Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem
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.
decision-making multiple-robot systems swarms decentralized control behavior-based control