Formation Reconstruction and Trajectory Replanning for Multi-UAV Patrol

This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. Unlike most of the formation flying related work, this article considers a more practical issue that some of the vehicles may break down during operation. In this case, a more reasonable coping strategy is proposed which is to reconstruct the formation such that the task objectives can be achieved optimally. To perform the patrol task, a virtual target is introduced which moves along the patrol path with a predetermined speed. Considering the fact that not all the vehicles have access to the patrol path information, a decentralized estimator is designed for each vehicle to estimate the virtual target state respectively based on which the individual reference trajectories can be generated. As these reference trajectories do not satisfy relevant physical constraints, including system model, control input limits, no-fly zone avoidance, and intervehicle collision avoidance, a novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which is computationally attractive by incorporating a situation-dependent mechanism. Simulations have been conducted to validate the effectiveness of our proposed algorithm.

Automated summary

This article addresses the dynamic formation reconstruction and trajectory replanning problem in the air patrol task using multiple fixed-wing unmanned aerial vehicle formations. A decentralized estimator is designed for each vehicle to estimate the virtual target state, based on which the individual reference trajectories can be generated. A novel model predictive trajectory replanning algorithm is developed to generate feasible reference trajectories for each vehicle in real time, which has been validated through simulations.