用强化学习让四轮转向机器人在复杂环境里又快又安全地避障
Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
- 基于软演员-评论家框架，结合经验回放与交叉Q网络提升策略
- 仿真中97%目标点可达且全程避障，无需人工轨迹或专家示范
- 适合需要自主避障的高机动移动机器人研发人员参考
我们提出一种基于软演员-评论家（SAC）的深度强化学习框架，用于实现双阿克曼转向移动机器人（DASMRs）的安全精准操控。与全向或简单非完整机器人（如差速驱动机器人）不同，DASMRs具有强运动学约束，在复杂环境中传统规划器容易失效。本框架融合事后经验回放（HER）与交叉Q叠加机制，提升操作效率的同时有效规避障碍物。对重型四轮转向火星车的仿真结果表明，所学策略可在97%的目标位置成功到达且完全避障。该方法不依赖人工设计轨迹或专家示范。
原文摘要 · Abstract (English)
We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike holonomic or simpler non-holonomic robots such as differential-drive robots, DASMRs face strong kinematic constraints that make classical planners brittle in cluttered environments. Our framework leverages the Hindsight Experience Replay (HER) and the CrossQ overlay to encourage maneuvering efficiency while avoiding obstacles. Simulation results with a heavy four-wheel-steering rover show that the learned policy can robustly reach up to 97% of target positions while avoiding obstacles. Our framework does not rely on handcrafted trajectories or expert demonstrations.
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