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.

翻译：本文提出了一种利用GPU计算距离变换实现冗余机械臂在实时运动学反馈控制中的反应式障碍物与自碰撞规避方法。所提出的框架采用机器人与环境的离散化表示，通过计算三维欧几里得距离变换，实现基于任务优先级的运动学控制。环境场景通过由实时点云数据创建并更新的三维GPU体素地图表示，而机器人连杆模型则离线转换为体素，并根据机器人关节状态插入体素地图以构建自障碍物地图。该方法在Tiago机器人上进行了评估，结果表明即使在快速移动障碍物存在的情况下，该框架也能在机器人实时执行末端执行器位姿跟踪时，同时满足所有障碍物与自碰撞规避约束。本文还对比了依赖GPU与CPU计算距离场的相关研究，以突显GPU距离场在时间性能与精度方面的优势。