Cooperative Mission Planning for Heterogeneous UAVs with the Improved Multi-objective Quantum-behaved Particle Swarm Optimization Algorithm

Abstract

The cooperative operation of the unmanned aerial vehicle (UAV) is the trend of the application of UAVs. Mission planning is the application basis of UAV formation. The appropriate mission planning can fully utilize the technical potential and increase the application effectiveness. Mission planning should consider several criteria, like the completion time, expected benefit, energy consumption or equipment loss. Hence, it should be treated as a Multi-objective Optimization Problem. Aiming to mission characteristics of heterogeneous UAV formation in anti-radar operations, a mission planning model based on multi-objective optimization is proposed. The appropriate collaborative constraint between UAVs and mission execution is established through multi-layer coding and penalty function. The model is solved by an improved Multi-objective Quantum-Behaved Particle Swarm Optimization. The crossover and mutation of Genetic Algorithm are introduced to enhance the diversity of solutions. Finally, the proposed model and algorithm are verified by simulation experiments.