In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for car-like Ackermann-steering robots which suffer from non-convex and non-holonomic kinematics. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a given map and destination while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the waypoint guidance. For validation, the robot was first trained in a simulated narrow track. Then, the well-trained model was transferred to other simulation tracks and could outperform other traditional methods including classical and learning methods. Finally, the trained model is demonstrated in the real world with our ZebraT robot.

翻译：在狭窄空间中，基于传统分层自主系统的运动规划可能因建图、定位和控制噪声导致碰撞，尤其对于受非凸和非完整运动学约束的类车式阿克曼转向机器人而言更为突出。为解决这些问题，我们利用深度强化学习（已被验证在自主决策方面有效）实现无需预设地图和目标的狭窄空间自主探索与避碰。具体而言，基于自研的阿克曼转向矩形ZebraT机器人及其Gazebo仿真器，我们提出了矩形安全区域概念用于表征矩形机器人的状态和碰撞检测，并设计了无需路径点引导的强化学习奖励函数。为验证有效性，首先在仿真狭窄赛道中训练机器人，随后将训练完成的模型迁移至其他仿真赛道，其表现优于包括经典方法和学习方法在内的传统方案。最终，我们使用ZebraT机器人实际部署了该训练模型。