第3期 楼传炜，等：无人机群目标搜索的主动感知方法 ·583 222-241 on intelligent systems,2016,11(1):27-36. [16]PURBOLINGGA Y.JAZIDIE A,EFFENDI R.Modified [24]薛政钢.基于多群体蚁群算法的多无人机协同搜索方 ant colony algorithm for swarm multi agent exploration 法研究D].开封：河南大学，2018 on target searching in unknown environment[Cl//Proceed- XUE Zhenggang.Research on Multi-UAV cooperative ings of 2019 International Conference of Artificial Intelli- search methods based on multi-colony ant colony al- gence and Information Technology.Yogyakarta,Indone- gorithm[D].Kaifeng:Henan University,2018. sia:IEEE.2019. [25]刘刚，裴红蕾.复合形引导蜂群寻优的无人机航迹多目 [17]LUO Fuyu,WANG Wei,LI Zhe.Multi-unmanned 标规划).机械设计与制造，2020(4)：253-257 vehicle for region traversal search based on ant colony al- LIU Gang,PEI Honglei.Unmanned air vehicle route gorithm[Cl//Proceedings of the 3rd International Sym- multi-object planning based on bee colony algorithm posium on Autonomous Systems.Shanghai,China,2019: guided by complex form[J].Machinery design manu- 329-334. facture,.2020(4):253-257 [18]YUE Wei,XI Yun,GUAN Xianhe.A new searching ap- proach using improved multi-ant colony scheme for 作者简介： multi-UAVs in unknown environments[J].IEEE access, 楼传炜，硕士研究生，主要研究方 2019,7:161094161102. 向为多智能体系统。 [19]BAJCSY R.Active perception[J].Proceedings of the IEEE,1988,76(8):966-1005. [20]AKKA K,KHABER F.Mobile robot path planning using an improved ant colony optimization[J].International journal of advanced robotic systems,2018,15(3):1-7. [21]时浩，田聪玲，任意，等.基于稀疏A*算法的微小型固 葛泉波，研究员，博士生导师，博 定翼无人机航迹规划[.兵工自动化，2021,40(3)： 士，主要研究方向为工程信息融合方 14-18,39. 法及应用、人机混合系统智能评估。 发表学术论文100余篇。 SHI Hao,TIAN Congling,REN Yi,et al.Route planning of small fixed-wing UAV based on sparse A*algorithm[J] Ordnance industry automation,2021,40(3):14-18,39. [22]LUO Qiang,WANG Haibao,ZHENG Yan,et al.Re- search on path planning of mobile robot based on im- 刘华平，副教授，博士生导师，国 家杰出青年基金获得者、中国人工智 proved ant colony algorithm[J].Neural computing and ap- 能学会理事、中国人工智能学会认知 plications,.2020,32(6:1555-1566. 系统与信息处理专业委员会秘书长 [23]夏小云，周育人.蚁群优化算法的理论研究进展[).智 主要研究方向为机器人感知、学习与 能系统学报，2016,11(1)27-36. 控制、多模态信息融合。主持国家自 XIA Xiaoyun,ZHOU Yuren.Advances in theoretical re- 然科学基金重点项目2项。发表学术 search of ant colony optimization[J].CAAl transactions 论文340余篇。222–241. PURBOLINGGA Y, JAZIDIE A, EFFENDI R. Modified ant colony algorithm for swarm multi agent exploration on target searching in unknown environment[C]//Proceed￾ings of 2019 International Conference of Artificial Intelli￾gence and Information Technology. Yogyakarta, Indone￾sia: IEEE, 2019. [16] LUO Fuyu, WANG Wei, LI Zhe. Multi-unmanned vehicle for region traversal search based on ant colony al￾gorithm[C]//Proceedings of the 3rd International Sym￾posium on Autonomous Systems. Shanghai, China, 2019: 329−334. [17] YUE Wei, XI Yun, GUAN Xianhe. A new searching ap￾proach using improved multi-ant colony scheme for multi-UAVs in unknown environments[J]. IEEE access, 2019, 7: 161094–161102. [18] BAJCSY R. Active perception[J]. Proceedings of the IEEE, 1988, 76(8): 966–1005. [19] AKKA K, KHABER F. Mobile robot path planning using an improved ant colony optimization[J]. International journal of advanced robotic systems, 2018, 15(3): 1–7. [20] 时浩, 田聪玲, 任意, 等. 基于稀疏 A*算法的微小型固 定翼无人机航迹规划 [J]. 兵工自动化, 2021, 40(3): 14–18, 39. SHI Hao, TIAN Congling, REN Yi, et al. Route planning of small fixed-wing UAV based on sparse A* algorithm[J]. Ordnance industry automation, 2021, 40(3): 14–18, 39. [21] LUO Qiang, WANG Haibao, ZHENG Yan, et al. Re￾search on path planning of mobile robot based on im￾proved ant colony algorithm[J]. Neural computing and ap￾plications, 2020, 32(6): 1555–1566. [22] 夏小云, 周育人. 蚁群优化算法的理论研究进展 [J]. 智 能系统学报, 2016, 11(1): 27–36. XIA Xiaoyun, ZHOU Yuren. Advances in theoretical re￾search of ant colony optimization[J]. CAAI transactions [23] on intelligent systems, 2016, 11(1): 27–36. 薛政钢. 基于多群体蚁群算法的多无人机协同搜索方 法研究 [D]. 开封: 河南大学, 2018. XUE Zhenggang. Research on Multi-UAV cooperative search methods based on multi-colony ant colony al￾gorithm[D]. Kaifeng: Henan University, 2018. [24] 刘刚, 裴红蕾. 复合形引导蜂群寻优的无人机航迹多目 标规划 [J]. 机械设计与制造, 2020(4): 253–257. LIU Gang, PEI Honglei. Unmanned air vehicle route multi-object planning based on bee colony algorithm guided by complex form[J]. Machinery design & manu￾facture, 2020(4): 253–257. [25] 作者简介： 楼传炜，硕士研究生，主要研究方 向为多智能体系统。 葛泉波，研究员，博士生导师，博 士，主要研究方向为工程信息融合方 法及应用、人机混合系统智能评估。 发表学术论文 100 余篇。 刘华平，副教授，博士生导师，国 家杰出青年基金获得者、中国人工智 能学会理事、中国人工智能学会认知 系统与信息处理专业委员会秘书长， 主要研究方向为机器人感知、学习与 控制、多模态信息融合。主持国家自 然科学基金重点项目 2 项。发表学术 论文 340 余篇。 第 3 期 楼传炜，等：无人机群目标搜索的主动感知方法 ·583·