Abstract: This paper proposed a local Nash optimal based distributed search decision method in the frame of distributed model predictive control (DMPC). To consider the interaction between the UAVs, a graph theory based multi-UAVs cooperative model was constructed, which was based on artificial potential field (AFP) cooperative mechanism. It proposed a connected component based hierarchical structure that decomposes the complex optimization problem into smaller, more manageable sub-problems, to reduce the computational complex and communication burden. In this approach, a decision priority sequence is determined by node output degree. According to the decision priority, the paper proposed three decision forms: symmetry, leader-follower(LF) and symmetry-LF form. The corresponding game models were generated. The MPC and particle swarm optimization (PSO) based method was implemented to solve the individual UAV decision making. The simulations show that this is potentially a good method for solving cooperative search problem involving a large number of vehicles with robust performance.  

Key words: unmanned aerial vehicle (UAV), cooperative search, artificial potential field, graph theory, game theory, local Nash optimal, decision priority order