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Cited by 6Robotic Path Planning AlgorithmsReinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning

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Trumpp, Raphael, Javanmardi, Ehsan, Nakazato, Jin, Tsukada, Manabu, Caccamo, Marco·2024·2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros58592.2024.10801657
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Abstract

The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that rely on predefined racing lines, RaceMOP operates without a map, utilizing only local observations to execute high-speed overtaking maneuvers. Our approach combines an artificial potential field method as a base policy with residual policy learning to enable long-horizon planning. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Extensive experiments on twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners and generalizes to unknown racetracks, affirming its potential for broader applications in robotics. Our code is available at http://github.com/raphajaner/racemop.

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Cited by

Sorted by citations

Authors
Trumpp, R., Javanmardi, E., Nakazato, J., Tsukada, M., & Caccamo, M.
Affiliations
  • Technical University of Munich (DE)
  • The University of Tokyo (JP)
ORCID
Venue
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2024-10-14
Pages
8449-8456
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros58592.2024.10801657
OpenAlex
W4405785955
Cited by
6
References
1
Citations by year
2026: 1 · 2025: 5
FWCI
1.24
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
F1TENTH, overtaking, artificial potential field, long-horizon planning, collision avoidance, autonomous racing car

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