Cooperative area reconnaissance for multi-UAV in dynamic environment

The increasingly complex battlefield environment has put forward higher requirement on Unmanned Aerial Vehicle (UAV) system, where the cooperative area reconnaissance (CAR) is the primary task for multi-UAV. However, the current research results hardly balance the optimality and real-time property. Especially, how to avoid and process emergent threats is rarely considered for UAV formation. So this paper researched on the problems of CAR for multi-UAV in dynamic environment to obtain optimum efficiency on the premise of ensuring real-time. Firstly, the mathematical model and optimization framework were established. Then the idea of Model Predictive Control (MPC) was introduced to process this model and an improved Particle Swarm Optimization (PSO) algorithm based on Simulated Annealing (SA) was proposed to solve the optimization problem. Furthermore, the termination condition of searching was defined and processing strategies in multiple emergent conditions were represented specially. Finally, analysis and comparison of the results from established simulation platform verified that the methods proposed in this paper could control the UAVs avoiding the static and mobile threats effectively, accomplishing task perfectly with more than 90% reconnaissance coverage rate and the run-time of each prediction step was only 1.3892s.