Task planning of multiple UAVs with simultaneous arrival constraints

This paper addresses the problem of task execution for unmanned aerial vehicles (UAV) swarms, considering the coupling characteristics of UAV task allocation and trajectory planning as well as the no-fly zone constraints. A task planning algorithm is proposed that can make the UAV swarm reach the target positions in the shortest time simultaneously. A "hovering waiting and dynamic speed adjustment" method is used to synchronize the arrival time of each UAV, Dubins curves are used to design the pathways, and an upgraded particle swarm optimization (PSO) algorithm with particle swarm mutation is used to optimize the task allocation scheme. Finally, the effectiveness of the algorithm is evaluated and verified in a simulation environment based on the six-degree-of-freedom dynamics model and the dynamic inverse control model. In contrast to the conventional PSO algorithm approach, the simulation results demonstrate that this enhanced PSO algorithm is capable of successfully escaping the local optimum and achieving a better allocation scheme. Under the control of the proposed algorithm, the maximum deviation of flight time among multiple UAVs is only 0.5%, meeting the requirements of a saturation attack.