Informed Circular Fields

A Global Reactive Obstacle Avoidance Framework for Robotic Manipulator

authored by: Marvin Becker, Philipp Caspers, Torsten Lilge, Sami Haddadin, Matthias A. Müller
Abstract: 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 4,000 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.
Organisation(s): Institute of Automatic Control
External Organisation(s): Technical University of Munich (TUM)
Type: Article
Journal: Frontiers in Robotics and AI
Volume: 11
Publication date: 03.01.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computer Science Applications, Artificial Intelligence
Electronic version(s): https://doi.org/10.3389/frobt.2024.1447351 (Access: Open)

BibTeX

@article{5c9b013ee9a8460fb92070e48c1faf9a,
title = "Informed Circular Fields: A Global Reactive Obstacle Avoidance Framework for Robotic Manipulator",
abstract = "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 4,000 simulation scenarios, where it consistently outperforms existing motion planners. Additionally, we validate our framework{\textquoteright}s performance in real-world experiments using a collaborative Franka Emika robot with vision feedback. Our experiments illustrate the robot{\textquoteright}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, Motion Planning, robotic manipulation arm, motion planning, real-time collision avoidance, guidance navigation and control, autonomous robotic systems",
author = "Marvin Becker and Philipp Caspers and Torsten Lilge and Sami Haddadin and M{\"u}ller, {Matthias A.}",
note = "Copyright 2025 Becker, Caspers, Lilge, Haddadin and M{\"u}ller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).",
year = "2025",
month = jan,
day = "3",
doi = "10.3389/frobt.2024.1447351",
language = "English",
volume = "11",
journal = "Frontiers in Robotics and AI",
issn = "2296-9144",
publisher = "Frontiers Media S.A.",
}