TY - JOUR

T1 - Informed Circular Fields

T2 - A Global Reactive Obstacle Avoidance Framework for Robotic Manipulator

AU - Becker, Marvin

AU - Caspers, Philipp

AU - Lilge, Torsten

AU - Haddadin, Sami

AU - Müller, Matthias A.

N1 - Copyright 2025 Becker, Caspers, Lilge, Haddadin and Müller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

PY - 2025/1/3

Y1 - 2025/1/3

N2 - 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.

AB - 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.

KW - Autonomous Robotic Systems

KW - Guidance navigation and control

KW - Real-Time Collision Avoidance

KW - Motion Planning

KW - robotic manipulation arm

U2 - 10.3389/frobt.2024.1447351

DO - 10.3389/frobt.2024.1447351

M3 - Article

VL - 11

JO - Frontiers in Robotics and AI

JF - Frontiers in Robotics and AI

M1 - 1447351

ER -