Robust Multi-Robot Coordination in Noisy and Dangerous Environments
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In this paper we present the design and implementation of a complete framework for multi-robot coordination in which robots collectively execute inter-dependent tasks of an overall complex mission requiring diverse capabilities. Given a heterogeneous team of robots and task dependencies, proposed framework provides a distributed, robust mechanism for assigning robots to tasks in an order that efficiently completes the mission. The approach is robust to unreliable communication and robot failures. It is a market-based approach, and therefore scalable, but it does not provide guarantees of optimality. In order to obtain optimum allocations in noisy environments we introduce a coalition maintenance scheme for dynamic reconfiguration of the assigned tasks at run time. Additional routines, called precautions are added in the framework for addressing different types of failures common in robot systems and solving conflicts in cases of these failures. Framework has been tested in simulations that include variable message loss rates and robot failures. We expect to port the system to mobile robots in the future. Our experiments illustrate effectiveness of the proposed approach in realistic scenarios.