Multi-Robot Task Allocation Games in Dynamically Changing Environments

Shinkyu Park, Yaofeng Desmond Zhong, Naomi Ehrich Leonard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


We propose a game-theoretic multi-robot task allocation framework that enables a large team of robots to optimally allocate tasks in dynamically changing environments. As our main contribution, we design a decision-making algorithm that defines how the robots select tasks to perform and how they repeatedly revise their task selections in response to changes in the environment. Our convergence analysis establishes that the algorithm enables the robots to learn and asymptotically achieve the optimal stationary task allocation. Through experiments with a multi-robot trash collection application, we assess the algorithm's responsiveness to changing environments and resilience to failure of individual robots.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781728190778
StatePublished - Jan 1 2021
Externally publishedYes


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