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

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...

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Main Authors: Xiaohua Gao, Lei Wang, Xichao Su, Chen Lu, Yu Ding, Chao Wang, Haijun Peng, Xinwei Wang
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/22/4241
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author Xiaohua Gao
Lei Wang
Xichao Su
Chen Lu
Yu Ding
Chao Wang
Haijun Peng
Xinwei Wang
author_facet Xiaohua Gao
Lei Wang
Xichao Su
Chen Lu
Yu Ding
Chao Wang
Haijun Peng
Xinwei Wang
author_sort Xiaohua Gao
collection DOAJ
description 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.
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spelling doaj.art-61b99ce9859740ce821b59ae73a4dc872023-11-24T09:08:15ZengMDPI AGMathematics2227-73902022-11-011022424110.3390/math10224241A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-AssignmentXiaohua Gao0Lei Wang1Xichao Su2Chen Lu3Yu Ding4Chao Wang5Haijun Peng6Xinwei Wang7School of Mathematical Science, Dalian University of Technology, Dalian 116024, ChinaSchool of Mathematical Science, Dalian University of Technology, Dalian 116024, ChinaDepartment of Airborne Vehicle Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaScience and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, ChinaScience and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, ChinaScience and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, ChinaThis 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.https://www.mdpi.com/2227-7390/10/22/4241unmanned aerial vehiclecooperative task assignmentmulti-objective optimizationgenetic algorithmcontract network protocol
spellingShingle Xiaohua Gao
Lei Wang
Xichao Su
Chen Lu
Yu Ding
Chao Wang
Haijun Peng
Xinwei Wang
A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
Mathematics
unmanned aerial vehicle
cooperative task assignment
multi-objective optimization
genetic algorithm
contract network protocol
title A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
title_full A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
title_fullStr A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
title_full_unstemmed A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
title_short A Unified Multi-Objective Optimization Framework for UAV Cooperative Task Assignment and Re-Assignment
title_sort unified multi objective optimization framework for uav cooperative task assignment and re assignment
topic unmanned aerial vehicle
cooperative task assignment
multi-objective optimization
genetic algorithm
contract network protocol
url https://www.mdpi.com/2227-7390/10/22/4241
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