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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Engineering Systems Modelling and Simulation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
