Mobile robot path planning based on improved Q learning algorithm

Abstract

For path planning of mobile robot, the traditional Q learning algorithm easy to fall into local optimum, slow convergence etc. issues, this paper proposes a new greedy strategy, multi-target searching of Q learning algorithm. Don't need to create the environment model, the mobile robot from a single-target searching transform into multi-target searching an unknown environment, firstly, by the dynamic greedy strategy exploring interim to use unknown environment, improve learning ability that mobile robot learn the environment, improve the convergence of the mobile robot speed. And a large number of improved Q-learning algorithms are applied to mobile robot optimization simulation in unknown environment, by comparing with traditional Q algorithm, theory and experiment proved that improved Q-learning algorithm speed up the convergence rate of the robot, improve collision avoidance capability and learning efficiency.