Task assignment/trajectory planning for unmanned vehicles via HFLC and PSO

摘要

This paper investigates the problems of task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization is proposed. Initially, teams of UAVs are moving in a pre-determined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target's degree of threat, degree of importance, and the separating distance between each team and each detected target. First, the ground station assigns the teams to the targets based on the gathered information. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Then, each team plans its own path by formulating the path planning problem as an optimization problem, while the objective is to minimize the time to reach their destination considering the UAVs dynamic constraints and the collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve the optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.