无人飞行器集群智能调度技术综述

杜永浩; 邢立宁; 蔡昭权

doi:10.16383/j.aas.c170681

无人飞行器集群智能调度技术综述

doi: 10.16383/j.aas.c170681

1.
国防科技大学系统工程学院长沙 410073
2.
惠州学院惠州 516007

基金项目:

国家自然科学基金 61773120

国家自然科学基金 61873328

国家自然科学基金 61772225

国家杰出青年科学基金 61525304

高等学校全国优秀博士学位论文作者专项资金 2014-92

广东省自然科学基金 2018B030311046

广东省自然科学杰出青年基金 2017KZDXM081

湖南省研究生科研创新项目 CX2018B022

详细信息

作者简介:
杜永浩国防科技大学系统工程学院博士研究生. 2017年获国防科技大学硕士学位.主要研究方向为智能优化理论、方法与应用. E-mail: duyonghao15@163.com

蔡昭权惠州学院教授. 2006年获华中科技大学硕士学位.主要研究方向为计算机网络, 智能计算和数据库. E-mail: 13502279833@126.com

通讯作者:
邢立宁国防科技大学系统工程学院研究员, 全国优秀博士学位论文获得者, 入选教育部新世纪优秀人才计划, 湖南省自然科学杰出青年基金获得者. 2009年获国防科技大学博士学位.主要研究方向为智能优化理论、方法与应用.本文通信作者. E-mail: xinglining@gmail.com

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出版历程
- 收稿日期: 2017-11-30
- 录用日期: 2019-03-28
- 刊出日期: 2020-03-06

Survey on Intelligent Scheduling Technologies for Unmanned Flying Craft Clusters

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073
2.
Huizhou University, Huizhou 516007

Funds:

Supported by National Natural Science Foundation of China 61773120

Supported by National Natural Science Foundation of China 61873328

Supported by National Natural Science Foundation of China 61772225

National Science Fund for Distinguished Young Scholars 61525304

National Excellent Doctoral Dissertation Foundation of China 2014-92

Natural Science Foundation of Guangdong 2018B030311046

Foundation for Distinguished Young Talents in Higher Education of Guangdong 2017KZDXM081

Hunan Postgraduate Research Innovation Project CX2018B022

More Information

Author Bio:
DU Yong-Hao Ph. D. candidate at the College of Systems Engineering, National University of Defense Technology. He received his master degree from National University of Defense Technology in 2017. His research interest covers intelligent optimization theory, method and application

CAI Zhao-Quan Professor at Huizhou University. He received his master degree from Huazhong University of Science and Technology in 2006. His research interest covers computer networks, intelligent computing and database

Corresponding author: XING Li-Ning Professor at the College of Systems Engineering, National University of Defense Technology. He was awarded with the National Excellent Ph. D. Dissertation of China and the New Century Excellent Researcher of Ministry of Education. He is also supported by the Natural Science Funds for Distinguished Young Scholar of Hunan Province. He received his Ph. D. degree from National University of Defense Technology in 2009. His research interest covers intelligent optimization theory, method and application. Corresponding author of this paper

摘要

摘要: 随着飞行器技术的快速发展, 以无人机和卫星为代表的无人飞行器在集群任务中得到广泛应用, 但日益增长的多样化任务需求和不平衡、不充足的任务资源也对无人飞行器集群调度技术提出新的挑战.针对无人飞行器任务类型特点, 分别从无人机群和多星两个角度出发, 介绍了无人机群访问、打击和察打一体化任务调度技术进展, 阐述了多星成像、数传与天地一体化任务调度研究成果.同时, 梳理了无人机群和多星任务调度问题的主要约束与收益指标, 综述了无人机群和多星任务调度常用的智能优化算法.最后, 面向未来无人飞行器技术应用需求, 指出了无人飞行器集群智能调度技术进一步的研究方向.
- 无人机群 /
- 多星 /
- 任务调度 /
- 约束条件 /
- 智能优化算法
Abstract: With the rapid development of flying craft technologies, unmanned flying craft, including unmanned aerial vehicles and satellites, have been widely applied. However, the increasing demands for diversified missions, as well as the unbalanced and insufficient resources, put great challenges to intelligent scheduling technologies for unmanned flying craft clusters. Considering the characteristics of unmanned flying craft clusters, this paper classifies the mission scheduling into unmanned aerial vehicles scheduling and multi-satellites scheduling. The research progresses on the visit, the attack and the integrated mission scheduling of unmanned aerial vehicles are introduced, and the advances on the observation, the transmission and the integrated mission scheduling of multi-satellites are illustrated. The major constraints and frequently-used intelligent optimization algorithms applied to the unmanned aerial vehicles and multi-satellites scheduling are also reviewed. Finally, several future research priorities about the intelligent scheduling technologies for unmanned flying craft clusters are proposed.
- Unmanned aerial vehicles /
- multi-satellites /
- mission scheduling /
- constraint condition /
- intelligent optimization algorithm
Recommended by Associate Editor SUN Fu-Chun
注释:

1) 本文责任编委孙富春

HTML全文

图 1 无人飞行器集群任务分类图

Fig. 1 Mission classification of unmanned flying craft clusters

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图 2 覆盖任务的优化策略

Fig. 2 Optimization strategy for coverage missions

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图 3 无人机群智能调度问题常见约束

Fig. 3 Common constraints in intelligent scheduling for unmanned aerial vehicles

下载: 全尺寸图片幻灯片

图 4 敏捷卫星与传统卫星成像策略对比

Fig. 4 Comparison on observation strategies between agile satellites and traditional satellites

下载: 全尺寸图片幻灯片

图 5 多星智能调度问题常见约束

Fig. 5 Common constraints in intelligent scheduling for multi-satellites

下载: 全尺寸图片幻灯片

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