NDLI: Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem

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Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem

Content Provider	Springer Nature Link
Author	Parker, Chris A. C. Zhang, Hong
Copyright Year	2010
Abstract	When a complex mission must be undertaken, it often can be simplified by dividing it into a sequence of smaller subtasks, which are then completed in order. This strategy implicitly requires a system to recognize the completion of each subtask and make the decision to begin work on the next one. Decentralized multiple-robot systems can tackle many tasks, but their behavior is typified by continuous responses to stimuli. Task sequencing, however, demands a controlled, self-induced phase change in collective behavior—working on one task one moment and then on a different task the next—which is nontrivial for an emergent system. The main contribution of this study is a collective decision-making framework for decentralized multiple-robot systems that enables such a system to cooperatively decide that a current task has been completed and thus focus its attention on the next one in a sequence using only anonymous local communication. Central to the framework is the use of consensus, whereby task sequencing is delayed until a prespecified proportion of a system’s robots agree that the current task is complete, reducing the likelihood of premature decisions. Two low-cost consensus estimation strategies are presented, both of which are practical for the extremely simple robots that are expected to compose large decentralized systems. Experiments in simulation and with real robots demonstrate that the proposed decision-making framework performs as predicted. Although the specific application of collective decision-making in this work is the cooperative task-sequencing problem, the proposed decision-making framework potentially has many additional applications.
Starting Page	199
Ending Page	220
Page Count	22
File Format	PDF
ISSN	19353812
Journal	Swarm Intelligence
Volume Number	4
Issue Number	3
e-ISSN	19353820
Language	English
Publisher	Springer US
Publisher Date	2010-05-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multiple-robot systems Swarms Decentralized control Behavior-based control Decision-making Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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