強化学習とORCA制約による一般非線形エージェントの相互衝突回避
Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning
2エージェントの衝突回避を強化学習で学習し、ORCAの線形制約と凸最適化で多エージェントに拡張することで、速度直接制御に依存せず一般非線形エージェントの安全な衝突回避を実現した。
著者: Hao Li, Bowen Weng, Abhishek Gupta, Jia Pan, Wei Zhang
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
Finding feasible and collision-free paths for multiple nonlinear agents is challenging in the decentralized scenarios due to limited available information of other agents and complex dynamics constraints. In this paper, we propose a fast multi-agent collision avoidance algorithm for general nonlinear agents with continuous action space, where each agent observes only positions and velocities of nearby agents. To reduce online computation, we first decompose the multi-agent scenario and solve a two agents collision avoidance problem using reinforcement learning (RL). When extending the trained policy to a multi-agent problem, safety is ensured by introducing the optimal reciprocal collision avoidance (ORCA) as linear constraints and the overall collision avoidance action could be found through simple convex optimization. Most existing RL-based multi-agent collision avoidance algorithms rely on the direct control of agent velocities. In sharp contrasts, our approach is applicable to general nonlinear agents. Realistic simulations based on nonlinear bicycle agent models are performed with various challenging scenarios, indicating a competitive performance of the proposed method in avoiding collisions, congestion and deadlock with smooth trajectories.
