A Global Path Planning Algorithm for Robots Using Reinforcement Learning
Penggang Gao, Zihan Liu, Zongkai Wu, Donglin Wang · 2019
Path planning is the key technology for autonomous mobile robots. In view of the shortage of paths found by traditional best first search (BFS) and rapidly-exploring random trees (RRT) algorithm which are not short and smooth enough for robot navigation, a new global planning algorithm combined with reinforcement learning is presented for robots. In our algorithm, a path graph is established firstly, in which the paths collided with the obstacles are removed directly. Then a collision-free path will be found by Q-Learning from starting point to the goal. The experiment results illustrate that it can generate shorter and smoother paths, compared with the BFS and RRT algorithm.