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. (C) 2018 Elsevier Masson SAS. All rights reserved.