Decentralized formation shape control of UAV swarm using dynamic programming

Formation control of unmanned aerial vehicles (UAVs) has many applications including target tracking, surveillance, terrain mapping, precision agriculture, etc. Although many centralized control methods (single command center/computer controlling the UAVs) exist, there are no standard decentralized control frameworks in the literature. In this paper, we present a novel UAV swarm formation control approach based on a decision theoretic approach. Specifically, we pose the decentralized swarm motion control problem as a Decentralized Markov Decision Process (Dec-MDP). Here, the objective is to drive the swarm from an initial geographical region to another geographical region where the swarm must lie on a certain geometrical surface (e.g., surface of a sphere). As most decision theoretic formulations suffer from the curse of dimensionality, we adapt an approximate dynamic programming method called nominal belief-state optimization (NBO) to solve the formation control problem approximately. We perform simulation studies in MATLAB to validate the performance of the algorithms.