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.
@article{arxiv.2212.05815,
title = {Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators},
author = {Marvin Becker and Philipp Caspers and Tom Hattendorf and Torsten Lilge and Sami Haddadin and Matthias A. Müller},
journal= {arXiv preprint arXiv:2212.05815},
year = {2023}
}