基于强化学习的机场服务机器人动态路径规划

Path Planning of Airport Service Robot Based on Reinforcement Learning

针对机场场面服务机器人自动寻路的过程中,特种车辆及地勤人员等都有可能会与服务机器人产生碰撞的问题。为了提高机器人避障能力以及其知识库的丰富程度,采用基于强化学习的路径规划方法,选择Q-Learning算法对机器人的寻路学习行为进行建模,使得位于机场场面中的服务机器人可以在寻路过程中获取到最佳路径策略,完善自身及外物冲突解脱知识库的不足,最终实验仿真结果表示Q-Learning算法在机场服务机器人在机场场面中的合理性和可行性。

In the course of automatic pathfinding of airport scene service robot,special vehicles and ground crew may collide with the service robot.In order to improve the ability of the robot obstacle avoidance and the richness of its knowledge base,the path planning method based on rein⁃forcement Learning,choose the Q-Learning algorithm for robot path finding learning behavior modeling,that is located in the airport scene in the service robot can in pathfinding,for obtaining the best path to the strategy in the process of perfecting itself and the external things conflict relief the shortage of the knowledge base,finally the experimental simulation results that the Q-Learning algorithm in the airport service robot scene at the airport in the rationality and feasibility.