Fast and coupled solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles

This paper studies a problem in which a fleet of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) must identify the optimal flyable trajectory to traverse over multiple targets and perform consecutive tasks. To obtain a fast and feasible solution, a coupled and distributed planning method is developed that integrates the task assignment and trajectory generation aspects of the problem. With specific constraints and a relaxed Dubins path, the cooperative mission-planning problem is reformulated. A distributed genetic algorithm is then proposed to search for the optimal solution, and chromosomal genes are modified to adapt to the heterogeneous characteristic of UAVs. Then, a fixed-wing UAV model with 6 degrees of freedom (DOF) and a path-following method is used to verify this proposed mission-planning method. The simulation results show that the proposed approach obtains feasible solutions and significantly improves the operating rate, with the potential for use in a real mission.