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

Content Provider	Springer Nature Link
Author	Ma, Xin Xu, Ya Sun, Guo qiang Deng, Li xia Li, Yi bin
Copyright Year	2013
Abstract	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.
Starting Page	167
Ending Page	178
Page Count	12
File Format	PDF
ISSN	18691951
Journal	Journal of Zhejiang University SCIENCE C
Volume Number	14
Issue Number	3
e-ISSN	1869196X
Language	English
Publisher	SP Zhejiang University Press
Publisher Date	2013-03-10
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	Path planning Q-learning Autonomous mobile robot Reinforcement learning Computer Science Electrical Engineering Computer Hardware Computer Systems Organization and Communication Networks Electronics and Microelectronics, Instrumentation Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Engineering

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