Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

• Published in: Journal of Central South University Vol. 27; no. 2; pp. 432 - 448
• Main Authors: Wang, Jian-feng, Jia, Gao-wei, Lin, Jun-can, Hou, Zhong-xi
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01-02-2020; Springer Nature B.V
• Subjects: Adaptive control; Algorithms; Completion time; Computer simulation; Engineering; Evolutionary design method; Metallic Materials; Multilayers; Multiple objective analysis; Optimization; Particle swarm optimization; Unmanned aerial vehicles; unmanned aerial vehicles; solution evaluation method; 模型约束; 无人机; 异构; constraint; 解评估方法; multi-objective optimization; 协同任务分配; 多目标优化; heterogeneous; cooperative task allocation
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-020-4307-0

The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time-window, variant equipment, specified execution sequence. Hence, a proper task allocation in UAVs is the crucial point for the final success. The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics. To this end, a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints. In addition, four optimization objectives: completion time, target reward, UAV damage, and total range, are introduced to evaluate various allocation plans. Subsequently, to efficiently solve the multi-objective optimization problem, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) algorithm is proposed. During this algorithm, a modified solution evaluation method is designed to guide algorithmic evolution; both the convergence and distribution of particles are considered comprehensively; and boundary solutions which may produce some special allocation plans are preserved. Moreover, adaptive parameter control and mixed update mechanism are also introduced in this algorithm. Finally, both the proposed model and algorithm are verified by simulation experiments.