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CLC number: TP242.6

On-line Access: 2013-03-05

Received: 2012-07-19

Revision Accepted: 2012-10-12

Crosschecked: 2013-01-04

Cited: 8

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.3 P.167-178


State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

Author(s):  Xin Ma, Ya Xu, Guo-qiang Sun, Li-xia Deng, Yi-bin Li

Affiliation(s):  School of Control Science and Engineering, Shandong University, Jinan 250061, China

Corresponding email(s):   maxin@sdu.edu.cn

Key Words:  Path planning, Q-learning, Autonomous mobile robot, Reinforcement learning

Xin Ma, Ya Xu, Guo-qiang Sun, Li-xia Deng, Yi-bin Li. State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots[J]. Journal of Zhejiang University Science C, 2013, 14(3): 167-178.

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journal="Journal of Zhejiang University Science C",
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%T State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots
%A Xin Ma
%A Ya Xu
%A Guo-qiang Sun
%A Li-xia Deng
%A Yi-bin Li
%J Journal of Zhejiang University SCIENCE C
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200226

T1 - State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots
A1 - Xin Ma
A1 - Ya Xu
A1 - Guo-qiang Sun
A1 - Li-xia Deng
A1 - Yi-bin Li
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 3
SP - 167
EP - 178
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1200226

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments. As a computational approach to learning through interaction with the environment, reinforcement learning algorithms have been widely used for intelligent robot control, especially in the field of autonomous mobile robots. However, the learning process is slow and cumbersome. For practical applications, rapid rates of convergence are required. Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning, a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments. The state chain is built during the searching process. After one action is chosen and the reward is received, the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning. With the increasing number of Q-values updated after one action, the number of actual steps for convergence decreases and thus, the learning time decreases, where a step is a state transition. Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments. The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time, compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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