<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>机器人 on Blog</title><link>https://icelemon.top/tags/%E6%9C%BA%E5%99%A8%E4%BA%BA/</link><description>Recent content in 机器人 on Blog</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Sat, 24 Oct 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://icelemon.top/tags/%E6%9C%BA%E5%99%A8%E4%BA%BA/index.xml" rel="self" type="application/rss+xml"/><item><title>ROS 2通信、QoS、时间与生命周期</title><link>https://icelemon.top/tech/robotics/ros2-runtime/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/ros2-runtime/</guid><description>按数据和任务选择topic、service或action，排查QoS、时间戳、回调阻塞和节点启动顺序。</description></item><item><title>SLAM、占据地图与因子图优化</title><link>https://icelemon.top/tech/robotics/slam-factor-graphs/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/slam-factor-graphs/</guid><description>理解边定位边建图的耦合，用一维位姿图检查回环、权重和坐标自由度。</description></item><item><title>URDF、TF与ros2_control的接口边界</title><link>https://icelemon.top/tech/robotics/robot-model-control/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/robot-model-control/</guid><description>用单关节模型区分外观、碰撞、惯量、状态与控制命令，理解mock接口能验证什么。</description></item><item><title>传感器模型、标定与时间同步</title><link>https://icelemon.top/tech/robotics/sensors-calibration/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/sensors-calibration/</guid><description>区分内参、外参、偏置和时间误差，理解相机、深度、激光、编码器与IMU的测量边界。</description></item><item><title>动力学与反馈控制：PID、LQR和MPC</title><link>https://icelemon.top/tech/robotics/dynamics-feedback/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/dynamics-feedback/</guid><description>从一维质量块理解模型、反馈、扰动和执行器饱和，比较控制器时保留相同条件。</description></item><item><title>轨迹生成、时间缩放与运动约束</title><link>https://icelemon.top/tech/robotics/trajectories-time-scaling/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/trajectories-time-scaling/</guid><description>把几何路径变成带速度与加速度的运动，计算五次多项式的时间下界并检查控制器接口。</description></item><item><title>机器人仿真、复现记录与评测</title><link>https://icelemon.top/tech/robotics/simulation-evaluation/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/simulation-evaluation/</guid><description>按需要选择数值模型、Gazebo或MuJoCo，固定数据与版本，并区分仿真通过和真实系统验证。</description></item><item><title>机器人具身学习路线</title><link>https://icelemon.top/tech/robotics/embodied-learning-route/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/embodied-learning-route/</guid><description>先建立机器人与学习基础，再完成BC、策略梯度和闭环评测，理解视觉动作模型的接口与研究边界。</description></item><item><title>机器人视觉：从像素、点云到物体位姿</title><link>https://icelemon.top/tech/robotics/robot-vision/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/robot-vision/</guid><description>区分检测、分割、三维位姿与配准，理解ICP的适用条件，并把视觉输出接入机器人坐标链。</description></item><item><title>机械臂项目：抓放流程与MoveIt 2集成</title><link>https://icelemon.top/tech/robotics/manipulation-project/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/manipulation-project/</guid><description>运行两连杆抓放状态机和跟踪实验，再把视觉、规划场景、夹爪与轨迹执行接入MoveIt 2。</description></item><item><title>机械臂与机器人操作学习路线</title><link>https://icelemon.top/tech/robotics/manipulation-route/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/manipulation-route/</guid><description>从运动学和力学到视觉、抓取、轨迹与MoveIt 2，区分到位、夹持和真实任务成功。</description></item><item><title>具身学习项目：经典策略、BC与REINFORCE对照</title><link>https://icelemon.top/tech/robotics/embodied-learning-project/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/embodied-learning-project/</guid><description>在可复现的一维闭环中完成合成演示、BC与RL训练、独立评估和梯度检查，并提供PyTorch对照。</description></item><item><title>模仿学习与行为克隆</title><link>https://icelemon.top/tech/robotics/imitation-learning/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/imitation-learning/</guid><description>把演示转成观测—动作数据，理解行为克隆的损失、闭环分布偏移和按episode切分。</description></item><item><title>强化学习：MDP、策略梯度与机器人约束</title><link>https://icelemon.top/tech/robotics/reinforcement-learning/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/reinforcement-learning/</guid><description>明确状态、动作、奖励和终止，用可运行REINFORCE实验理解回报、基线与闭环评估。</description></item><item><title>任务执行、行为树与失败恢复</title><link>https://icelemon.top/tech/robotics/task-execution-recovery/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/task-execution-recovery/</guid><description>把导航和抓取接成可取消、可重试的任务，处理过期结果、重试上限和执行状态。</description></item><item><title>视觉动作策略：Diffusion Policy、ACT与VLA</title><link>https://icelemon.top/tech/robotics/visuomotor-policies/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/visuomotor-policies/</guid><description>理解动作分布、动作序列和语言条件，检查策略输出的坐标、单位、归一化与执行频率。</description></item><item><title>移动机器人项目：导航闭环与Nav2集成</title><link>https://icelemon.top/tech/robotics/mobile-navigation-project/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/mobile-navigation-project/</guid><description>先运行A*、外形膨胀和差速跟踪的小型实验，再按TF、定位、代价地图和任务执行接入Nav2。</description></item><item><title>移动机器人学习路线</title><link>https://icelemon.top/tech/robotics/mobile-robotics-route/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/mobile-robotics-route/</guid><description>从坐标与运动模型走到传感器、定位建图、规划控制和Nav2，逐层完成可检查的导航闭环。</description></item><item><title>运动规划：配置空间、搜索与采样</title><link>https://icelemon.top/tech/robotics/motion-planning/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/motion-planning/</guid><description>把机器人形状与运动约束纳入规划，区分栅格A*、PRM/RRT、轨迹优化和局部跟踪。</description></item><item><title>正逆运动学、雅可比与奇异性</title><link>https://icelemon.top/tech/robotics/kinematics-jacobians/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/kinematics-jacobians/</guid><description>用两连杆机械臂理解关节与末端的映射，数值检查雅可比，并识别不可达目标和奇异位形。</description></item><item><title>抓取、接触力与阻抗控制</title><link>https://icelemon.top/tech/robotics/grasping-contact/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/grasping-contact/</guid><description>用摩擦锥和功率关系理解夹持稳定性，区分位置、力与阻抗控制，并计算一个夹持力下界。</description></item><item><title>状态估计：Kalman、EKF与粒子滤波</title><link>https://icelemon.top/tech/robotics/state-estimation/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/state-estimation/</guid><description>把运动预测和带噪观测合并，检查协方差、创新和缺测，而不是只追求平滑曲线。</description></item><item><title>坐标系、旋转与刚体变换</title><link>https://icelemon.top/tech/robotics/frames-transforms/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/frames-transforms/</guid><description>把传感器、机器人和目标的位置放进同一套坐标约定，检查变换方向与单位。</description></item></channel></rss>