Decentralized UAV Swarm Control for Multitarget Tracking using Approximate Dynamic Programming

We develop a decentralized control method for a UAV swarm for a multitarget tracking application using the theory of decentralized Markov decision processes (Dec-MDPs). This study develops a UAV control strategy to maximize the overall target tracking performance in a decentralized setting. Motivation for this case study comes from the surveillance applications using UAV swarms. Decision-theoretic approaches are very difficult to solve due to high dimensionality and being computationally expensive. We extend an approximate dynamic programming method called nominal belief-state optimization (NBO) to solve the UAV swarm control problem for target tracking application. We also implement a centralized MDP approach as a benchmark to compare the performance of the Dec-MDP approach.