By Topic

Robot path planning by artificial potential field optimization based on reinforcement learning with fuzzy state

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiaodong Zhuang ; Dept. of Electron. & Eng., Ocean Univ. of Qingdao, China ; Qingchun Meng ; Bo Yin ; Hanping Wang

Temporal difference (TD) learning with fuzzy state is applied to robot navigation in a multi-obstacle environment. An interpretation of the state evaluation function is given by regarding the state evaluation as a discrete artificial potential field (APF). Global optimal path planning is implemented with the APF obtained by TD learning. The APF obtained is globally optimal and avoids the local minimum areas, which always appear in traditional APF methods. Fuzzy state is introduced to improve the learning efficiency. A computer evaluation experiment shows the method's effectiveness and efficiency.

Published in:

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on  (Volume:2 )

Date of Conference:

2002