Distributed chance-constrained task allocation for autonomous multi-agent teams
Author(s)Ponda, Sameera S.; Johnson, Luke B.; How, Jonathan P.
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This research presents a distributed chanceconstrained task allocation framework that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. The algorithm employs an approximation strategy to convert centralized problem formulations into distributable sub-problems that can be solved by individual agents. A key component of the distributed approximation is a risk adjustment method that allocates individual agent risks based on a global risk threshold. The results show large improvements in distributed stochastic environments by explicitly accounting for uncertainty propagation during the task allocation process.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Proceedings of the 2012 American Control Conference (ACC)
American Automatic Control Council
Ponda, Sameera S., Luke B. Johnson and Jonathan P. How. In 2012 American Control Conference, Fairmont Queen Elizabeth, Montréal, Canada, June 27-June 29, 2012. American Automatic Control Council.
Author's final manuscript