Applications of DEC-MDPs in Multi-Robot Systems

Applications of DEC-MDPs in Multi-Robot Systems

Aurélie Beynier (University Pierre and Marie Curie, France) and Abdel-Illah Mouaddib (University of Caen, France)
Copyright: © 2014 |Pages: 23
DOI: 10.4018/978-1-4666-4607-0.ch008
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Abstract

Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera. In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decentralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.
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Decentralized Control In Multi-Robot Systems

This section introduces problematics related to the decentralized control of multi-robot systems. Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments.

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