Circular Fields and Predictive Multi-Agents for Online Global Trajectory Planning

Abstract

Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards this end, based on the initial idea proposed in Haddadin et al. 2013 we propose the Circular Field Predictions approach, which unifies reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning. The proposed approach is inspired by electromagnetic fields, free of local minima and extended with artificial multi-agents to efficiently explore the environment. The algorithm is extensively analysed in complex simulation environments where it is shown to be able to generate smooth trajectories around arbitrarily shaped obstacles. Moreover, we experimentally verified the approach with a 7 Degree-of-Freedom (DoF) Franka Emika robot.