Obstacle avoidance of a mobile robot using hybrid learning approach
Meng Joo Er
Chang Deng
Intelligent Syst. Centre, Nanyang Technol. Univ., Singapore;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: June 2005
Volume: 52,
Issue: 3
On page(s): 898- 905
ISSN: 0278-0046
INSPEC Accession Number: 8464616
Digital Object Identifier: 10.1109/TIE.2005.847576
Current Version Published: 2005-05-31
Abstract
in this paper, a hybrid learning approach for obstacle avoidance of a mobile robot is presented. the key features of the approach are, firstly, innate hardwired behaviors which are used to bootstrap learning in the mobile robot system. a neuro-fuzzy controller is developed from a pre-wired or innate controller based on supervised learning in a simulation environment. the fuzzy inference system has been constructed based on the generalized dynamic fuzzy neural networks learning algorithm of Wu and Er, whereby structure and parameters identification are carried out automatically and simultaneously. Secondly, the neuro-fuzzy controller is capable of re-adapting in a new environment. After carrying out the learning phase on a simulated robot, the controller is implemented on a real robot. A reinforcement learning method based on the fuzzy actor-critic learning algorithm is employed so that the system can re-adapt to a new environment without human intervention. In this phase, the structure of the fuzzy inference system and the parameters of the antecedent parts of fuzzy rules are frozen, and reinforcement learning is applied to further tune the parameters in the consequent parts of the fuzzy rules. Through the hybrid learning approach, an efficient and compact neuro-fuzzy system is generated for obstacle avoidance of a mobile robot in the real world.
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