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.

翻译：多智能体自主竞速中的交互式决策为自动驾驶领域之外的场景提供了重要洞见。无图在线路径规划尤其具有实际应用价值，但由于规划视域受限，在安全超车方面面临挑战。为此，本文提出RaceMOP——一种专为F1TENTH赛车多智能体竞速设计的新型无图在线路径规划方法。不同于依赖预设赛道路线的经典规划器，RaceMOP无需地图，仅通过局部观测实现高速超车。我们的方法将人工势场法作为基础策略，并结合残差策略学习引入长视域规划能力。通过提出一种在概率空间中直接融合残差策略的革新性策略融合方法，我们推动了该领域的发展。在12个模拟赛道上的实验验证表明，RaceMOP能够在超车机动中实现具备鲁棒避障能力的长视域决策。与现有无图规划器相比，RaceMOP展现出更优的操控性能，同时具备对未知赛道的泛化能力，为该方法在机器人领域的进一步应用铺平道路。我们已在http://github.com/raphajaner/racemop开源RaceMOP代码。