A Nash-Based Evolutionary Algorithm for Dynamic Optimization in Multi-target UAV Tracking

Target tracking and path planning using unmanned aerial vehicles (UAVs) have attracted increasing research attention in recent years. The rapid development of communication technology enables the use of multiple UAVs to perform target tracking collaboratively. But it remains challenging to coordinate multiple UAVs in some complicated scenarios, e.g., tracking multiple targets using multiple UAVs. In this paper, we intend to propose a Nash-based evolutionary dynamic optimization algorithm for multi-target tracking using multiple UAVs. Firstly, considering the requirement of balancing the number of UAVs tracking each target, we formulate the tracking problem as a distributed constrained multi-objective dynamic optimization problem using model predictive control (MPC). Secondly, to better track dynamic targets with stochastic behaviors, we design an evolutionary dynamic optimization (EDO) approach to solve the optimization problem. Thirdly, in order to avoid collisions, we combine the EDO approach with Nash optimization. The experimental results show that our approach has better performance than compared algorithms.