NDLI: Reinforcement learning based group navigation approach for multiple autonomous robotic system

Content Provider	IEEE Xplore Digital Library
Author	Azouaoui, O. Cherifi, A. Bensalem, R. Farah, A.
Copyright Year	2005
Description	Author affiliation: Laboratoire de Robotique et d'Intelligence Artificielle, Centre de Developpement des Technol. Avancees, Algiers, Algeria (Azouaoui, O.; Cherifi, A.; Bensalem, R.)
Abstract	In several complex applications, the use of multiple autonomous robotic systems (ARS) becomes necessary to achieve different tasks such as foraging and transport of heavy and large objects with less cost and more efficiency. They have to achieve a high level of flexibility, adaptability and efficiency in real environments. In this paper, a reinforcement learning (RL) based group navigation approach for multiple ARS is suggested. Indeed, the robots must have the ability to form geometric figures and navigate without collisions while maintaining the formation. Thus, each robot must learn how to take its place in the formation and avoid obstacles and other ARS from its interaction with the environment. This approach must provide ARS with capability to acquire the group navigation approach among several ARS from elementary behaviors by learning with trial and error search. Then, simulation results display the ability of the suggested approach to provide ARS with capability to navigate in a group formation in dynamic environments.
Starting Page	1539
Ending Page	1544
File Size	204353
Page Count	6
File Format	PDF
ISBN	078039044X
DOI	10.1109/ICMA.2005.1626784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Navigation Robot sensing systems Orbital robotics Space exploration Humans Intelligent robots Application software Costs Displays
Content Type	Text
Resource Type	Article

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