Decentralized control of unmanned aerial vehicles for multitarget tracking

We design a guidance control method for a fleet of autonomous unmanned aerial vehicles (UAVs) tracking multiple targets in a decentralized setting. Our method is based on the theory of decentralized partially observable Markov decision process (Dec-POMDP). Like partially observable Markov decision processes (POMDPs), it is intractable to solve Dec-POMDPs exactly. So, we extend a POMDP approximation method called nominal belief-state optimization (NBO) to solve Dec-POMDP. We incorporate the cost of communication into the objective function of Dec-POMDP, i.e., we explicitly optimize the communication among the UAVs along with the kinematic-control commands for the UAVs. We measure the performance of our guidance method with the following metrics: 1) average target-location error, and 2) average communication cost. The goal to maximize the performance with respect to each of the above metrics conflict with each other, and we show through empirical study how to trade off between these performance metrics using a scalar parameter.