Reinforcement learning based mobile robot path planning in unknown environment

Abstract

In order to solve problem of the adaptive path planning on mobile robot in unknown environments,a self-learning method based on Q-learning algorithm was proposed.Firstly,the learning framework was designed for the adaptive path planning of mobile robot in unknown environments based on sensor information,and the mathematics model for each element of learning algorithm was proposed.Then the generalization problem of continuous state space of reinforcement learning system was solved by fuzzy logic,the size of the Q-table was reduced and the speed of the learning algorithm was increased.Finally,the simulation of self-learning method based on Q-learnning algorithm was carried in the environment with different obstacles,the adaptive path planning was achieved by the mobile robot through self-learning.The research results certify the validity of this method.