Multi-UAVs tracking target in urban environment by model predictive control and Improved Grey Wolf Optimizer

In this paper, based on the Model Predictive Control (MPC) and Improved Grey Wolf Optimizer (IGWO), a hybrid method is proposed to plan the optimal trajectories of multi-UAVs for target tracking in urban environment. Firstly, the target tracking problem in urban environment is modeled detailedly by formulating the visibility region, the sensor coverage area, the restricted region, the space constraints, etc. Based on the model of target tracking, the centralized MPC method is then adopted as the framework to obtain the trajectories of UAVs in real time, the objective of which is to optimize tracking performance under various constraints. In consideration of the computational complexity of this problem, IGWO, a novel intelligent algorithm with the advantages of good stability and strong search ability is utilized to solve the MPC formulation. It imitates the social hierarchy and predatory strategy of wolf pack, and some improvement strategies are also introduced e.g. the individual memory and the principle of survival of the fittest. Finally, the proposed method is demonstrated in a simulated urban environment. The simulation results show the effectiveness of the proposed hybrid method to solve target tracking problem.