RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
CoRR(2024)
摘要
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. Accordingly, this
paper introduces RaceMOP, a novel method for mapless online path planning
designed for multi-agent racing of F1TENTH cars. Unlike classical planners that
depend on predefined racing lines, RaceMOP operates without a map, relying
solely on local observations to overtake other race cars at high speed. Our
approach combines an artificial potential field method as a base policy with
residual policy learning to introduce long-horizon planning capabilities. We
advance the field by introducing a novel approach for policy fusion with the
residual policy directly in probability space. Our experiments for 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 while
generalizing to unknown racetracks, paving the way for further use of our
method in robotics. We make the open-source code for RaceMOP available at
http://github.com/raphajaner/racemop.
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