In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises. Additionally, it is disabled when mapless. 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 map 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 destination information. Then we benchmark five reinforcement learning algorithms including DDPG, DQN, SAC, PPO, and PPO-discrete, in a simulated narrow track. After training, the well-performed DDPG and DQN models can be transferred to three brand new simulated tracks, and furthermore to three real-world tracks.

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，且在无地图环境下无法运行。为解决这些问题，我们利用在自主决策中已被验证有效的深度强化学习，在无地图条件下实现狭窄空间的自探索并避免碰撞。具体而言，基于阿克曼转向矩形结构的ZebraT机器人及其Gazebo仿真器，我们提出了用于矩形机器人状态表征与碰撞检测的矩形安全区域，以及一种无需目标点信息的定制化强化学习奖励函数。随后，我们在仿真狭窄通道中对DDPG、DQN、SAC、PPO和PPO-discrete五种强化学习算法进行基准测试。训练完成后，性能优异的DDPG和DQN模型可迁移至三个全新仿真通道，并进一步应用于三个真实环境测试场景。