Study on Adaptive Path Planning for Mobile Robot Based on Q Learning

Abstract

Q learning is a popular method of reinforcement learning algorithms. In order to decrease the learning space and increase the learning convergent velocity, Q-layered learning method was adopted to divide the task of searching optimal path into three basic behaviors, namely static obstacle-avoidance, dynamic obstacle-avoidance and goal-finding. Especially in the learning for the static obstacle-avoidance behavior, a new priority Q search method (PQA) was used to avoid the blindly search of the random search algorithm (RA). PQA used the sum of weighted vectors pointing away from obstacles to predict the reinforcement reward receiving from the possible state-action after acting. Robot controller selected an action based on the result at the next executing time. At last PQA and RA were both simulated in two different environment. The learning results show that PQA has fewer learning steps and higher task complete percent than RA. PQA is an effective way to solve the problem of the path planning under dynamic environment.