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

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, severa...

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Vydáno v:	Journal of Central South University Ročník 27; číslo 2; s. 432 - 448
Hlavní autoři:	Wang, Jian-feng, Jia, Gao-wei, Lin, Jun-can, Hou, Zhong-xi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Changsha Central South University 01.02.2020 Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Lin, Jun-can Hou, Zhong-xi Jia, Gao-wei Wang, Jian-feng
Author_xml	– sequence: 1 givenname: Jian-feng surname: Wang fullname: Wang, Jian-feng organization: College of Aerospace Science and Engineering, National University of Defense Technology – sequence: 2 givenname: Gao-wei orcidid: 0000-0001-5786-6436 surname: Jia fullname: Jia, Gao-wei email: ji_as@126.com organization: College of Aerospace Science and Engineering, National University of Defense Technology – sequence: 3 givenname: Jun-can surname: Lin fullname: Lin, Jun-can organization: College of Aerospace Science and Engineering, National University of Defense Technology – sequence: 4 givenname: Zhong-xi surname: Hou fullname: Hou, Zhong-xi organization: College of Aerospace Science and Engineering, National University of Defense Technology
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Keywords	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
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Snippet	The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and...
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SubjectTerms	Adaptive control Algorithms Completion time Computer simulation Engineering Evolutionary design method Metallic Materials Multilayers Multiple objective analysis Optimization Particle swarm optimization Unmanned aerial vehicles
Title	Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm
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