arrow
Return

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

delete2020-04-13
delete49
PRE
AI
J
Jianfeng Wang
G
Gaowei Jia *
J
Juncan Lin
Z
Zhongxi Hou
DOI:10.1007/s11771-020-4307-0delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
unmanned aerial vehicles
cooperative task allocation
heterogeneous
constraint
multi-objective optimization
solution evaluation method
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Journal of Central South University cover
Journal of Central South University
IF:
4.4
Papers:
5.2K
Citations:
1.0W

Organization

N
national university of defense technology - china
Scholars:
1.8W
Papers: 1.4W
Citations: 9
Cited Papers

Cited Papers

Push and pull search for solving constrained multi-objective optimization problems
err2019-02-01
err342
errOAAI
errFan, Zhun; Li, Wenji; Cai, Xinye; Li, Hui; Wei, Caimin; Zhang, Qingfu; Deb, Kalyanmoy; Goodman, Erik
errShare
errSave
Comparing epidemic tuberculosis in demographically distinct heterogeneous populations
err2002-11-01
err0
PREAI
errBrian M. Murphy; Benjamin H. Singer; Shoana Anderson; Denise Kirschner
errShare
errSave
Intraperitoneal Polypropylene Mesh Hernia Repair Complicates Subsequent Abdominal Surgery
err2006-12-01
err0
PREAI
errJ. A. Halm; L. L. de Wall; E. W. Steyerberg; J. Jeekel; J. F. Lange
errShare
errSave
errShare
errSave
errShare
errSave
researcher View more