Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators

TL;DR

This paper introduces an enhanced global reactive motion planning framework for robotic manipulators that ensures obstacle avoidance of the entire structure in dynamic environments, improving trajectory quality and computational efficiency.

Contribution

It extends the circular field predictions planner to account for the whole manipulator structure and integrates global avoidance directions for better trajectory planning.

Findings

Successfully avoids dynamic obstacles in complex simulations

Reduces computational power compared to existing methods

Demonstrates improved global trajectories in tests

Abstract

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.