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.

翻译：本文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）的安全精确操控。与全向或更简单的非完整机器人（如差速驱动机器人）不同，DASMRs面临强烈的运动学约束，导致经典规划器在杂乱环境中表现脆弱。该框架利用后见经验回放（HER）和CrossQ覆盖层，在规避障碍物的同时提升操控效率。通过重型四轮转向机器人的仿真实验表明，学习得到的策略能够鲁棒地抵达高达97%的目标位置并成功避障。本框架不依赖于人工轨迹设计或专家示范数据。