RaceMOP：面向多智能体自主赛车的基于残差策略学习的无地图在线路径规划
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
AI总结：
针对多智能体自主赛车无地图在线路径规划超车安全难题，提出融合人工势场基础策略与概率空间残差策略的RaceMOP方法，实现长视野鲁棒避碰超车，性能优于现有方案且可泛化至未知赛道。
摘要：
多智能体自主赛车中的交互式决策能提供自动驾驶汽车领域之外的宝贵洞见。无地图在线路径规划尤其具有实用吸引力，但由于规划视野有限，在安全超越对手方面存在挑战。为解决这一问题，我们提出RaceMOP，一种专为F1TENTH赛车多智能体竞速设计的新型无地图在线路径规划方法。与依赖预设赛车线的经典规划器不同，RaceMOP无需地图即可运行，仅利用局部观测执行高速超车动作。我们的方法将人工势场法作为基础策略，与残差策略学习相结合，以实现长视野规划。我们提出了一种直接在概率空间中与残差策略进行策略融合的新方法，推动了该领域的发展。在12条模拟赛道上开展的大量实验验证了RaceMOP具备长视野决策能力，在超车动作中能实现鲁棒的避碰。RaceMOP在操控性能上优于现有无地图规划器，且能泛化到未知赛道，证明了其在机器人领域更广泛应用的潜力。我们的代码可在http://github.com/raphajaner/racemop获取。
英文摘要：
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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
