Robust multi-robot cooperation through dynamic task allocation and precaution routines

In this paper, we present the design and implementation of a multi-robot cooperation framework to collectively execute inter-dependent tasks of an overall complex mission requiring diverse capabilities. Given a heterogeneous team of robots and task dependencies, the proposed framework provides a distributed mechanism for assigning tasks to robots in an order that efficiently completes the mission. The approach is robust to unreliable communication and robot failures. It is a distributed auction-based approach, and therefore scalable. In order to obtain optimal allocations, effective bid evaluations are needed. Additionally to maintain optimality in noisy environments, dynamic re-allocations of tasks are needed as implemented in dynamic task selection and coalition maintenance scheme that we propose. Real-time contingencies are handled by recovery routines, called Plan B precautions in our framework. Here, in this paper, we present performance results of our framework for robustness in simulations that include variable message loss rates and robot failures. Experiments illustrate robustness of our approach against several contingencies.