A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment

Xiaohua Gao, Lei Wang, Xichao Su, Chen Lu, Yu Ding, Chao Wang (), Haijun Peng and Xinwei Wang ()
Additional contact information
Xiaohua Gao: School of Mathematical Science, Dalian University of Technology, Dalian 116024, China
Lei Wang: School of Mathematical Science, Dalian University of Technology, Dalian 116024, China
Xichao Su: Department of Airborne Vehicle Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China
Chen Lu: Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
Yu Ding: Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
Chao Wang: Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
Haijun Peng: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China
Xinwei Wang: State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China

Mathematics, 2022, vol. 10, issue 22, 1-24

Abstract: This paper focuses on cooperative multi-task assignment and re-assignment problems when multiple unmanned aerial vehicles (UAVs) attack multiple known targets. A unified multi-objective optimization framework for UAV cooperative task assignment and re-assignment is studied in this paper. In order to simultaneously optimize the losses and benefits of the UAVs, we establish a multi-objective optimization model. The amount of tasks that each UAV can perform and the number of attacks on each target are limited according to the ammunition capacity of each UAV and the value of each target. To solve this multi-objective optimization problem, a multi-objective genetic algorithm suitable for UAV cooperative task assignment is constructed based on the NSGA-II algorithm. At the same time, a selection strategy is used to assist decision-makers in choosing one or more solutions from the Pareto-optimal front. Moreover, to deal with emergencies such as UAV damage and to detect of new targets, a task re-assignment algorithm based on the contract network protocol (CNP) is developed. It can be implemented in real-time while only slightly sacrificing the ability to seek the optimal solution. Simulation results demonstrate that the methods developed in this paper are effective.

Keywords: unmanned aerial vehicle; cooperative task assignment; multi-objective optimization; genetic algorithm; contract network protocol (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/22/4241/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/22/4241/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:22:p:4241-:d:971231

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().