Experimental Demonstration of Multi-Agent Learning and Planning under Uncertainty for Persistent Missions with Automated Battery Management
Nazım Kemal Üre, Tuna Toksoz, Girish Vinayak Chowdhary, Joshua Redding, Jonathan P. How, Matthew A. Vavrina, John L. Vian · 2012
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 signicantly larger than individual vehicle endurance with a single battery. The results indicate that the architecture can be used as an computationally ecient solution to multi-agent uncertain cooperative planning problems.