In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.

翻译：本文提出了一种适用于复杂动态环境中机械臂的全局反应式运动规划框架。具体而言，我们对Becker等人（2021）提出的圆场预测（CFP）规划器进行了扩展，以确保机械臂整体结构能够实现避障。为此，我们开发了一个运动规划框架，该框架利用来自任意构型空间运动规划器的全局有利避障方向信息，在反应式规避动态障碍物的同时改进全局轨迹，并降低所需的计算能力。所提出的运动规划框架在多个包含复杂动态障碍物的仿真中进行了测试，与现有运动规划方法相比展现出巨大潜力。