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

verfasst von

Marvin Becker, T. Lilge, Matthias Müller, S. Haddadin

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 [27] 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 planningreactive 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.

Organisationseinheit(en)

Institut für Regelungstechnik

Externe Organisation(en)

Technische Universität München (TUM)

Typ

Artikel

Journal

IEEE Robotics and Automation Letters

Band

6

Seiten

2618-2625

Anzahl der Seiten

8

ISSN

2377-3766

Publikationsdatum

04.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Maschinenbau, Steuerung und Optimierung, Artificial intelligence, Mensch-Maschine-Interaktion, Steuerungs- und Systemtechnik, Maschinelles Sehen und Mustererkennung, Biomedizintechnik, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.15488/11326 (Zugang: Offen)
https://doi.org/10.1109/lra.2021.3061997 (Zugang: Geschlossen)