Planning to fail: using reliability to improve multirobot task allocation

The reliability of individual robots influences the success of multirobot missions. When one robot fails, others must be retasked to complete the failed robot's tasks. This increases the failure likelihood for these other robots. Existing multirobot task allocation systems consider robot failures only after the fact, via replanning. In this paper we show that mission performance for multirobot missions can be improved by using knowledge of robot failure rates to inform the initial task allocation.

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