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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Robotic Control Systems
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1447351

Informed Circular Fields: A Global Reactive Obstacle Avoidance Framework for Robotic Manipulators

Provisionally accepted
Marvin Becker Marvin Becker 1*Philipp Caspers Philipp Caspers 1Torsten Lilge Torsten Lilge 1Sami Haddadin Sami Haddadin 2Matthias A. Müller Matthias A. Müller 1
  • 1 Leibniz University Hannover, Hanover, Germany
  • 2 Technical University of Munich, Munich, Bavaria, Germany

The final, formatted version of the article will be published soon.

    In this paper, we present a global reactive motion planning framework designed for robotic manipulators navigating in complex dynamic environments. Utilizing local minima-free circular fields, our methodology generates reactive control commands while also leveraging global environmental information from arbitrary configuration space motion planners to identify promising trajectories around obstacles. Furthermore, we extend the virtual agents framework introduced in (Becker et al., 2021) to incorporate this global information, simulating multiple robot trajectories with varying parameter sets to enhance avoidance strategies. Consequently, the proposed unified robotic motion planning framework seamlessly combines global trajectory planning with local reactive control and ensures comprehensive obstacle avoidance for the entire body of a robotic manipulator. The efficacy of the proposed approach is demonstrated through rigorous testing in over 4000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework's performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot's ability to promptly adapt its motion plan and effectively avoid unpredictable movements by humans within its workspace. Overall, our contributions offer a robust and versatile solution for global reactive motion planning in dynamic environments.

    Keywords: Autonomous robotic systems, Guidance navigation and control, Real-time collision avoidance, Robotic manipulation arm, motion planning

    Received: 11 Jun 2024; Accepted: 12 Nov 2024.

    Copyright: © 2024 Becker, Caspers, Lilge, Haddadin and Müller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Marvin Becker, Leibniz University Hannover, Hanover, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.