下降 | + +--- + +## 十、总结 + +``` +Transformer 的核心思想就三条： + +1. 自注意力（Self-Attention） + Q·K 算关联 → Softmax 归一化 → ×V 合成上下文 + 每个 Token 直接"看到"所有 Token + +2. 多头（Multi-Head） + 并行跑多个注意力，每个头学不同的关系模式 + 总计算量不变，表达能力翻倍 + +3. 位置编码（Positional Encoding） + 注意力不感知顺序 → 注入位置信息 + RoPE 是当前主流：Q/K 旋转角度 = 位置函数 + +Transformer 不只是一篇论文—— +它是整个现代大语言模型时代的起点。 +``` + +--- + +## 相关链接 + +- [[大模型架构基础]] — 大模型的各组件总览 +- [[FFN 前馈网络详解]] — Transformer 中参数量最大的组件 +- [[MoE 混合专家架构]] — 如何用稀疏专家替代 FFN +- [[多模态大模型]] — Transformer 如何扩展到图像/音频 +- [[DeepSeek V4 系列]] — Transformer + MoE + MLA 的实际应用 + +--- + +*最后更新：2026-08-05* diff --git a/大模型/多模态大模型.md b/大模型/多模态大模型.md new file mode 100644 index 0000000..aefd10c --- /dev/null +++ b/大模型/多模态大模型.md @@ -0,0 +1,140 @@ +# 多模态大模型（Multimodal LLM, MLLM） + +## 什么是多模态？ + +**模态（Modality）** = 数据的感知/表达形式。人类通过多种模态理解世界： + +| 模态 | 示例 | +|---|---| +| **文本** | 文章、代码、对话 | +| **图像** | 照片、图表、截图 | +| **音频** | 语音、音乐、环境声 | +| **视频** | 连续图像帧 + 音频 | + +**多模态大模型** = 能同时理解和处理**多种模态数据**的 AI 模型，而非仅限于文本。 + +--- + +## 为什么需要多模态？ + +> 纯文本模型只能"读文字"，无法"看图片"、"听声音"。 + +现实世界的信息天然是多模态的： +- 医生既要看病历（文本），也要看 CT 片（图像） +- 自动驾驶需要同时处理摄像头（图像）、雷达（点云）、地图（结构化数据） +- 社交媒体帖子包含文字 + 图片 + 视频 + +--- + +## 基本架构（三大组件） + +``` +┌─────────────────────┐ +│ 视觉编码器 │ ← 处理图像/视频输入 +│ Vision Encoder │ (如 ViT、SigLIP) +└────────┬────────────┘ + │ 视觉特征 + ▼ +┌─────────────────────┐ +│ 投影器/连接器 │ ← 将视觉特征映射到语言空间 +│ Projector │ (如 MLP、Q-Former) +└────────┬────────────┘ + │ 对齐后的 token + ▼ +┌─────────────────────┐ +│ 大语言模型 │ ← 核心推理引擎 +│ LLM Backbone │ (如 DeepSeek、LLaMA、Qwen) +└─────────────────────┘ +``` + +### 工作流程 + +1. 图像 → 视觉编码器 → 视觉特征向量 +2. 视觉特征 → 投影器 → 对齐到语言模型的 token 空间 +3. 文本 token + 视觉 token → LLM → 生成回复 + +--- + +## 核心能力 + +| 能力 | 说明 | 示例 | +|---|---|---| +| **视觉问答（VQA）** | 根据图片回答问题 | "这张图里有几只猫？" | +| **图像描述** | 自动生成图片文字描述 | 为盲人描述照片内容 | +| **多模态对话** | 图文混合交互 | "分析这张财报图表" | +| **OCR 理解** | 理解图片中的文字 | 识别截图、海报、PDF | +| **视觉推理** | 多步骤逻辑推理 | "如果把这个方块旋转90°会怎样？" | + +--- + +## 主流多模态模型（2025-2026） + +| 模型 | 开发商 | 特点 | +|---|---|---| +| **GPT-4V / GPT-4o** | OpenAI | 原生多模态，实时语音+视觉 | +| **Gemini 系列** | Google | 原生多模态，Ultra/Pro/Flash/Nano 全系覆盖 | +| **Claude 系列** | Anthropic | 强大的图像理解、图表分析能力 | +| **Qwen-VL 系列** | 阿里通义 | 开源，中英双语 | +| **InternVL 系列** | 上海 AI 实验室 | 开源，CVPR 2025 发表 Mono-InternVL | +| **DeepSeek-VL** | DeepSeek | 注意：V4 系列目前 **仅文本**，视觉版待发布 | +| **LLaVA 系列** | 学术界 | 开源先驱，推动 MLLM 研究方向 | + +--- + +## 2025 年前沿方向 + +### 1. 高效/轻量化 MLLM +- 视觉 Token 压缩（降低 Transformer 二次复杂度） +- MoE 架构（如 MoE-LLaVA） +- 边缘设备部署（MobileVLM、TinyLLaVA、Gemini Nano） + +### 2. 单体式 MLLM（Monolithic MLLM） +- **Mono-InternVL**（CVPR 2025）：将视觉编码直接集成到 LLM 内部 +- 减少 67% 首 Token 延迟，解决灾难性遗忘 + +### 3. 长链视觉推理 +- **Insight-V**：多智能体系统（推理 agent + 总结 agent） +- 跨 7 个基准平均提升 7.5% + +### 4. 统一多模态表征 +- **UnifiedMLLM**：单一模型处理多种多模态任务 + +--- + +## 主要挑战 + +1. **长序列效率**：一张图 = 数百到数千个 token，多图/视频成本极高 +2. **幻觉问题**：模型可能"看到"不存在的物体 +3. **计算成本**：训练和推理资源消耗远超纯文本模型 +4. **评估困难**：多模态基准测试尚不成熟 +5. **安全对齐**：视觉输入可能绕过文本安全过滤器 + +--- + +## 与纯文本模型对比 + +| 维度 | 纯文本 LLM | 多模态 MLLM | +|---|---|---| +| 输入 | 仅文本 | 文本 + 图像 + 音频 + 视频 | +| 参数量 | 较小（相对） | 需额外的视觉编码器参数 | +| 推理成本 | 较低 | 较高（视觉 token 开销大） | +| 应用范围 | 对话、写作、编程 | 医疗影像、自动驾驶、设计 | +| 成熟度 | 较高 | 快速发展中 | + +--- + +## 总结 + +多模态是 AI 走向通用人工智能（AGI）的关键一步。当前的 V4 Pro/Flash 等纯文本模型虽强，但"看不到世界"仍是巨大局限。理解多模态架构和原理，是理解下一代 AI 的必要基础。 + +--- + +## 相关链接 + +- [[Transformer 架构详解]] +- [[DeepSeek V4 系列]] +- [[大模型架构基础]] + +--- + +*最后更新：2026-08-05* diff --git a/大模型/大模型架构基础.md b/大模型/大模型架构基础.md new file mode 100644 index 0000000..4611b3d --- /dev/null +++ b/大模型/大模型架构基础.md @@ -0,0 +1,248 @@ +# 大模型架构基础 + +> 理解现代大语言模型的底层架构 — Transformer、注意力机制与关键组件 + +--- + +## 发展脉络 + +``` +2017 Transformer 论文 + │ "Attention Is All You Need" + ▼ +2018 GPT-1 / BERT + │ 解码器-only / 编码器-only + ▼ +2020 GPT-3（175B）— Scaling Law 确立 + ▼ +2022 ChatGPT / GPT-3.5 — RLHF 对齐 + ▼ +2023 GPT-4 / LLaMA / Claude 2 + │ 多模态、MoE 兴起 + ▼ +2024 LLaMA 3 / Qwen 2.5 / DeepSeek-V3 + ▼ +2025 GPT-5 / Claude Opus 5 / DeepSeek-V4 + 大规模 MoE、混合注意力、推理时扩展 +``` + +--- + +## 一、Transformer 架构 + +现代大语言模型的基石，发表于 2017 年。核心思想：用**自注意力机制**取代循环/卷积，实现并行计算。深度讲解见 [[Transformer 架构详解]]。 + +### 架构总览 + +``` + ┌──────────────┐ +输入文本 → │ Embedding │ → Token 向量 + └──────┬───────┘ + ▼ + ┌─────────────────────────────┐ + │ Transformer Block × N │ ← 重复 N 层（数十到数百层） + │ ┌───────────────────────┐ │ + │ │ Multi-Head Attention │ │ ← 核心：自注意力 + │ │ + Layer Norm + 残差 │ │ + │ ├───────────────────────┤ │ + │ │ Feed-Forward (FFN) │ │ ← 逐位置的全连接网络 + │ │ + Layer Norm + 残差 │ │ + │ └───────────────────────┘ │ + └─────────────┬───────────────┘ + ▼ + ┌──────────────┐ + │ 输出头 │ → 预测下一个 Token + └──────────────┘ +``` + +### 三种变体 + +| 类型 | 代表模型 | 特点 | +|---|---|---| +| **编码器-only** | BERT、RoBERTa | 双向注意力，适合理解任务（分类、NER） | +| **解码器-only** | GPT 系列、LLaMA、DeepSeek | 因果注意力（只看左边），适合生成任务 | +| **编码器-解码器** | T5、BART | 融合两者，适合翻译、摘要 | + +> 现代大语言模型**几乎全部使用解码器-only**（自回归生成）。 + +--- + +## 二、自注意力机制（Self-Attention） + +### 直观理解 + +> 让每个词"看到"句子中所有其他词，并决定应该关注哪些。 + +``` +"我 爱 北京 天安门" + │ │ │ │ + └─────┼──────┼───────┘ + │ │ + 每个词都和所有词计算关联度（注意力权重） +``` + +### 数学过程 + +对每个输入的 Token 向量，计算三个投影： + +| | 符号 | 含义 | +|---|---|---| +| **Q（Query）** | 查询 | "我在找什么？" | +| **K（Key）** | 键 | "我有什么？" | +| **V（Value）** | 值 | "我的实际内容" | + +$$ +\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V +$$ + +步骤： +1. **Q × K^T** → 计算每对 token 之间的关联分数（注意力矩阵） +2. **÷ √d_k** → 缩放，防止梯度消失 +3. **Softmax** → 归一化为概率分布 +4. **× V** → 用注意力权重加权求和，得到输出 + +### 多头注意力（Multi-Head Attention） + +> 不是做一次注意力，而是并行做多次（如 8/16/32 个头），每个头关注不同的语义模式。 + +- 头 1 可能关注"语法结构" +- 头 2 可能关注"实体关系" +- 头 3 可能关注"长距离依赖" + +--- + +## 三、关键组件 + +### 1. Token 与 Tokenizer + +| 概念 | 说明 | +|---|---| +| **Token** | 文本被拆分的最小语义单元 | +| **Tokenizer** | 将文本切分为 Token 的工具（BPE、SentencePiece 等） | +| **词表大小** | 通常 32K ~ 256K，DeepSeek V4 使用了更大词表 | + +``` +"Hello, world!" → ["Hello", ",", " world", "!"] +"你好世界" → ["你好", "世界"] +``` + +### 2. 位置编码（Positional Encoding） + +Transformer 本身**不感知顺序**，需要额外注入位置信息： + +| 类型 | 说明 | 代表 | +|---|---|---| +| **绝对位置编码** | 每个位置有唯一的向量 | 原始 Transformer 的正弦编码 | +| **相对位置编码** | 编码 token 之间的相对距离 | T5 的相对偏置 | +| **旋转位置编码（RoPE）** | 对 Q/K 做旋转，编码相对位置 | LLaMA、DeepSeek、Qwen 主流方案 | + +### 3. 前馈网络（Feed-Forward Network, FFN） + +``` +FFN(x) = Act(W₂ · Act(W₁ · x + b₁) + b₂) +``` + +- 两层全连接，中间有激活函数（GELU/SwiGLU） +- 参数量占比最大（约 2/3） +- SwiGLU 是现代模型的首选激活函数 +- 深度讲解见 [[FFN 前馈网络详解]] + +### 4. 残差连接（Residual Connection） + +``` +输出 = LayerNorm(x + SubLayer(x)) +``` + +- 缓解深层网络的梯度消失 +- 让模型可以"跳过"不必要的变化 +- GPT 使用 Pre-LN（层归一化在子层之前） + +### 5. 层归一化（Layer Normalization） + +- 对每个样本的特征维度做归一化 +- 稳定训练、加速收敛 +- 现代主流：**RMSNorm**（去掉均值中心化，更快更简单） + +--- + +## 四、训练范式 + +``` +┌──────────────┐ +│ 1. 预训练 │ 海量互联网文本，无监督学习（预测下一个 Token） +│ Pretraining │ 数十 TB 数据，数万 GPU，数周到数月 +└──────┬───────┘ + ▼ +┌──────────────┐ +│ 2. 监督微调 │ 高质量人工标注的指令-回复对 +│ SFT │ 让模型学会"对话格式"和"遵循指令" +└──────┬───────┘ + ▼ +┌──────────────┐ +│ 3. 对齐训练 │ RLHF / DPO / RLVR +│ Alignment │ 让模型输出更安全、更有用、更符合人类偏好 +└──────────────┘ +``` + +### 关键概念 + +| 概念 | 说明 | +|---|---| +| **Scaling Law** | 模型性能随参数量、数据量、计算量幂律增长 | +| **涌现能力** | 大模型独有的、小模型没有的能力（如思维链推理） | +| **RLHF** | 基于人类反馈的强化学习 — 训练奖励模型指导策略优化 | +| **DPO** | 直接偏好优化 — 更简单地对齐方法，不需要单独的奖励模型 | + +--- + +## 五、推理时技术 + +### 自回归生成（Autoregressive Generation） + +``` +输入："今天天气" → 预测下一个 Token "真" + "今天天气真" → 预测下一个 Token "好" + "今天天气真好" → 预测下一个 Token ""（停止） +``` + +每次生成一个 Token，直到输出结束符或达到最大长度。 + +### 关键概念 + +| 概念 | 说明 | +|---|---| +| **Temperature** | 控制随机性，越高越随机（创造性高），越低越确定 | +| **Top-p（核采样）** | 只从累积概率 ≥ p 的最少 Token 集合中采样 | +| **Top-k** | 只从概率最高的 k 个 Token 中采样 | +| **KV Cache** | 缓存已生成 Token 的 Key/Value，避免重复计算，加速推理 | +| **上下文窗口** | 模型能同时"看到"的最大 Token 数（现在主流 128K~1M） | + +--- + +## 六、当前主流架构扩展 + +### MoE（混合专家） + +> 详见 [[MoE 混合专家架构]] + +- 不是所有参数都参与每次推理 +- 每个 Token 只路由到少数"专家"子网络 +- 总参数极大但计算量可控 + +### 多模态扩展 + +> 见 [[多模态大模型]] + +- 在 LLM 基础上添加视觉/音频编码器 +- 通过投影器对齐不同模态的 Token + +--- + +## 扩展阅读 + +- 原始论文：[Attention Is All You Need (2017)](https://arxiv.org/abs/1706.03762) +- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) — 图文详解 Transformer + +--- + +*最后更新：2026-08-05* diff --git a/工作/Pasted image 20260402112914.png b/工作/Pasted image 20260402112914.png new file mode 100644 index 0000000000000000000000000000000000000000..de728f4f29f3971a67aacabc9480810488663a26 GIT binary patch literal 101046 zcmaI82{@E*`#x^Q#Mo*^_N~F#Wy`*geaS9MmMBR`ND;}**vn2)QOa5&C3}TPQCTBN zMG>+jAxZgNkCylS{(ry!_c-47sOB^C%=6s$b)DCFo!5C!qP3+l3nLFB4Gj&8sfmFt z4GsDk4Gk?T77ed_S=drcLxZC+H6YoCp=R^81i z^E@L|y3pE3Z{EB)PBDC-^ZIJjNpA=5aIc8)fDOsKPoMhi298_hH8#eqi)IrDSS*@K z-u%N-moUE3A@ZLW)b*NG-S8Qnx%FA;|K8Q0r)v^dQLL{n{QHgh>?ny69ZZbZ)KSYq 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zh^c(>gsI$s;flLYB!#~Gx$WSH+JNF$+L__psitJ-I8=AOFD{0D80VM)94)VU2yl?PUHbn!NL#2OHg;`PSr+)Bwm%CmXy=XUsIM$82j#vDiNDKzJEFLf zSAbd7!xJoccUWHz{hM6_HDq|d!eoRuxa5CwZ;V3t)YsON9icx<{*e^n83zHu*_ojr ijXnVZ!3F&Cbe@2rdCyS~wyg|aA<$6ORjE<33jZIp24_S7 literal 0 HcmV?d00001 diff --git a/工作/VCU控制逻辑_精简版.md b/工作/VCU控制逻辑_精简版.md new file mode 100644 index 0000000..9168b10 --- /dev/null +++ b/工作/VCU控制逻辑_精简版.md @@ -0,0 +1,67 @@ +# VCU控制逻辑（精简版） + +## 一、核心定位 +VCU是车辆核心控制单元，设计原则：**功能简单、运行稳定**，是整车控制的基石，负责协调各子系统工作。 + +## 二、对接子系统 +VCU通过CAN总线与以下部件通信： +- BMS（电池管理系统）、OBC（车载充电机） +- 电机控制器、灯光控制系统 +- ORIN（上层自动驾驶平台）、遥控器、机械臂控制器 + +--- + +## 三、核心控制逻辑 +### 1. 充电控制 +- 检测到OBC可充电后，优先等待ORIN指令，**5分钟超时自动充电** +- 电量充满后自动发送停止充电指令 +- 长时间停在充电区且无上层控制时，**电量低于25%自动充电**，充满自停 + +### 2. 驾驶模式控制 +| 状态 | 控制逻辑 | +|------|----------| +| 遥控器未开机 | 等待ORIN连接使能，进入自动驾驶模式 | +| 遥控器开机-手动模式 | 完全不响应ORIN指令，所有控制以遥控器为准 | +| 遥控器开机-自动模式 | ORIN可以接管车辆控制 | + +### 3. 机械臂控制 +- 通过遥控器E拨杆切换底盘/机械臂模式，**两种模式互斥** +- 机械臂手动模式：不响应ORIN机械臂指令，完全由遥控器控制 +- 遥控器操作完全兼容《履带车T10遥控器协议》，支持5个关节+滚刷控制 + +### 4. 高优先级刹车（最高优先级） +- 独立刹车摇杆，**全模式生效**，不受手自动模式限制 +- 拉动幅度对应刹车力度，线性控制减速度，最大不超过系统限值，避免急停 +- 刹车期间临时上报ORIN为手动模式，屏蔽上层速度指令，释放后自动恢复原模式 + +### 5. 电机控制 +- 支持单摇杆原地转向 +- 最大加速度/减速度可配置，避免急停 +- 遥控器速度突降时平滑减速，不直接切断动力 + +### 6. 灯光控制 +- 自动驾驶指示灯：自动驾驶模式点亮，手动模式熄灭 +- 转向灯：转向指令触发，回正自动关闭 +- 刹车灯：减速或驻车时点亮 +- 远近光灯：接收上层/遥控器指令控制 + +--- + +## 四、控制优先级（从高到低） +1. **高优先级刹车摇杆** > 2. 遥控器手动模式 > 3. ORIN自动驾驶 + +--- + +## 五、关键配置参数 +| 参数 | 默认值 | 说明 | +|------|--------|------| +| 充电等待超时 | 5分钟 | 检测到可充电后等待上层指令的最长时间 | +| 自动充电SOC阈值 | 25% | 低于该值自动启动充电 | +| 最大加速度 | 1m/s² | 正向加速度上限 | +| 最大减速度 | 1m/s² | 减速上限（刹车力度不超过该值） | +| CAN通信超时 | 500ms | 连续未收到报文判定为通信丢失 | + +--- + +## 六、核心安全机制 +1. 通信丢失保护：对应模式下减速停车，上报故障 diff --git a/工作/VCU控制逻辑（v0.2）.md b/工作/VCU控制逻辑（v0.2）.md new file mode 100644 index 0000000..80c57cf --- /dev/null +++ b/工作/VCU控制逻辑（v0.2）.md @@ -0,0 +1,234 @@ +# VCU（车辆控制单元）控制逻辑文档 + +## 1. 概述 +VCU（Vehicle Control Unit）是车辆的核心控制中心，作为整车控制的基石，设计原则为**功能简单、运行稳定**，负责协调车辆各子系统的工作。 + +### 1.1 系统架构 +VCU通过CAN总线与以下子系统直接通信： +- **BMS（电池管理系统）**：电池状态采集 +- **OBC（车载充电机）**：充电状态监测、充电指令控制 +- **电机控制器**：速度控制、扭矩输出控制 +- **灯光控制系统**：远近光灯、转向灯、刹车灯、自动驾驶指示灯控制 +- **ORIN（上层自动驾驶计算平台）**：接收自动驾驶指令、上报车辆状态 +- **遥控器**：接收手动驾驶指令、手自动模式切换 +- **机械臂控制器**：机械臂关节控制、作业工具控制 + +```mermaid +graph TD + RC --> VCU + ARM --> VCU + BMS--> VCU + VCU[VCU 控制中心] --> BMS[BMS 电池管理系统] + VCU --> OBC[OBC 车载充电机] + OBC --> VCU + ORIN --> VCU + VCU --> MOTOR[电机控制器] + VCU --> LIGHT[灯光控制系统] + VCU --> ORIN[ORIN 自动驾驶平台] + VCU --> RC[遥控器] + VCU --> ARM[机械臂控制器] + +``` + +## 2. 充电控制逻辑 +VCU负责整车充电流程的管理，通过CAN总线与BMS和OBC通信，收集两者状态并上报上层系统。 + +### 2.1 充电状态机 +```mermaid +stateDiagram-v2 + [*] --> OBC: 待充电状态 + OBC --> WAIT_FOR_CHARGE_CMD: 检测到OBC可充电 + ORIN --> CHARGING: 上层发送充电指令 + WAIT_FOR_CHARGE_CMD --> CHARGING: 收到上层充电指令 + WAIT_FOR_CHARGE_CMD --> CHARGING: 5分钟超时自动充电 + + OBC --> AUTO_CHARGE: 电量<25%且长时间停留在充电区域\n无上层控制 + AUTO_CHARGE --> CHARGING: 自动执行充电指令 + CHARGING --> AUTO_STOP_CHARGE: 电量充满 + AUTO_STOP_CHARGE --> OBC: 自动停止充电 +``` + +### 2.2 充电控制规则 +1. **充电触发条件**： + - 当VCU检测到无线OBC处于可充电状态时，进入等待充电指令状态 + - 优先等待上层（ORIN）发送充电开始指令 + - 若等待超时（5分钟），自动进入充电状态 + +2. **充电停止条件**： + - BMS上报电量充满后，VCU自动向OBC发送停止充电指令 + - 收到上层停止充电指令时，立即停止充电 + +3. **自动充电策略**： + - 车辆长时间停留在可充电区域且无上层控制指令时 + - 当电池电量低于25%时，自动执行充电指令 + - 电量充满后自动执行停止充电指令 + +## 3. 驾驶控制逻辑 +VCU支持两种驾驶模式：**自动驾驶模式**（上层ORIN控制）和**手动驾驶模式**（遥控器控制），遥控器控制优先级高于上层控制。 + +### 3.1 模式切换逻辑 +```mermaid +stateDiagram-v2 + [*] --> SBUS_WAIT: 遥控器未开机 + SBUS_WAIT --> AUTO_MODE: 上层ORIN连接并使能\n进入自动驾驶模式 + AUTO_MODE --> SBUS_CONNECTED: 遥控器开机 + SBUS_CONNECTED --> MANUAL_MODE: 遥控器手动模式\n不响应上层任何指令 + SBUS_CONNECTED --> AUTO_MODE: 遥控器自动模式\n上层可接管控制 +``` + +### 3.2 控制优先级规则 +1. **遥控器未开机时**： + - 等待上层ORIN连接并发送使能指令 + - 进入自动驾驶模式，响应ORIN的控制指令 + +2. **遥控器开机后**： + - 优先读取遥控器的手自动模式状态 + - **手动模式**：完全不响应上层ORIN的任何指令，所有控制以遥控器为准 + - **自动模式**：上层ORIN可以接管车辆控制 + +3. **高优先级刹车最高优先级**：遥控器独立刹车摇杆优先级高于所有其他控制指令，无论当前处于自动驾驶模式还是手动模式，只要拉动刹车摇杆，立即响应刹车指令，不受模式限制。 + +### 3.3 指令接收处理 +VCU通过CAN总线接收两类控制指令： + +#### 3.3.1 上层ORIN指令 +- 速度控制指令 +- 使能控制指令 +- 灯光控制指令（远近光灯） +- 驻车控制指令 + +#### 3.3.2 遥控器指令 +- 速度控制指令（单摇杆） +- 方向控制（单摇杆） +- 使能控制指令 +- 灯光控制指令 +- 手自动切换指令 +- 驻车控制指令 +- 模式切换指令 +- 机械臂各关节控制指令 +- 作业工具（滚刷）控制指令 +- 高优先级刹车控制指令 + +## 4. 电机控制逻辑 +### 4.1 遥控器单摇杆控制 +- 支持**原地转向**功能：摇杆左右方向控制转向，前后方向控制速度 +- 左右摇杆量直接对应转向角度，前后摇杆量对应目标速度 + +### 4.2 加减速限制 +为保证行驶平稳性，对电机输出进行如下限制： +1. **最大加速度限制**：加速度不超过预设最大值（可配置，默认0.5g） +2. **最大减速度限制**：减速度不超过预设最大值（可配置，默认0.3g） +3. **避免急停**：当遥控器速度指令突然变为0时，不直接切断动力输出，而是按照最大减速度平滑减速至停止 + +### 4.3 高优先级刹车控制 +遥控器配备独立高优先级刹车摇杆，为最高优先级控制输入，逻辑如下： +1. **全模式生效**：无论车辆当前处于自动驾驶模式还是手动驾驶模式，刹车摇杆指令均生效，不受当前控制模式限制 +2. **力度线性控制**：刹车摇杆拉动幅度对应刹车力度，幅度越大减速度越大，最大减速度不超过系统预设的最大减速度限值，实现缓慢刹车，避免急停 +3. **临时状态上报**：拉动刹车摇杆期间，VCU临时向上层ORIN上报车辆处于**手动模式**，同时屏蔽上层ORIN的速度控制指令，仅响应刹车指令 +4. **释放自动恢复**：刹车摇杆完全释放后，立即恢复原运行模式，向ORIN上报实际模式状态，恢复响应上层速度控制指令 +5. **不干涉其他控制**：刹车控制仅干预速度控制相关指令，不影响灯光、驻车、机械臂等其他控制功能 + +## 5. 灯光控制逻辑 +灯光控制暂时由VCU自主判断处理，无需上层系统干预。 + +### 5.1 控制列表 +| 灯光类型 | 控制逻辑 | +| -------- | -------------------- | +| 自动驾驶指示灯 | 自动驾驶模式开启时点亮，手动模式时熄灭 | +| 转向灯（左/右） | 根据转向指令自动开启，转向回正后自动关闭 | +| 刹车灯 | 车辆处于减速状态或驻车状态时点亮 | +| 远近光灯 | 接收上层或遥控器指令控制，默认不开启 | + +### + +## 6. 通信协议规范 +### 6.1 CAN通信矩阵（摘要） + +待补充 + +## 7. 故障处理与安全机制 +### 7.1 通信故障处理 +- 与BMS通信丢失：立即禁止充电，上报故障 +- 与OBC通信丢失：终止当前充电流程，上报故障 +- 与ORIN通信丢失：自动驾驶模式下减速停车，切换到待机状态 +- 与遥控器通信丢失：手动模式下减速停车，开启危险报警灯；机械臂手动模式下停止机械臂所有运动并锁定 + +### 7.2 安全保护机制 +待补充 +## 8. 配置参数（可调整） +| 参数名称 | 默认值 | 说明 | +| --------- | ----- | ------------------- | +| 充电指令等待超时 | 5分钟 | 检测到可充电后等待上层指令的最长时间 | +| 自动充电SOC阈值 | 25% | 低于该值时自动启动充电 | +| 最大加速度 | 1m/s² | 正向加速度上限 | +| 最大减速度 | 1m/s² | 反向加速度（减速）上限 | +| CAN通信超时阈值 | 500ms | 连续500ms未收到报文判定为通信丢失 | + +## 9. 机械臂控制逻辑 +VCU支持机械臂手动控制功能，通过遥控器切换操作模式，实现底盘控制与机械臂控制的复用，遵循**手动优先**的控制原则。 + +### 9.1 模式切换逻辑 +机械臂控制与底盘控制通过遥控器E拨杆进行切换，完全兼容《履带车T10遥控器协议与操作说明》： + +```mermaid +stateDiagram-v2 + [*] --> CHASSIS_MODE: E拨杆最左侧\n底盘控制模式 + CHASSIS_MODE --> ARM_MODE: E拨杆拨到最右侧\n切换到机械臂控制模式 + ARM_MODE --> CHASSIS_MODE: E拨杆拨到最左侧\n切换到底盘控制模式 + + state CHASSIS_MODE { + [*] --> CHASSIS_MANUAL: 手动模式 + [*] --> CHASSIS_AUTO: 自动模式 + } + + state ARM_MODE { + [*] --> ARM_MANUAL: 手动模式（默认） + ARM_MANUAL --> ARM_AUTO: A按键切换到自动模式\n上层可控制机械臂 + ARM_AUTO --> ARM_MANUAL: A按键切换到手动模式\n不响应上层指令 + } +``` + +### 9.2 控制优先级规则 +1. **手动模式优先**：无论机械臂处于何种状态，只要遥控器切换到机械臂手动控制模式，立即停止响应上层ORIN的机械臂控制指令 +2. **模式互斥**：E拨杆同一时间只能处于一种模式，底盘控制与机械臂控制不可同时操作 +3. **自动模式使能**：机械臂自动模式下，上层ORIN可以发送机械臂控制指令，VCU转发给机械臂控制器 + +### 9.3 遥控器机械臂操作映射（E拨杆最右侧） +完全遵循《履带车T10遥控器协议与操作说明》定义的操作逻辑： + +| 控制部件 | 功能 | 通道映射 | +|---------------|--------------------------|----------| +| 右摇杆X轴（x2） | 控制回转关节（joint1） | CH1 | +| 右摇杆Y轴（y2） | 控制动臂关节（joint4） | CH2 | +| 左摇杆Y轴（y1） | 控制横梁关节（joint3） | CH3 | +| 左摇杆X轴（x1） | 控制力臂关节（joint2） | CH4 | +| A按键 | 切换机械臂自动/手动控制模式 | CH7 | +| F拨杆 | 控制刷头上下移动（joint5） | CH6 | +| D轮盘 | 控制滚刷启停/转速 | CH10 | + +### 9.4 机械臂控制处理流程 +1. VCU实时接收遥控器CAN报文（ID范围：0x187~0x18A） +2. 解析E拨杆（CH5）数值判断当前操作模式 +3. **机械臂模式下**： + - 解析各通道摇杆量，转换为对应关节的目标角度/速度 + - 将控制指令通过CAN总线发送给机械臂控制器 + - 手动模式下直接转发遥控器指令，自动模式下转发上层ORIN指令 +4. **底盘模式下**： + - 忽略机械臂相关通道数据，保持机械臂当前状态 + - 执行底盘控制逻辑 + +### 9.5 安全保护机制 +1. **模式切换保护**：在机械臂运行过程中切换到底盘模式时，立即停止机械臂所有运动，保持当前位置 +2. **通信丢失保护**：遥控器通信丢失时，立即停止机械臂所有运动，进入锁定状态 +3. **急停保护**：收到急停指令时，立即切断机械臂动力输出 + +## 10. 通信协议补充 +### 10.1 遥控器CAN通信扩展 +| CAN ID | 发送方 | 接收方 | 报文内容 | 周期 | +|--------|--------|--------|----------|------| +| 0x187 | 遥控器 | VCU | 通道1-4数据（摇杆） | 10ms | +| 0x188 | 遥控器 | VCU | 通道5-8数据（拨杆、按键） | 10ms | +| 0x189 | 遥控器 | VCU | 通道9-10数据（轮盘） | 10ms | +| 0x18A | 遥控器 | VCU | 保留 | 10ms | +| 0x18B | 遥控器 | VCU | 校验帧 | 10ms | + diff --git a/工作/nav2 优缺点.md b/工作/nav2 优缺点.md new file mode 100644 index 0000000..a01b773 --- /dev/null +++ b/工作/nav2 优缺点.md @@ -0,0 +1,6 @@ +控制相关参数没有按速度区间机制，需要自己实现 +rtk精度行走是够的，但是目前看会有5-8cm的跳变，所以充电还是需要用docking server配合视觉或者雷达精确定位，加上限位和触点开关之类的。 + +体量小，开发快，资料够多。 + +route server支持大地图 \ No newline at end of file diff --git a/工作/履带车T10遥控器协议与操作说明.md b/工作/履带车T10遥控器协议与操作说明.md new file mode 100644 index 0000000..738740e --- /dev/null +++ b/工作/履带车T10遥控器协议与操作说明.md @@ -0,0 +1,75 @@ + +## 1. 硬件概述 +本文档描述云卓T10遥控器通过SBUS转CAN模块输出的CAN通信协议及操作逻辑。遥控器布局参考下图： + +![[Pasted image 20260402112914.png|350]] +![[Pasted image 20260402112951.png|369]] +--- + +## 2. CAN通信协议规范 +### 2.1 基础参数 +- CAN ID范围：`0x187` ~ `0x18A`（共4个ID，每个ID对应4个通道数据） +- 比特率：500K +- 数据长度：每帧8字节，包含4个通道值，每个通道占2字节 +- 字节序：大端模式（第一字节为高位字节） +- 通道值范围： + - 最小值：`0x011A` (十进制 282) + - 中间值：`0x03EA` (十进制 1002) + - 最大值：`0x06BA` (十进制 1722) +- 开关量特性：A/B按键为开关量，开关打开时输出最大值，关闭时输出最小值 + +### 2.2 帧结构 + +| CAN ID | 0-1 | 2-3 | 4-5 | 6-7 | +| ------ | --- | ---- | --- | --- | +| 0x187 | CH1 | CH2 | CH3 | CH4 | +| 0x188 | CH5 | CH6 | CH7 | CH8 | +| 0x189 | CH9 | CH10 | 保留 | 保留 | +| 0x18A | 保留 | 保留 | 保留 | 保留 | +| 0x18B | 校验 | | | | +18B高位为数字通道和状态位，低位为前33位累加和校验。 + +## 3. 遥控器通道映射表 +| 控制部件 | 通道编号 | 说明 | +| --------- | ---- | --------- | +| 右摇杆X轴（x2） | CH1 | 横向数值，左小右大 | +| 右摇杆Y轴（y2） | CH2 | 纵向数值，上小下大 | +| 左摇杆Y轴（y1） | CH3 | 纵向数值，下小上大 | +| 左摇杆X轴（x1） | CH4 | 横向数值，左小右大 | +| E拨杆 | CH5 | 三档，左小右大 | +| F拨杆 | CH6 | 三档，左小右大 | +| A按键 | CH7 | 开关量 | +| B按键 | CH8 | 开关量 | +| C轮盘 | CH9 | 左小右大 | +| D轮盘 | CH10 | 左小右大 | + +--- + +## 4. 操作模式说明 +遥控器由E拨杆位置切换两种工作模式： + +### 4.1 底盘控制模式（E拨杆拨到最左侧，通道5输出最小值） +| 控制部件 | 功能 | +| --------- | ----------- | +| 左摇杆X轴（x1） | 控制底盘左右转向 | +| 左摇杆Y轴（y1） | 控制底盘前后移动 | +| A按键 | 切换自动/手动控制模式 | +| B按键 | 切换驻车状态 | +| F拨杆 | 切换前灯远近光 | + +### 4.2 机械臂控制模式（E拨杆拨到最右侧，通道5输出最大值） +| 控制部件 | 功能 | +| --------- | ---------------- | +| 右摇杆X轴（x2） | 控制回转关节（joint1） | +| 右摇杆Y轴（y2） | 控制动臂关节（joint4） | +| 左摇杆Y轴（y1） | 控制横梁关节（joint3） | +| 左摇杆X轴（x1） | 控制力臂关节（joint2） | +| A按键 | 切换自动/手动控制模式 | +| F拨杆 | 控制刷头上下移动（joint5） | +| D轮盘 | 控制滚刷启停/转速 | + +--- + +## 5. 版本信息 +- 文档版本：V1.0 +- 最后更新：2026-04-02 diff --git a/工作/履带车T10遥控器协议与操作说明.pdf b/工作/履带车T10遥控器协议与操作说明.pdf new file mode 100644 index 0000000000000000000000000000000000000000..be4f4ed9c6371828f991e3537c463f0e1ae03fda GIT binary patch literal 610318 zcmc$`2{@K-+dV2xLZM_xA{8q0oMcWarKnWqnT(mIPzXhmgiMtp$&_RsDr2UEkTLU+ zS>|D{yMFKce*4(R{*L|s{(B$0;~nbpc%J*duJc^yTI)Qo`_Kh>1#W&GA@V~Fqr*SR z`C0i`t@Lh?OGxluGPN@|WR*}jsh}V%Cn79-Qb0sZK}3X)PgsbLPmmw~)y!0v7gxe+!7n6$pZs~ZsgLX>5rv30%gp66IMtOnDh~sBfxN;mTsqRi&r>}f6*&9Vx86AvTwto^^-|}nL 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zJPXJS&joC0*wzKfVZbp8eH&mH1nmKHzLD-^Xak;Q_x0I5-ky#U}iLY7E_<$(Da zguxKJ22>u5EkGy`0CI??(jb8Y-mB8Uw*>bC$PvbgAaCyHA&g(`OJFP@VvC^dXXU2N z{eaFxjDbb?>VdNf;j0I22rLTF5K=D?q!IQ92{pk!b6|-BX$R2-KzRh-Aer4T|AD*_ zgx&}E4*B;y_$gOtVy(*R&S*e?nkU)W#ey_<{0Ir}p%v~qHiy63z=F 2026-08-05 分析笔记。源自一个判断：大模型时代模型结构收敛、数量变少，传统 AI 编译器必要性下降，"只需适配特定子网络深入优化即可"——这个判断对一半。 + +--- + +## 背景：原判断 + +作者背景：做过编译器工作，TVM 0.6 魔改 + 接触过 MLIR。 + +原判断： +- 大模型时代，模型结构越来越固定（decoder-only Transformer 一统） +- 模型数量远少于传统 CNN 时代 +- 传统 AI 编译器（通用图级自动优化）必要性下降 +- 只需适配特定子网络，深入优化即可 + +--- + +## 一、判断对的部分：通用图编译器退潮 + +在**图级通用编译**这一层，判断成立，有实证： + +### OctoML 案例（最重要的证据） + +- NVIDIA 2024年5月收购 OctoML（TVM 团队），官方理由是"拿到能把固定硅片上最后几个点性能挤出来的人" +- **结果相反**：编译流水线产品被逐步废弃，客户流向 vLLM 等 serving-first 引擎 +- **结论：编译优先输给了服务优先**——生产 LLM 系统真正花时间的是低 TTFT、持续吞吐、API 契约，而不是"把模型编译成最优 artifact" + +### AOT → JIT 的转移 + +| | AOT（传统编译器） | JIT（2026 主流） | +|---|---|---| +| 模式 | 模型进 → 优化 passes → 编译产物出 | 运行时按需编译 kernel，首次启动缓存 | +| 代表 | TVM、TensorRT-LLM | FlashInfer、DeepGEMM、Triton | +| 部署速度 | TensorRT-LLM 构建 engine 需数小时 | vLLM 加载 HF 模型只需几分钟 | + +### CNN vs LLM 的模型量 + +- CNN 时代：几百上千种网络结构 → 通用图编译"一套处理任意图"有价值 +- LLM 时代：decoder-only 一统，结构高度收敛 → 为"任意图"做的通用优化确实浪费 + +--- + +## 二、判断的偏差："AI 编译器"分裂成了四个层 + +> 核心偏差：把"AI 编译器"等同于 TVM 0.6 式的通用图编译器。 +> 实际 2026 年编译器工作量没有变少，而是转移到四个更深更专的层。 + +### 层 1：Kernel 层——最热战场 + +LLM 时代性能关键算子的集合**比 CNN 时代更丰富**： + +| 算子/技术 | 为什么难 | +|---|---| +| FlashAttention / MLA | 每次新注意力变体 = 新 kernel 的数月专家工作 | +| MoE 路由 + GEMM | 专家调度、跨卡通信；vLLM 的 MoE backend 从 FlashInfer 切到 Triton（H200 更快） | +| FP8 / FP4 / NVFP4 量化 GEMM | 新数值格式把旧 cost model 和调度规则**全部作废** | +| 长上下文 / PagedAttention | 变长序列、内存受限的 kernel 调度 | + +当前 Kernel 层的生态谱系（从高层 DSL 到底层）： + +``` +Triton（事实标准，高层 tile 抽象） + ↓ +TileLang / Helion（布局自动推断） + ↓ +CuTe DSL（CUTLASS v4，可组合布局抽象） + ↓ +TIRx / TLX（硬件原生，显式 orchestration） + ↓ +CUDA / PTX（手工极限优化） +``` + +### 层 2：推理引擎层——"编译器/运行时边界模糊" + +**LLM 推理编译已被定义为独立学科**：传统编译器五层框架（图→kernel→内存→运行时调度）被在线调度吸收。 + +- 动态形状（变长序列、KV cache） +- chunked prefill、continuous batching +- 这些调度问题**本质上变成了编译问题** + +代表：vLLM、SGLang、TensorRT-LLM、llama.cpp。 + +2026 实战数据： +- vLLM V1 引擎比 V0 快 1.5~2×（异步调度、chunked prefill、torch.compile 集成） +- vLLM 默认 MoE backend 从 FlashInfer 切到 Triton：H200 上 57,821 vs 55,700 tokens/s +- TensorRT-LLM 高并发（32+ 并发）时仍快 10~30%，但需数小时 build engine + +### 层 3：硬件使能层——传统编译器反而吃香 + +反转点：**通用模型少，但硬件爆发式碎片化**。 + +- NVIDIA Blackwell、AMD MI300X、华为昇腾、寒武纪、国产 ASIC +- 非 NVIDIA 芯片没有厂商 kernel 库 → **编译栈是必需的** +- 国内芯片编译团队从"建设期"进入"换血期"：要求 codegen 优化、内存规划、target-specific 调度的深度人才 +- TVM/MLIR"一套栈、多后端"思路在这一层真正兑现价值 + +### 层 4：LLM 生成 kernel——角色重塑 + +2025-2026 最大变量：**LLM agent 成为 kernel 生成器和编译器优化器**。 + +| 系统 | 结果 | +|---|---| +| **KernelEvolve**（Meta，已部署） | 480 个算子-平台组合 100% 正确率，1.25~17× 加速，开发时间从数周降到数小时 | +| **KernelBench** | LLM kernel 能力基准 | +| **AutoTriton / TritonRL / KernelLLM** | RL 驱动的 Triton 生成 | +| **Compiler-R1 / REASONING COMPILER**（NeurIPS 2025） | RL 驱动的编译自动调优 | + +> 意义：未来编译器工程师核心技能从"手写 kernel"变为"**引导 agent 搜索 + 验证正确性 + 设计搜索空间**"。 + +--- + +## 三、反驳"只需适配特定子网络" + +策略对，深度被低估： + +- **"只优化少量关键算子"是对的**——这正是 vLLM/TensorRT-LLM 的做法 +- 但"只需"严重低估深度：把 FlashAttention-4 或 MoE kernel 在 B200 调到峰值是**数周专家工作** +- **动态形状是关键分水岭**：静态形状下融合是"规则问题"，动态形状下是"开放问题"（广播开销、索引计算、codegen 分支）。2026 人才市场把"动态形状编译"列为 LLM 推理团队硬性要求 +- 值钱的是**精通动态形状 pass 设计、memory-bound 优化、新数值格式**的人，不是"会写 dialect 定义和基本 lowering"的人 + +MLIR 生态（2026 年已是"编译器基础设施的 Linux"）： +- torch.compile (Inductor) 是 MLIR 思想设计 +- Google XLA 已迁移到 MLIR 底层 +- IREE 是纯 MLIR 栈 +- 需求已从"懂 dialect 机制"转向"**自定义 pass 流水线设计、图融合策略、跨 IR 优化**" + +--- + +## 四、前景判断（2026） + +``` +通用图编译器（TVM 全自动调优式） ↘ 退潮，仅存于特定 niche +Kernel DSL（Triton/CuTe/TileLang） ↗ 繁荣，LLM 推理事实标准 +推理引擎内部编译（vLLM/SGLang） ↗ 最热，编译器×分布式交叉岗位稀缺 +硬件使能编译栈（昇腾/AMD/寒武纪） ↗ 确定性增长，国产芯片刚需 +LLM agent 生成 kernel ↗ 爆发前夜，重塑工程师角色 +``` + +### 关键结论 + +> **AI 编译器没有死，死的是"通用图编译"这个窄定义。** + +- AI 推理支出 2026 年已超过训练 +- 编译器工程师是 AI 基础设施链上最稀缺岗位之一 +- 能力栈从"覆盖任意图"转向"**少量关键算子做到极致的深度 + 动态形状 + 新硬件 + 新数值格式**" + +--- + +## 五、职业建议（针对本笔记作者背景） + +作者技能：TVM 0.6（Relay/TIR/AutoTVM 时代）+ MLIR。 + +| 技能 | 2026 年变现路径 | +|---|---| +| MLIR 了解 | 深入 torch.compile/Inductor 的 pass 设计、动态形状融合 | +| TVM 调度/调优思想 | 转向 Triton/CUTLASS——AutoTVM/Ansor 的调优心智模型可迁移 | +| 编译原理底子（IR/pass/调度） | vLLM/SGLang 内部，编译器×分布式调度是稀缺交叉 | +| 多后端思维 | 国产芯片（昇腾 CANN、寒武纪）使能，"适配子网络"直觉在此是核心竞争力 | + +**一句话建议**： +> 不守"通用编译框架"这条船；把"适配特定子网络深入优化"做深到极致——但"深"是指**动态形状 + 新数值格式 + 新硬件架构**三个维度，而不是 TVM 0.6 时代的静态图自动调优。编译功底在这三个维度上直接可迁移，是"只会写 PyTorch 的 ML 工程师"够不着的护城河。 + +--- + +## 参考资料 + +- [LLM Inference Compiler Panorama（推理编译独立成领域）](https://arxiv.org/pdf/2512.23236v1) +- [TIRx：TVM 上的新 kernel DSL（2026-06）](https://tvm.apache.org/2026/06/22/tirx) +- [vLLM 切换 Triton MoE backend（PR #44220）](https://github.com/vllm-project/vllm/pull/44220) +- [AI Inference Engines 2026 全景](https://www.youngju.dev/blog/culture/2026-05-16-ai-inference-engines-2026-vllm-sglang-llama-cpp-tgi-tensorrt-llm-mlx-mistralrs-deepspeed-aphrodite-deep-dive.en) +- [KernelEvolve：Meta 生产级 LLM kernel 生成](https://www.ivoox.com/llm-inference-compiler-panorama-research-and-engineering-evolution-audios-mp3_rf_176267639_1.html) + +## 相关链接 + +- [[大模型架构基础]] — 编译的对象：Transformer 结构 +- [[DeepSeek V4 系列]] — 最新模型的 FP8/NVFP4、DSpark 都是 kernel 层优化 +- [[FFN 前馈网络详解]] — MoE/SwiGLU 是最热的 kernel 优化对象 + +--- + +*最后更新：2026-08-05* diff --git a/自建服务/Gitea部署指南.md b/自建服务/Gitea部署指南.md new file mode 100644 index 0000000..5ed5c34 --- /dev/null +++ b/自建服务/Gitea部署指南.md @@ -0,0 +1,104 @@ +# Gitea 部署指南 + +> 2026-08-13 记录。自托管轻量 Git 服务，个人代码仓库，Web 用 Caddy 反代套 HTTPS。域名域名 `git.hyonglin.top`。 + +--- + +## 选型 + +| 方案 | 是否选用 | 原因 | +|---|---|---| +| **Gitea** | ✅ | Go 单二进制，轻量（~50–100MB），功能全（仓库/Issue/PR/Actions/Package），社区活跃、安全更新及时 | +| Gogs | ❌ | Gitea 的 fork 前身，开发停滞，新项目不推荐 | +| 裸仓库+SSH | ❌ | 无 Web 界面 | + +> Gitea 是 Gogs 的 fork（社区驱动），新项目无脑选 Gitea；若更在意「纯社区治理」可选 Forgejo。 + +--- + +## docker-compose 服务 + +`silverbullet/docker-compose.yml`： + +```yaml + gitea: + image: gitea/gitea:latest + container_name: gitea + restart: unless-stopped + environment: + - USER_UID=1000 + - USER_GID=1000 + - GITEA__server__ROOT_URL=https://git.hyonglin.top/ + - GITEA__server__DOMAIN=git.hyonglin.top + - GITEA__server__SSH_DOMAIN=git.hyonglin.top + - GITEA__server__SSH_PORT=2222 + ports: + - "2222:22" # SSH clone 用（服务器 22 已被 SSH 占用，故映射 2222） + volumes: + - ./gitea:/data + - /etc/timezone:/etc/timezone:ro + - /etc/localtime:/etc/localtime:ro +``` + +- Web（3000）不映射端口，由 Caddy 反代 `gitea:3000` +- SSH 需映射 `2222:22`，因为宿主机 22 被 OpenSSH 占用 + +## Caddy 反代 + +`silverbullet/Caddyfile` 加一段： + +```caddyfile +# Git 服务 +git.hyonglin.top { + tls { + dns cloudflare {env.CF_API_TOKEN} + } + reverse_proxy gitea:3000 +} +``` + +## compose 部署 + +```bash +docker compose up -d gitea +docker compose up -d --force-recreate caddy # 让 caddy 加载新站点 +``` + +--- + +## 首次安装（web 向导） + +打开 `https://git.hyonglin.top` 走安装向导： + +- **数据库**：默认 `SQLite3`（个人用足够） +- **域名/URL**：环境变量已预设（`git.hyonglin.top`），保持默认 +- **SSH 端口**：`2222` +- **管理员账号**：设置用户名/邮箱/密码 +- 点「安装 Gitea」 + +验证安装完成： + +```bash +# app.ini 应已锁定安装 +grep INSTALL_LOCK /home/ubuntu/silverbullet/gitea/gitea/conf/app.ini # → true + +# 确认管理员存在 +docker compose exec -T gitea su git -c "gitea admin user list" +``` + +--- + +## 使用 + +- **Web**：登录 `https://git.hyonglin.top` 建仓库 +- **HTTPS clone**：`git clone https://git.hyonglin.top/用户名/仓库.git` +- **SSH clone**：`git clone git@git.hyonglin.top:2222/用户名/仓库.git`（需安全组放行 TCP 2222） +- **备份**：备份 `silverbullet/gitea` 目录（含数据库和所有仓库） + +--- + +## 相关链接 + +- [[主页与Caddy反向代理]] — git.hyonglin.top 的 HTTPS 入口 +- [[frp内网穿透部署指南]] — 本服务器其他自建服务 +- [Gitea 官方文档](https://docs.gitea.com/zh-cn/) \ No newline at end of file diff --git a/自建服务/Silverbullet部署指南.md b/自建服务/Silverbullet部署指南.md new file mode 100644 index 0000000..fd16f0e --- /dev/null +++ b/自建服务/Silverbullet部署指南.md @@ -0,0 +1,219 @@ +# Silverbullet 部署指南 + +> 2026-08-05 记录。目标：在远程 Linux 服务器上用 Docker 部署自托管笔记服务，实现"随时随地在任意设备用浏览器查看编辑笔记"。 + +--- + +## 背景与选型 + +**需求**：远程服务器部署，随时随地任意设备查看、编辑笔记。 + +**选型结论**：Silverbullet（浏览器直接编辑 + Linux/Docker）。 + +### 为什么选 Silverbullet + +- 任何设备浏览器打开网址即可**查看 + 编辑**，无需安装 App +- 支持 `[[]]` 双链、反向链接（**与 Obsidian 语法兼容**，现有笔记可直接用） +- 笔记是纯 Markdown 文件，存在服务器目录，备份 = 复制文件夹 +- PWA 支持，手机浏览器可离线编辑 +- Docker 一条命令部署，开源 + +### 备选：code-server（VS Code Web） + +如果更想要完整 VS Code 体验，用 codercom/code-server。缺点：移动端体验差、无双链支持。**先用 Silverbullet，不够再补。** + +--- + +## 一、服务器部署 + +### 创建 docker-compose.yml + +```yaml +services: + silverbullet: + image: ghcr.io/silverbulletmd/silverbullet:latest + container_name: silverbullet + restart: unless-stopped + ports: + - "3000:3000" + volumes: + - ./space:/space # 笔记存储目录，备份就是复制这个文件夹 + environment: + - SB_USER=admin:改成你的强密码 # 登录认证，必填！ +``` + +### 启动 + +```bash +docker compose up -d +``` + +访问 `http://服务器IP:3000` → 输入 admin/密码 → 完成。 + +### 常用环境变量 + +| 变量 | 说明 | 默认 | +|---|---|---| +| `SB_FOLDER` | Markdown 文件目录 | `/space` | +| `SB_PORT` | 内部端口 | `3000` | +| `SB_USER` | Basic auth（`user:pass`） | 无认证 | +| `SB_AUTH_TOKEN` | Bearer token API 访问 | 无 | +| `SB_READ_ONLY` | 只读模式 | `0` | + +--- + +## 二、迁移现有笔记 + +本机笔记在 `d:\onedrive\note`（Windows），用 Git Bash 一次性传到服务器： + +```bash +# 服务器上先建目录 +ssh user@server "mkdir -p /path/to/space" + +# rsync 上传（排除 Obsidian 配置目录，Silverbullet 用不上） +rsync -avP --exclude ".obsidian" "/d/onedrive/note/" user@server:/path/to/space/ +``` + +--- + +## 三、HTTPS + 域名（强烈建议） + +裸 HTTP 下密码明文传输，不安全。用 Caddy 做反向代理 + 自动 TLS 证书（Let's Encrypt）。 + +### 前提条件 + +- 一个已备案/可用的域名，如 `notes.yourdomain.com` +- 域名 A 记录已指向服务器公网 IP +- 服务器 80 和 443 端口未被占用（云服务器需在安全组放行这两个端口） + +### 整合后的 docker-compose.yml + +将 Caddy 和 Silverbullet 放在同一个 compose 文件中，让它们共享同一个 Docker 网络，容器间用服务名互相访问： + +```yaml +services: + silverbullet: + image: ghcr.io/silverbulletmd/silverbullet:latest + container_name: silverbullet + restart: unless-stopped + volumes: + - ./space:/space + environment: + - SB_USER=admin:改成你的强密码 + # 不暴露 ports，只允许 Caddy 通过内部网络访问 + + caddy: + image: caddy:latest + container_name: caddy + restart: unless-stopped + ports: + - "80:80" + - "443:443" + volumes: + - ./Caddyfile:/etc/caddy/Caddyfile + - caddy_data:/data # 证书和密钥持久化，避免重启重新申请 + - caddy_config:/config # Caddy 自身配置 + +volumes: + caddy_data: + caddy_config: +``` + +> **关键**：`caddy_data` 和 `caddy_config` 用 named volume 而非 bind mount，避免权限问题，且证书重启不丢失。 + +### Caddyfile 配置 + +在 `docker-compose.yml` 同级目录创建 `Caddyfile`（纯文本，无后缀）： + +```caddyfile +# 基础版：自动 HTTPS + 反向代理 +notes.yourdomain.com { + reverse_proxy silverbullet:3000 +} +``` + +**常用增强配置**： + +```caddyfile +notes.yourdomain.com { + # 反向代理 + reverse_proxy silverbullet:3000 + + # 访问日志（排查问题时打开） + log { + output file /var/log/caddy/access.log + } + + # 限制请求体大小（上传大文件时调大） + request_body { + max_size 50MB + } + + # 安全头（可选） + header { + X-Content-Type-Options nosniff + X-Frame-Options DENY + -Server + } +} +``` + +### 启动与验证 + +```bash +# 启动 +docker compose up -d + +# 查看 Caddy 日志（确认证书申请是否成功） +docker compose logs caddy -f +``` + +正常日志中应能看到类似 `certificate obtained successfully` 的输出。然后浏览器访问 `https://notes.yourdomain.com` 即可。 + +### 排错 + +| 问题 | 检查项 | +|---|---| +| 证书申请失败 | 确认域名 A 记录已生效（`dig notes.yourdomain.com` 或 `nslookup`），且 80 端口能从公网访问 | +| 502 Bad Gateway | `docker compose ps` 确认 silverbullet 容器在运行；确认 Caddyfile 中 `reverse_proxy` 的目标容器名与 compose 中 service 名一致 | +| 端口被占用 | 如果服务器已有 nginx/apache，先停掉再启 Caddy，或让 Caddy 监听其他端口 | +| 国内服务器 | 80/443 端口需备案；若无备案可用非标端口 + DNS 验证，但 Caddy 默认 HTTP-01 验证仍需 80 端口可达 | + +> 无域名临时方案：frp / 路由侠等内网穿透工具把 3000 端口暴露出去。 + +--- + +## 四、数据备份 + +笔记是纯 Markdown → 备份 = 压缩目录： + +```bash +# 手动备份 +tar czf notes-backup-$(date +%F).tar.gz /path/to/space/ + +# cron 定时备份 + 保留最近 7 份 +0 3 * * * tar czf /backup/notes-$(date +%F).tar.gz /path/to/space/ && ls -t /backup/notes-*.tar.gz | tail -n +8 | xargs rm -f +``` + +--- + +## 五、使用方式转变（关键） + +选浏览器编辑 → **服务器目录成为唯一权威版本**，本地 OneDrive 降级为离线备份： + +| 场景 | 操作 | +|---|---| +| 日常编辑 | 浏览器打开 Silverbullet，直接改服务器文件 | +| 本地离线备份 | `rsync user@server:/path/to/space/ /d/onedrive/note/` | +| 多设备 | 全部走浏览器，无需装任何东西 | + +--- + +## 相关链接 + +- [[大模型架构基础]] — 本笔记库的正式学习内容 +- [[AI编译器现状与前景]] — 技术趋势分析 + +--- + +*最后更新：2026-08-05* diff --git a/自建服务/frp内网穿透部署指南.md b/自建服务/frp内网穿透部署指南.md new file mode 100644 index 0000000..396d780 --- /dev/null +++ b/自建服务/frp内网穿透部署指南.md @@ -0,0 +1,218 @@ +# frp 内网穿透部署指南 + +> 2026-08-13 更新。目标：通过云服务器（公网 IP `58.87.87.208`）随时随地访问家里多台机器的 SSH 和 RDP，统一用域名 `hyonglin.top` + 端口区分。 + +--- + +## 背景与方案 + +**需求**：家里机器在 NAT 内网、没有公网 IP，想在任意地点 SSH 进去、远程桌面操作。 + +**选型结论**：frp（Fast Reverse Proxy），成熟的内网穿透工具。 + +- 家里机器（内网）主动反向连到云服务器，云服务器监听公网端口转发进来 +- 一个服务端（frps）对多个客户端（frpc），加机器只需加端口，服务端不用动 +- Go 编写，单文件二进制，Linux/Windows 通用，systemd 管理开机自启 + +### 架构 + +``` +你(任意地点) 云服务器 frps 家里机器 frpc（可多台） + │ │ │ + ├─SSH hyonglin.top:2201──▶│─监听 2201───────────▶│─▶ pc01:22 + ├─SSH hyonglin.top:2202──▶│─监听 2202───────────▶│─▶ pc02:22 + ├─RDP hyonglin.top:3389──▶│─监听 3389───────────▶│─▶ pc01:3389 + ├─RDP hyonglin.top:3390──▶│─监听 3390───────────▶│─▶ pc02:3389 + │ └─frpc 主动连 bindPort 7000◀─┘ +``` + +> 域名只解析到 IP，**端口才是区分机器的关键**。RDP 不能共用 3389（TCP 层没有域名概念），但 frp 的 `remotePort` 独立于本机 `localPort`，本机 RDP 端口保持 3389 不用改。 + +--- + +## 端口规划 + +| 机器 | SSH（remotePort） | RDP（remotePort） | 本机端口 | +|---|---|---|---| +| pc01 | 2201 | 3389 | 22 / 3389 | +| pc02 | 2202 | 3390 | 22 / 3389 | +| 新增机器 | 2203 | 3391 | … | + +固定端口：frps 客户端连接 `7000`、管理面板 `7500`。 + +--- + +## 一、服务端 frps 部署（云服务器） + +### 下载与安装 + +```bash +# 下载（GitHub 慢时走镜像 ghfast.top） +wget https://ghfast.top/https://github.com/fatedier/frp/releases/download/v0.65.0/frp_0.65.0_linux_amd64.tar.gz +tar xzf frp_0.65.0_linux_amd64.tar.gz + +sudo cp frp_0.65.0_linux_amd64/frps /usr/local/bin/ +sudo mkdir -p /etc/frp +``` + +### 服务端配置 `/etc/frp/frps.toml` + +```toml +bindPort = 7000 # frpc 连接端口 +auth.token = "0ae2664b411c41645e5c9ab6779724d4" # 客户端认证令牌（frpc 必须一致） + +# 管理面板 +webServer.addr = "0.0.0.0" +webServer.port = 7500 +webServer.user = "admin" +webServer.password = "Admin-0ae2664b" + +# 日志 +log.to = "/var/log/frps.log" +log.level = "info" +log.maxDays = 7 +``` + +### systemd 服务 `/etc/systemd/system/frps.service` + +```ini +[Unit] +Description=frp server +After=network.target + +[Service] +Type=simple +ExecStart=/usr/local/bin/frps -c /etc/frp/frps.toml +Restart=on-failure +RestartSec=5 + +[Install] +WantedBy=multi-user.target +``` + +```bash +sudo systemctl daemon-reload +sudo systemctl enable --now frps +systemctl status frps # active (running) +``` + +--- + +## 二、客户端 frpc 部署（家里机器） + +### 客户端配置 `/etc/frp/frpc.toml` + +以 pc01 为例： + +```toml +serverAddr = "hyonglin.top" +serverPort = 7000 +auth.token = "0ae2664b411c41645e5c9ab6779724d4" + +# SSH：服务器 2201 → 本机 22 +[[proxies]] +name = "pc01-ssh" +type = "tcp" +localIP = "127.0.0.1" +localPort = 22 +remotePort = 2201 + +# RDP：服务器 3389 → 本机 3389 +[[proxies]] +name = "pc01-rdp" +type = "tcp" +localIP = "127.0.0.1" +localPort = 3389 +remotePort = 3389 +``` + +> 加第二台（pc02）：把 `name` 换成 `pc02-*`、`remotePort` 换成 `2202`/`3390`，`serverAddr`/`token` 不变即可。 + +### 安装（frpc 二进制 + systemd） + +```bash +sudo install -m 755 ~/frpc /usr/local/bin/frpc +sudo mkdir -p /etc/frp +sudo cp ~/frpc.toml /etc/frp/frpc.toml +sudo cp ~/frpc.service /etc/systemd/system/frpc.service +sudo systemctl daemon-reload +sudo systemctl enable --now frpc +systemctl status frpc # active (running) +``` + +--- + +## 三、家里开启 SSH 和 RDP 服务 + +### SSH + +```bash +sudo apt update && sudo apt install -y openssh-server +sudo systemctl enable --now ssh +``` + +### RDP（Ubuntu 内置，无需装 xrdp） + +- 打开 **设置 → 系统 → 远程桌面** +- 开启 **远程登录**（Remote Desktop / RDP）开关，记下用户名密码 +- 端口默认 3389 + +> ⚠️ Ubuntu 内置 RDP 依赖**已登录的图形会话**。重启后停在登录界面、无人登录时 RDP 连不上。若要无人值守，需换 xrdp 方案。 + +--- + +## 四、云安全组放行端口（关键坑） + +**最容易漏的一步**：云服务商控制台的「防火墙/安全组」默认只开 22/80/443，需手动放行 TCP： + +``` +7000（frps 连接）、2201/2202（SSH 转发）、3389/3390（RDP 转发） +``` + +> 服务器内部 `ufw` 是 inactive，iptables 只黑名单了若干恶意 IP，不影响端口；问题出在**云控制台安全组**，与服务器内防火墙无关。 + +> 7500（frps 管理面板）走 `frp.hyonglin.top` HTTPS 后，建议在安全组**撤销 7500 的公网放行**，避免明文暴露。 + +--- + +## 五、验证与使用 + +```bash +# 服务器端确认 frpc 已登录、代理已监听 +ss -tlnp | grep -E '2201|2202|3389|3390' +sudo tail -f /var/log/frps.log # 应看到 client login / new proxy success +``` + +连接方式（统一 hyonglin.top + 端口）： + +| 目标 | 命令 | +|---|---| +| pc01 SSH | `ssh 用户名@hyonglin.top -p 2201` | +| pc01 RDP | 远程桌面连 `hyonglin.top:3389` | +| pc02 SSH | `ssh 用户名@hyonglin.top -p 2202` | +| pc02 RDP | 远程桌面连 `hyonglin.top:3390` | + +管理面板：`https://frp.hyonglin.top`（admin / Admin-0ae2664b） + +--- + +## 排错记录 + +| 症状 | 原因 | 解决 | +|---|---|---| +| frpc 状态 active 但 frps 日志无 login 记录 | 云安全组没放行 7000 | 控制台放行 7000 及转发端口 | +| `login to server failed` | token 不一致 | 检查 frpc/frps 的 auth.token 是否相同 | +| 2201 没监听 | frpc 没启动或代理注册失败 | `journalctl -u frpc -n 50` 看报错 | + +--- + +## 相关链接 + +- [[主页与Caddy反向代理]] — 主页/面板的 HTTPS 入口 +- [[sshwifty网页SSH终端]] — 浏览器里连 SSH +- [[Silverbullet部署指南]] — 本服务器自建服务 +- [frp官方文档](https://gofrp.org/zh-cn/) + +--- + +*最后更新：2026-08-13* diff --git a/自建服务/sshwifty网页SSH终端.md b/自建服务/sshwifty网页SSH终端.md new file mode 100644 index 0000000..e671a61 --- /dev/null +++ b/自建服务/sshwifty网页SSH终端.md @@ -0,0 +1,84 @@ +# sshwifty 网页 SSH 终端 + +> 2026-08-13 记录。网页版通用 SSH 客户端，浏览器里填主机/端口/账号即可连接任意机器。Go 单二进制，内存占用 ~10–30MB。 + +--- + +## docker-compose 服务 + +```yaml + sshwifty: + image: niruix/sshwifty:latest + container_name: sshwifty + restart: unless-stopped + environment: + - SSHWIFTY_CONFIG=/etc/sshwifty.conf.json # 指向配置文件 + volumes: + - ./sshwifty.conf.json:/etc/sshwifty.conf.json +``` + +不暴露端口，由 Caddy 反代 `sshwifty:8182`（WebSocket，Caddy 默认支持），入口 `https://ssh.hyonglin.top`。 + +--- + +## 配置文件 sshwifty.conf.json（含预设主机） + +```json +{ + "SharedKey": "hylssh0813", + "Servers": [ + { "ListenInterface": "0.0.0.0", "ListenPort": 8182 } + ], + "Presets": [ + { + "Title": "pc01", + "Type": "SSH", + "Host": "hyonglin.top:2201", + "Meta": { "User": "ylhan", "Encoding": "utf-8", "Authentication": "Password" } + }, + { + "Title": "pc02", + "Type": "SSH", + "Host": "hyonglin.top:2202", + "Meta": { "User": "ylhan", "Encoding": "utf-8", "Authentication": "Password" } + }, + { + "Title": "云服务器", + "Type": "SSH", + "Host": "hyonglin.top:22", + "Meta": { "User": "ubuntu", "Encoding": "utf-8", "Authentication": "Password" } + } + ], + "OnlyAllowPresetRemotes": false +} +``` + +> 端口写在 `Host` 里用 `host:port`，没有单独的 Port 字段。`OnlyAllowPresetRemotes: true` 可限制只连预设主机。改完配置 `docker compose up -d sshwifty` 重启生效。 + +--- + +## 使用 + +1. 打开 `https://ssh.hyonglin.top`，输入 SharedKey 密码 +2. 预设主机点一下即连，也可手动填任意主机： + +| 预设 | 目标 | +|---|---| +| pc01 | `hyonglin.top:2201` | +| pc02 | `hyonglin.top:2202` | +| 云服务器 | `hyonglin.top:22` | + +--- + +## 坑：Known Remotes 不跨设备 + +Known Remotes（已知主机）存在浏览器 `localStorage`，换机器/换浏览器/清缓存都会丢，**没有服务端存储**。 + +要跨设备固定入口，就用上面的 **Presets**（服务端配置，所有机器通用）。 + +--- + +## 相关链接 + +- [[frp内网穿透部署指南]] — SSH/RDP 端口转发 +- [[主页与Caddy反向代理]] — ssh.hyonglin.top 的 HTTPS 入口 diff --git a/自建服务/主页与Caddy反向代理.md b/自建服务/主页与Caddy反向代理.md new file mode 100644 index 0000000..63c84f7 --- /dev/null +++ b/自建服务/主页与Caddy反向代理.md @@ -0,0 +1,127 @@ +# 主页与 Caddy 反向代理 + +> 2026-08-13 记录。用 Caddy 给自建服务套 HTTPS，并做一个主页统一列出服务链接。域名都在 Cloudflare 管理，用 DNS challenge 自动签证书。 + +--- + +## 域名规划 + +所有域名 A 记录统一指向 `58.87.87.208`： + +| 域名 | 用途 | +|---|---| +| `hyonglin.top` | 主页（导航）+ SSH/RDP 直连（纯 DNS 不经 Caddy） | +| `www.hyonglin.top` | 301 重定向到 hyonglin.top | +| `note.hyonglin.top` | 笔记 Silverbullet | +| `frp.hyonglin.top` | frp 管理面板 | +| `ssh.hyonglin.top` | 网页 SSH 终端 | +| `git.hyonglin.top` | Git 服务（Gitea） | + +--- + +## 主页 + +静态页 `silverbullet/homepage/index.html`，导航分「自建服务」「常用链接」两组；以后加服务在 `