Application of Artificial Neural Network Based on Q-learning for Mobile Robot Path Planning
Path planning is a difficult part of the navigation task for the mobile robot under dynamic and unknown environment. It needs to solve a mapping relationship between the sensing space and the action space. The relationship can be achieved through different ways. But it is difficult to be expressed by an accurate equation. This paper uses multi-layer feedforward artificial neural network (ANN) to construct a path-planning controller by its powerful nonlinear functional approximation. Then the path planning task is simplified to a classified problem which are five state-action mapping relationship. One reinforcement learning method, Q-learning, is used to collect training samples for the ANN controller. At last the trained controller runs in the simulation environment and retrains itself furthermore combining the reinforcement signal during the interaction with the environment. Strategy based on the Combination of ANN and Q-learning is better than using only one of the two methods. The simulation result also shows that the strategy can find the optimal path than using Q-learning only.
ANN Q-learning path planning mobile robot
Caihong Li Jingyuan Zhang Yibin Li
School of Computer Science and Technology Shandong University of Technology Zibo, Shandong province, School of Control Science and Engineering Shandong University Jinan, Shandong province, china School of Electrical and Automation Engineering Tianjin University Tianjin, china;School of Control
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
978-982
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）