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We present an ensemble framework for the design of distributed control and communication strategies for the dynamic allocation of a team of robots to a set of tasks. In this work, we assume individual robot controllers are sequentially composed of individual task controllers. This assumption enables the representation of the robot ensemble dynamics as a class of stochastic hybrid systems that can be modeled as continuous-time Markov jump processes where feedback strategies can be derived to control the team's distribution across the tasks. Since the distributed implementation of these feedback strategy requires the estimation of certain population variables, we show how the ensemble model can be expanded to incorporate the dynamics of the information exchange. This then enables us to optimize the individual robot control policies to ensure overall system robustness given some likelihood of resource failures. We consider the assignment of a team of homogeneous robots to a collection of spatially distributed tasks and validate our approach via high-fidelity simulations.

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