Experimental demonstration of multi-agent learning and planning under uncertainty for persistent missions with automated battery management

This paper presents algorithms and ight test results for multi-agent cooperative planning problems in presence of state-correlated uncertainty.An online learning and planning framework is used to address the problem of improving planner performance for missions with state-dependent uncertain agent health dynamics. The framework includes a previously introduced Decentralized Multi-agent Markov decision process (Dec-MMDP) as an online planning algorithm that is scalable in number of agents, and Incremental Feature Discovery (iFDD) which is a compact and fast learning algorithm for estimating parameters of a state-correlated uncertainty model. In combination, this architecture yield an integrated learning-planning algorithm where the planning performance improves as uncertainty is reduced through learning. The presented algorithms are validated in a persistent search and track scenario with a novel automated battery swapping/recharging system that enables the UAVs to collaboratively track targets over durations that are significantly larger than individual vehicle endurance with a single battery. The results indicate that the architecture can be used as an computationally effcient solution to multi-agent uncertain cooperative planning problems.