Robust Distributed Coordination of Heterogeneous Robots through Temporal Plan Networks

Real-world applications of autonomous agents require coordinated groups to work in collaboration. Dependable systems must plan and carry out activities in a way that is robust to failure and uncertainty. Previous work has produced algorithms that provide robustness at the planning phase, by choosing between functionally redundant methods, and the execution phase, by dispatching temporally flexible plans. However, these algorithms use a centralized architecture in which all computation is performed by a single processor. As a result, these implementations require significant computational capabilities, introduce a single point of failure, do not scale well, and suffer from communication bottlenecks. This paper introduces the plan extraction component of a robust, distributed executive for contingent plans. Contingent plans are encoded as Temporal Plan Networks (TPNs), which compose temporally flexible plans hierarchically and provide a choose operator. First, the TPN is distributed over multiple agents, by creating a hierarchical ad-hoc network and mapping the TPN onto this hierarchy. Second, candidate plans are extracted from the TPN with a distributed, parallel algorithm that exploits the structure of the TPN. Third, temporal consistency of the candidate plans is tested using a distributed Bellman-Ford algorithm. This algorithm is empirically validated on randomized contingent plans.