Experimental evaluation of new navigator of mobile robot using fuzzy Q-learning. (15th August 2019)

• Record Type: Journal Article
• Authors: Lachekhab, Fadhila; Tadjine, Mohamed; Kesraoui, Mohamed
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 11:Number 2(2019)
• Page Start: 50
• Page End: 59
• Publication Date: 2019-08-15
• Subjects: fuzzy Q-learning -- FQL -- fuzzy inference system -- fusing behaviours -- mobile robot -- Pioneer 2P -- unknown environment -- temporal difference -- reinforcement learning; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

In this paper, we propose an approach of fusing the fuzzy control actions of the obstacle avoidance and goal-seeking which utilises fuzzy logic and reinforcement learning for navigation of a mobile robot in unknown environments. The proposed reactive navigator consists of three modules: move to goal, obstacle avoidance, and fuzzy behaviour supervisor. The selection of the actions available in each fuzzy rule is learned through reinforcement learning (Q-learning algorithm). A new and powerful method is used to construct automatically these rules. The experiments carried out on the Pioneer 2P mobile robot have shown that the navigator is able to perform a successful navigation task in various unknown environments with smooth action and exceptionally good robustness.