利用 GPU 计算距离图实现实时避碰
Real Time Collision Avoidance with GPU-Computed Distance Maps
AI总结：
提出基于 GPU 三维距离变换与体素地图的实时运动学控制框架，使 Tiago 机器人在末端位姿跟踪中统一规避外部障碍和自碰撞。
摘要：
本文提出在实时运动学反馈控制中，利用 GPU 计算的距离变换实现冗余机械臂的反应式障碍物与自碰撞规避。所提框架采用机器人与环境的离散化表示，计算三维欧氏距离变换，以用于基于任务优先级的运动学控制。环境场景由三维 GPU 体素地图表示，该地图根据实时点云数据创建并更新；同时，机器人连杆模型离线转换为体素，并依据机器人关节状态插入体素地图，从而形成自障碍物地图。所提方法使用 Tiago 机器人进行评估，结果表明：即使存在快速移动障碍物，机器人在实时执行末端执行器位姿跟踪时，也能在同一框架内满足所有障碍物与自碰撞规避约束。本文还对依赖 GPU 与 CPU 计算距离场的相关工作进行了比较，以突出 GPU 距离场的时间性能与精度。
英文摘要：
This paper presents reactive obstacle and self-collision avoidance of redundant robotic manipulators within real time kinematic feedback control using GPU-computed distance transform. The proposed framework utilizes discretized representation of the robot and the environment to calculate 3D Euclidean distance transform for task-priority based kinematic control. The environment scene is represented using a 3D GPU-voxel map created and updated from a live pointcloud data while the robotic link model is converted into a voxels offline and inserted into the voxel map according to the joint state of the robot to form the self-obstacle map. The proposed approach is evaluated using the Tiago robot, showing that all obstacle and self collision avoidance constraints are respected within one framework even with fast moving obstacles while the robot performs end-effector pose tracking in real time. A comparison of related works that depend on GPU and CPU computed distance fields is also presented to highlight the time performance as well as accuracy of the GPU distance field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
