A Real-Time Collision-Avoidance Motion Planner for Robot Soccer

This work presents the implementation and evaluation of a real-time collision-avoiding motion planning algorithm for highly dynamic environments. By combining short-horizon obstacle estimation with the robot constraints, our method implements collision-avoiding situational-aware motion planning by heuristically exploring multiple relevant paths. Directly feeding the current motion setpoint of the path into the low-level controller closes the loop between motion planning and low-level control, ensuring constraint-aware execution. Its practical implementation in physical robots in dynamic RoboCup-like scenarios validated its effectiveness, with low computational costs enabling fast adaptation to changing environments. Furthermore, the capabilities of our motion planner were demonstrated during RoboCup 2024 and practice matches<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.

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Publication Details

Published
2025-10-19
DOI
https://doi.org/10.1109/iros60139.2025.11247494
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

A Real-Time Collision-Avoidance Motion Planner for Robot Soccer

Mark van den Brand, René van de Molengraft, Aneesh Deogan, Dennis Bruijnen
Robotic Path Planning Algorithms
article

A Real-Time Collision-Avoidance Motion Planner for Robot Soccer

Mark van den Brand, René van de Molengraft, Aneesh Deogan, Dennis Bruijnen
article en

Abstract

This work presents the implementation and evaluation of a real-time collision-avoiding motion planning algorithm for highly dynamic environments. By combining short-horizon obstacle estimation with the robot constraints, our method implements collision-avoiding situational-aware motion planning by heuristically exploring multiple relevant paths. Directly feeding the current motion setpoint of the path into the low-level controller closes the loop between motion planning and low-level control, ensuring constraint-aware execution. Its practical implementation in physical robots in dynamic RoboCup-like scenarios validated its effectiveness, with low computational costs enabling fast adaptation to changing environments. Furthermore, the capabilities of our motion planner were demonstrated during RoboCup 2024 and practice matches<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.

Robotics Research (United States) (US), AkzoNobel (Netherlands) (NL), Eindhoven University of Technology (NL)
Openalex Percentile: Top 62%
Robotic Path Planning Algorithms
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