Distributed Multi-Agent Target Tracking: A Nash-Combined Adaptive Differential Evolution Method for UAV Systems

Abstract

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories.