论文标题
安全区域的狭窄空间中的无指导自我探索增强了加固学习的安装机器人
Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots
论文作者
论文摘要
在狭窄的空间中，基于传统层次自主系统的运动计划可能会由于映射，定位和控制噪声而导致碰撞，尤其是对于患有非凸面和非自主运动学的类似汽车的Ackermann-stewering机器人。为了解决这些问题，我们利用深厚的加强学习，可以证明可以有效地进行自我决策，在没有给定的地图和目的地的狭窄空间中进行自我探索，同时避免碰撞。具体而言，基于我们的Ackermann-Steer矩形Zebrat机器人及其凉亭模拟器，我们建议矩形安全区域来代表状态并检测矩形形状机器人的碰撞，以及针对不需要WayPoint引导的强化奖励功能的精心设计的奖励功能。为了进行验证，该机器人首先在模拟的狭窄轨道上进行了训练。然后，训练有素的模型被转移到其他模拟轨道上，并可以胜过其他传统方法，包括经典和学习方法。最后，通过我们的Zebrat机器人在现实世界中展示了训练有素的模型。
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.
