生物集群能量高效利用機制研究綜述

吳曉陽; 鄒堯; 付強; 賀威

doi:10.16383/j.aas.c230161

生物集群能量高效利用機制研究綜述

doi: 10.16383/j.aas.c230161

吳曉陽^{1, 2, 3,},
鄒堯^{1, 2, 3,},
付強^{1, 2, 3,},
賀威^{1, 2, 3,}

1.
北京科技大學智能科學與技術學院北京 100083
2.
北京科技大學人工智能研究院北京 100083
3.
北京科技大學智能仿生無人系統教育部重點實驗室北京 100083

基金項目: 國家自然科學基金(62225304, 61933001, 62073028, 62173031), 中央高?；究蒲袠I務費專項資金(FRF-TP-22-003C2) 資助

詳細信息

作者簡介:
吳曉陽：北京科技大學智能科學與技術學院博士研究生. 2017年獲得河北工業大學學士學位. 2020年獲得北京科技大學碩士學位. 主要研究方向為撲翼飛行機器人和飛行器控制. E-mail: wxy1995_jz@163.com

鄒堯：北京科技大學智能科學與技術學院教授. 2010年獲得大連理工大學學士學位, 2016年獲得北京航空航天大學博士學位. 主要研究方向為飛行器控制, 多智能體系統. E-mail: zouyao@ustb.edu.cn

付強：北京科技大學智能科學與技術學院副教授. 2009年獲得北京交通大學學士學位, 2016年獲得北京航空航天大學博士學位. 主要研究方向為視覺導航, 視覺伺服和撲翼飛行機器人. 本文通信作者. E-mail: fuqiang@ustb.edu.cn

賀威：北京科技大學智能科學與技術學院教授. 2006年獲得華南理工大學自動化學院學士學位, 2011年獲得新加坡國立大學電氣工程與計算機科學系博士學位. 主要研究方向為仿生撲翼飛行機器人, 智能無人系統和智能控制. E-mail: weihe@ieee.org

計量
- 文章訪問數: 455
- HTML全文瀏覽量: 202
- PDF下載量: 190
- 被引次數: 0
出版歷程
- 收稿日期: 2023-03-28
- 錄用日期: 2023-07-27
- 網絡出版日期: 2023-12-28
- 刊出日期: 2024-03-29

An Overview of Energy Efficient Utilization Mechanism of Biological Colonies

WU Xiao-Yang^{1, 2, 3
,},
ZOU Yao^{1, 2, 3
,},
FU Qiang^{1, 2, 3
,},
HE Wei^{1, 2, 3
,}

1.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083
3.
Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (62225304, 61933001, 62073028, 62173031) and Fundamental Research Funds for the Central Universities (FRF-TP-22-003C2)

More Information

Author Bio:
WU Xiao-Yang　Ph.D. candidate at the School of Intelligence Science and Technology, University of Science and Technology Beijing. He received his bachelor degree from Hebei University of Technology in 2017, and his master degree from University of Science and Technology Beijing in 2020. His research interest covers flapping-wing aerial vehicles and control of air vehicles

ZOU Yao　Professor at the School of Intelligence Science and Technology, University of Science and Technology Beijing. He received his bachelor degree from Dalian University of Technology in 2010, and his Ph.D. degree from Beihang University in 2016. His research interest covers control of air vehicles and multi-agent system

FU Qiang　Associate professor at the School of Intelligence Science and Technology, University of Science and Technology Beijing. He received his bachelor degree from Beijing Jiaotong University in 2009, and his Ph.D. degree from Beihang University in 2016. His research interest covers vision-based navigation, visual servoing, and flapping-wing aerial vehicles. Corresponding author of this paper

HE Wei　Professor at the School of Intelligence Science and Technology, University of Science and Technology Beijing. He received his bachelor degree from College of Automation Science and Engineering, South China University of Technology (SCUT) in 2006, and his Ph.D. degree from Department of Electrical and Computer Engineering, National University of Singapore (NUS), Singapore in 2011. His research interest covers flapping-wing aerial vehicles, intelligent unmanned system, and intelligent control

摘要

摘要: 近年來, 智能體集群的能量高效利用(Energy efficient utilization, EEU)機制已經成為多智能體系統領域的熱點問題, 如何使用有限的能量資源實現系統性能最優是該問題的核心研究內容. 考慮到智能體集群與生物族群的相似性, 探究生物族群的能量高效利用機制對提升智能體集群節能性能有著重要的研究價值. 為此, 首先介紹不同生物族群中蘊含的能量利用機制, 并根據節能方式的差異分成3類, 流體優勢利用機制、流體阻礙克服機制和熱量交換與擴散機制; 然后對這些機制進行總結與分析, 并提出一種具有一般性的能量高效利用模型; 最后, 探討能量高效利用機制在多智能體系統應用中面臨的挑戰和發展趨勢.
- 生物集群 /
- 能量高效利用 /
- 節能機制 /
- 編隊
Abstract: The energy efficient utilization (EEU) mechanism of agent clusters has become a hot topic in the multi-agent system field. The core research content of this topic is how to use limited energy resources to optimize multi-agent system performance. Considering the similarity between the agent clusters and the biological colonies, exploring the energy efficient utilization mechanism of biological colonies has important research value in improving the energy utilization performance of intelligent agent clusters. Firstly, this paper introduces the energy utilization mechanism of multiple biological colonies, and classifies them according to the differences in energy saving methods, fluid advantage utilization mechanism, fluid obstacle overcoming mechanism and heat exchange and diffusion mechanism. Then these mechanisms are summarized and analyzed, and a general model of efficient energy utilization is proposed. Finally, the challenges and development trends of energy efficient utilization mechanisms in multi-agent applications are discussed.
- Biological colonies /
- energy efficient utilization (EEU) /
- energy-saving mechanism /
- formation
注釋:

1) 1¹ 本文中流體是指生物族群長期生存的液體(海水)和氣體(空氣).

HTML全文

圖 1 遷徙鳥群的線性編隊方式

Fig. 1 Line formation of migratory birds

下載: 全尺寸圖片幻燈片

圖 2 鳥群編隊的誘導阻力比率^[27]

Fig. 2 Induced power ratio of different formation flight^[27]

下載: 全尺寸圖片幻燈片

圖 3 相鄰鳥類間的“翼尖間距”、“深度”和“撲翼相位差”定義

Fig. 3 Definitions of “wing tip spacing”, “depth” and “flapping wing phase difference”

下載: 全尺寸圖片幻燈片

圖 4 鳥群“V型”編隊示意圖

Fig. 4 Bird flock with V-configuration formation

下載: 全尺寸圖片幻燈片

圖 5 鳥群能量利用機制與集群規模$ n $和翼尖間距$ s $的關系

Fig. 5 Relationship between EEU of bird flock and the size $ n $ and wing tip spacing $ s $

下載: 全尺寸圖片幻燈片

圖 6 有鰭魚類的“菱形”編隊

Fig. 6 Diamond formation of finfishs

下載: 全尺寸圖片幻燈片

圖 7 魚群節能區域及節能效果圖

Fig. 7 Energy saving zone and energy saving effect of fish school

下載: 全尺寸圖片幻燈片

圖 8 “菱形”編隊參數示意圖

Fig. 8 Schematic diagram of diamond formation parameters

下載: 全尺寸圖片幻燈片

圖 9 鰻魚游動方式和有鰭魚類游動方式^[28]

Fig. 9 Swimming method of eel and finfishs^[28]

下載: 全尺寸圖片幻燈片

圖 10 “菱形”編隊示意圖

Fig. 10 Diamond formation of diagram

下載: 全尺寸圖片幻燈片

圖 11 EEU實驗結果

Fig. 11 Results of EEU experiment

下載: 全尺寸圖片幻燈片

圖 12 南極磷蝦集群 ((a)不同規模生物群體在聚集和分散情況下的能耗情況^[104]; (b)磷蝦運動時流體擾動的影響^[108]; (c)磷蝦群中不同的編隊方式^[109])

Fig. 12 Krill swarm ((a) Energy consumption of different group in non-swarming and swarming condition^[104]; (b) Hydrodynamic disturbance from the motion of krill^[108]; (c) Different formation method of krill swarm (Focal krill, FK)^[109])

下載: 全尺寸圖片幻燈片

圖 13 不同規模的棘刺龍蝦隊列^[120]

Fig. 13 Different sizes of migrating lobsters^[120]

下載: 全尺寸圖片幻燈片

圖 14 三葉蟲集群((a)首尾相連的三葉蟲隊列^[128]; (b)線性的三葉蟲隊列^130]; (c)非線性的三葉蟲集群^[130])

Fig. 14 Trilobite clusters ((a) Queue with most individuals oriented head-under-tail^[128]; (b) Linear autochthonous trilobite clusters^[130]; (c) Nonlinear trilobite clusters^[130])

下載: 全尺寸圖片幻燈片

圖 15 帝企鵝群的溫度分布^[116]

Fig. 15 Temperature distribution of penguins^[116]

下載: 全尺寸圖片幻燈片

圖 16 擁擠團體EEU隨團體半徑$ r $的變化趨勢

Fig. 16 Relationship between EEU of huddling and radius $ r $

下載: 全尺寸圖片幻燈片

表 1 多圓柱體阻力表

Table 1 Drag coefficients of multi circle cylinders

位置序號	阻力系數
1	1.2158
2	0.4212
3	0.2191
4	0.1069
5	0.0861
6	0.0991

下載: 導出CSV

表 2 多種生物族群的能量高效利用機制總結

Table 2 Summary of energy efficient utilization mechanism in multiple biological clusters

族群種類	能量高效利用機制	實驗數據	集群規模	EEU模型估計節能效果	參考文獻
加拿大鵝	流體優勢利用機制	能耗降低36.0%	55	9.4% ~ 45.3% (根據編隊參數的差異)	[57]
粉紅足雁	流體優勢利用機制	能耗降低14.0%	54	9.4% ~ 47.4% (根據編隊參數的差異)	[59]
白鵜鶘	流體優勢利用機制	能耗降低11.4% ~ 14.0%	8	7.4% ~ 28.9% (根據編隊參數的差異)	[62]
鯖魚	流體優勢利用機制	擺動頻率15.0% ~ 29.0%	—	14.4% ~ 23.0% (根據編隊間距的差異)	[82]
海鱸魚	流體優勢利用機制	擺動頻率9.0% ~ 14.0%	9	14.4% ~ 23.0% (根據編隊間距的差異)	[83]
歐洲擬鯉	流體優勢利用機制	擺動頻率7.3% ~ 11.6%	8	14.4% ~ 23.0% (根據編隊間距的差異)	[54]
鯔魚	流體優勢利用機制	擺動頻率10.5% ~ 27.0%	8	14.4% ~ 23.0% (根據編隊間距的差異)	[87]
鰻魚	流體優勢利用機制	耗氧量30.0%	7	14.4% ~ 23.0% (根據編隊間距的差異)	[96]
南極磷蝦	流體優勢利用機制	耗氧量小7.2倍	—	—	[104]
棘刺龍蝦	流體阻礙克服機制	65.0%阻力減免	19	70.6% (6只組成的隊列)	[117]
三葉蟲	流體阻礙克服機制	—	3	30.6% (2只組成的隊列)	[129]
帝企鵝	熱量交換與擴散機制	能耗降低51.0%	—	最大節能效率不超過55.0%	[138]
嚙齒類動物幼崽	熱量交換與擴散機制	—	100	最大節能效率不超過55.0%	[148?149]

下載: 導出CSV

<button id="qm3rj"><thead id="qm3rj"></thead></button>

亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页

參考文獻(155)

[1]	Saad W, Bennis M, Mozaffari M, Lin X Q. Wireless Communications and Networking for Unmanned Aerial Vehicles. Cambridge: Cambridge University Press, 2020.
[2]	Ding J P, Mei H Y, Chih-Lin I, Zhang H, Liu W W. Frontier progress of unmanned aerial vehicles optical wireless technologies. Sensors, 2020, 20(19): Article No. 5476 doi: 10.3390/s20195476
[3]	Baltaci A, Dinc E, Ozger M, Alabbasi A, Cavdar C, Schupke D. A survey of wireless networks for future aerial communications (FACOM). IEEE Communications Surveys & Tutorials, 2021, 23(4): 2833-2884
[4]	Cai C W, Wu S, Jiang L Y, Zhang Z P, Yang S Y. A 500-W wireless charging system with lightweight pick-up for unmanned aerial vehicles. IEEE Transactions on Power Electronics, 2020, 35(8): 7721-7724 doi: 10.1109/TPEL.2020.2964023
[5]	李敏, 包富瑜, 王恒. 無人機使能的無線傳感網總能耗優化方法. 自動化學報, DOI: 10.16383/j.aas.c220914 Li Min, Bao Fu-Yu, Wang Heng. Optimization of total energy consumption for unmanned aerial vehicle-enabled wireless sensor networks. Acta Automatica Sinica, DOI: 10.16383/j.aas.c220914
[6]	羅小元, 楊帆, 李紹寶, 關新平. 多智能體系統的最優持久編隊生成策略. 自動化學報, 2014, 40(7): 1311-1319 Luo Xiao-Yuan, Yang Fan, Li Shao-Bao, Guan Xin-Ping. Generation of optimally persistent formation for multi-agent systems. Acta Automatica Sinica, 2014, 40(7): 1311-1319
[7]	Shao L Y, Karci A E H, Tavernini D, Sorniotti A, Cheng M. Design approaches and control strategies for energy-efficient electric machines for electric vehicles—A review. IEEE Access, 2020, 8: 116900-116913 doi: 10.1109/ACCESS.2020.2993235
[8]	Jiang B, Huang G S, Wang T, Gui J S, Zhu X Y. Trust based energy efficient data collection with unmanned aerial vehicle in edge network. Transactions on Emerging Telecommunications Technologies, 2022, 33(6): Article No. e3942 doi: 10.1002/ett.3942
[9]	Boursianis A D, Papadopoulou M S, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, et al. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 2022, 18: Article No. 100187 doi: 10.1016/j.iot.2020.100187
[10]	Liu Y L, Dai H N, Wang Q B J, Shukla M K, Imran M. Unmanned aerial vehicle for internet of everything: Opportunities and challenges. Computer Communications, 2020, 155: 66-83 doi: 10.1016/j.comcom.2020.03.017
[11]	Khan M T R, Muhammad Saad M, Ru Y, Seo J, Kim D. Aspects of unmanned aerial vehicles path planning: Overview and applications. International Journal of Communication Systems, 2021, 34(10): Article No. e4827 doi: 10.1002/dac.4827
[12]	Chen J C, Ling F Y, Zhang Y, You T, Liu Y F, Du X Y. Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system. Swarm and Evolutionary Computation, 2022, 69: Article No. 101005 doi: 10.1016/j.swevo.2021.101005
[13]	Cho S W, Park H J, Lee H, Shim D H, Kim S Y. Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations. Computers & Industrial Engineering, 2021, 161: Article No. 107612
[14]	Ahmed F, Mohanta J C, Keshari A, Yadav P S. Recent advances in unmanned aerial vehicles: A review. Arabian Journal for Science and Engineering, 2022, 47(7): 7963-7984 doi: 10.1007/s13369-022-06738-0
[15]	Bouguettaya A, Zarzour H, Taberkit A M, Kechida A. A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Signal Processing, 2022, 190: Article No. 108309 doi: 10.1016/j.sigpro.2021.108309
[16]	Saikin D A, Baca T, Gurtner M, Saska M. Wildfire fighting by unmanned aerial system exploiting its time-varying mass. IEEE Robotics and Automation Letters, 2020, 5(2): 2674-2681 doi: 10.1109/LRA.2020.2972827
[17]	Neupane K, Baysal-Gurel F. Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sensing, 2021, 13(19): Article No. 3841 doi: 10.3390/rs13193841
[18]	Hu H M, Kaizu Y, Huang J J, Furuhashi K, Zhang H D, Li M, et al. Research on methods decreasing pesticide waste based on plant protection unmanned aerial vehicles: A review. Frontiers in Plant Science, 2022, 13: Article No. 811256 doi: 10.3389/fpls.2022.811256
[19]	Jang G, Kim J, Yu J K, Kim H J, Kim Y, Kim D W, et al. Review: Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application. Remote Sensing, 2020, 12(6): Article No. 998 doi: 10.3390/rs12060998
[20]	McNelly B P, Whitcomb L L, Brusseau J P, Carr S S. Evaluating integration of autonomous underwater vehicles into port protection. In: Proceedings of the OCEANS, Hampton Roads. Hampton Roads, USA: IEEE, 2022. 1?8
[21]	Al Abkal S, Talas R, Shaw S, Ellis T. The application of unmanned aerial vehicles in managing port and border security in the US and Kuwait: Reflections on best practice for the UK. International Journal of Maritime Crime & Security (IJMCS), 2020, 1(1): 26-33
[22]	Gorodetsky V, Skobelev P, Marik V. System engineering view on multi-agent technology for industrial applications: Barriers and prospects. Cybernetics and Physics, 2020, 9(1): 13-30
[23]	杜永浩, 邢立寧, 蔡昭權. 無人飛行器集群智能調度技術綜述. 自動化學報, 2020, 46(2): 222-241 Du Yong-Hao, Xing Li-Ning, Cai Zhao-Quan. Survey on intelligent scheduling technologies for unmanned flying craft clusters. Acta Automatica Sinica, 2020, 46(2): 222-241
[24]	Calegari R, Ciatto G, Mascardi V, Omicini A. Logic-based technologies for multi-agent systems: A systematic literature review. Autonomous Agents and Multi-Agent Systems, 2021, 35(1): Article No. 1 doi: 10.1007/s10458-020-09478-3
[25]	Wieselsberger C. Beitrag zur Erklarung des Winkelfluges eineger Zugvogel. Motorluftschiffahrt, 1914, 3(5): 225-229
[26]	Gould L L, Heppner F. The vee formation of Canada geese. The Auk, 1974, 165(11): 494-506
[27]	Lissaman P B S, Shollenberger C A. Formation flight of birds. Science, 1970, 168(3934): 1003-1005 doi: 10.1126/science.168.3934.1003
[28]	Weihs D. Hydromechanics of fish schooling. Nature, 1973, 241(5387): 290-291 doi: 10.1038/241290a0
[29]	Belyayev V V. Zuyev G V. Hydrodynamic hypothesis of school formation in fishes. Problems of Ichthyology, 1969, 9(27): 578-584
[30]	Fish F E. Energetics of swimming and flying in formation. Comments on Theoretical Biology, 1999, 5(35): 283-304
[31]	Le Maho Y. The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin. American Scientist, 1977, 65(6): 680-693
[32]	Ancel A, Visser H, Handrich Y, Masman D, Le Maho Y. Energy saving in huddling penguins. Nature, 1997, 385(6614): 304-305 doi: 10.1038/385304a0
[33]	Herrnkind W. Queuing behavior of spiny lobsters. Science, 1969, 164(3886): 1425-1427 doi: 10.1126/science.164.3886.1425
[34]	Chatterton B D E, Fortey R A. Linear clusters of articulated trilobites from Lower Ordovician (Arenig) strata at Bini Tinzoulin, North of Zagora, southern Morocco. Advances in Trilobite Research. Cuadernos del Museo Geominero, 2008, 24(9): 73-77
[35]	Li W, Wang G G, Gandomi A H. A survey of learning-based intelligent optimization algorithms. Archives of Computational Methods in Engineering, 2021, 28(5): 3781-3799 doi: 10.1007/s11831-021-09562-1
[36]	劉成漢, 何慶. 融合多策略的黃金正弦黑猩猩優化算法. 自動化學報, 2023, 49(11): 2360?2373 Liu Cheng-Han, He Qing. Golden sine chimp optimization algorithm integrating multiple strategies. ACTA AUTOMATICA SINICA, 2023, 49(11): 2360?2373
[37]	李雅麗, 王淑琴, 陳倩茹, 王小鋼. 若干新型群智能優化算法的對比研究. 計算機工程與應用, 2020, 56(22): 1-12 Li Ya-Li, Wang Shu-Qin, Chen Qian-Ru, Wang Xiao-Gang. Comparative study of several new swarm intelligence optimization algorithms. Computer Engineering and Applications, 2020, 56(22): 1-12
[38]	楊旭, 王銳, 張濤. 面向無人機集群路徑規劃的智能優化算法綜述. 控制理論與應用, 2020, 37(11): 2291-2302 Yang Xu, Wang Rui, Zhang Tao. Review of unmanned aerial vehicle swarm path planning based on intelligent optimization. Control Theory & Applications, 2020, 37(11): 2291-2302
[39]	Qiu H X, Duan H B. A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Information Sciences, 2020, 509: 515-529 doi: 10.1016/j.ins.2018.06.061
[40]	Li L, Nagy M, Graving J M, Bak-Coleman J, Xie G M, Couzin I D. Vortex phase matching as a strategy for schooling in robots and in fish. Nature Communications, 2020, 11(1): Article No. 5408 doi: 10.1038/s41467-020-19086-0
[41]	Wu X Y, He W, Wang Q, Meng T T, He X Y, Fu Q. A long-endurance flapping-wing robot based on mass distribution and energy consumption method. IEEE Transactions on Industrial Electronics, 2023, 70(8): 8215-8224 doi: 10.1109/TIE.2022.3213905
[42]	Fu Q, Wang X Q, Zou Y, He W. A miniature video stabilization system for flapping-wing aerial vehicles. Guidance, Navigation and Control, 2022, 2(1): Article No. 2250001 doi: 10.1142/S2737480722500017
[43]	Huang H F, He W, Fu Q, He X Y, Sun C Y. A bio-inspired flapping-wing robot with cambered wings and its application in autonomous airdrop. IEEE/CAA Journal of Automatica Sinica, 2022, 9(12): 2138-2150 doi: 10.1109/JAS.2022.106040
[44]	Huang H F, He W, Wang J B, Zhang L, Fu Q. An all servo-driven bird-like flapping-wing aerial robot capable of autonomous flight. IEEE/ASME Transactions on Mechatronics, 2022, 27(6): 5484-5494
[45]	He W, Mu X X, Zhang L, Zou Y. Modeling and trajectory tracking control for flapping-wing micro aerial vehicles. IEEE/CAA Journal of Automatica Sinica, 2021, 8(1): 148-156 doi: 10.1109/JAS.2020.1003417
[46]	Pennycuick C J, Alerstam T, Hedenstr?m A. A new low-turbulence wind tunnel for bird flight experiments at Lund University, Sweden. Journal of Experimental Biology, 1997, 200(10): 1441-1449 doi: 10.1242/jeb.200.10.1441
[47]	Sachs G, Moelyadi M A. Effect of slotted wing tips on yawing moment characteristics. Journal of Theoretical Biology, 2006, 239(1): 93-100 doi: 10.1016/j.jtbi.2005.07.016
[48]	Kawaguchi S, King R, Meijers R, Osborn J E, Swadling K M, Ritz D A, et al. An experimental aquarium for observing the schooling behaviour of Antarctic krill (Euphausia superba). Deep Sea Research Part II: Topical Studies in Oceanography, 2010, 57(7-8): 683-692 doi: 10.1016/j.dsr2.2009.10.017
[49]	Hamner W M, Hamner P P. Behavior of Antarctic krill (Euphausia superba): Schooling, foraging, and antipredatory behavior. Canadian Journal of Fisheries and Aquatic Sciences, 2000, 57(S3): 192-202 doi: 10.1139/f00-195
[50]	Catton K B, Webster D R, Kawaguchi S, Yen J. The hydrodynamic disturbances of two species of krill: Implications for aggregation structure. Journal of Experimental Biology, 2011, 214(11): 1845-1856 doi: 10.1242/jeb.050997
[51]	Lopez U, Gautrais J, Couzin I D, Theraulaz G. From behavioural analyses to models of collective motion in fish schools. Interface Focus, 2012, 2(6): 693-707 doi: 10.1098/rsfs.2012.0033
[52]	Viscido S V, Parrish J K, Grünbaum D. Individual behavior and emergent properties of fish schools: A comparison of observation and theory. Marine Ecology Progress Series, 2004, 273: 239-249 doi: 10.3354/meps273239
[53]	Tian G Z, Zhang Y S, Feng X M, Hu Y S. Focus on bioinspired textured surfaces toward fluid drag reduction: Recent progresses and challenges. Advanced Engineering Materials, 2022, 24(1): Article No. 2100696 doi: 10.1002/adem.202100696
[54]	Svendsen J C, Skov J, Bildsoe M, Steffensen J F. Intra-school positional preference and reduced tail beat frequency in trailing positions in schooling roach under experimental conditions. Journal of Fish Biology, 2003, 62(4): 834-846 doi: 10.1046/j.1095-8649.2003.00068.x
[55]	Wiese K, Ebina Y. The propulsion jet of Euphausia superba (Antarctic Krill) as a potential communication signal among conspecifics. Journal of the Marine Biological Association of the United Kingdom, 1995, 75(1): 43-54 doi: 10.1017/S0025315400015186
[56]	Berger M. Formationsflug ohne Phasenbeziehung der Flügelschl?ge. Journal für Ornithologie, 1972, 113(2): 161-169
[57]	Hainsworth F R. Precision and dynamics of positioning by Canada geese flying in formation. Journal of Experimental Biology, 1987, 128(1): 445-462 doi: 10.1242/jeb.128.1.445
[58]	Bajec I L, Heppner F H. Organized flight in birds. Animal Behaviour, 2009, 78(4): 777?789
[59]	Cutts C J, Speakman J R. Energy savings in formation flight of pink-footed geese. Journal of Experimental Biology, 1994, 189(1): 251-261 doi: 10.1242/jeb.189.1.251
[60]	Badgerow J P, Hainsworth F R. Energy savings through formation flight? A re-examination of the vee formation. Journal of Theoretical Biology, 1981, 93(1): 41-52 doi: 10.1016/0022-5193(81)90055-2
[61]	Ward S, Bishop C M, Woakes A J, Butler P J. Heart rate and the rate of oxygen consumption of flying and walking barnacle geese (Branta leucopsis) and bar-headed geese (Anser indicus). Journal of Experimental Biology, 2002, 205(21): 3347-3356 doi: 10.1242/jeb.205.21.3347
[62]	Weimerskirch H, Martin J, Clerquin Y, Alexandre P, Jiraskova S. Energy saving in flight formation. Nature, 2001, 413(6857): 697-698 doi: 10.1038/35099670
[63]	Bairlein F, Fritz J, Scope A, Schwendenwein I, Stanclova G, van Dijk G, et al. Energy expenditure and metabolic changes of free-flying migrating northern bald ibis. PLoS One, 2015, 10(9): Article No. e0134433 doi: 10.1371/journal.pone.0134433
[64]	Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz W M. Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: General concepts and tools illustrated for griffon vultures. Journal of Experimental Biology, 2012, 215(6): 986-996 doi: 10.1242/jeb.058602
[65]	Sp?ni D, Arras M, K?nig B, Rülicke T. Higher heart rate of laboratory mice housed individually vs in pairs. Laboratory Animals, 2003, 37(1): 54-62 doi: 10.1258/002367703762226692
[66]	Maeng J S, Park J H, Jang S M, Han S Y. A modeling approach to energy savings of flying Canada geese using computational fluid dynamics. Journal of Theoretical Biology, 2013, 320: 76-85 doi: 10.1016/j.jtbi.2012.11.032
[67]	Kshatriya M, Blake R W. Theoretical model of the optimum flock size of birds flying in formation. Journal of Theoretical Biology, 1992, 157(2): 135-174 doi: 10.1016/S0022-5193(05)80618-6
[68]	Mirzaeinia A, Hassanalian M, Lee K, Mirzaeinia M. Energy conservation of V-shaped swarming fixed-wing drones through position reconfiguration. Aerospace Science and Technology, 2019, 94: Article No. 105398 doi: 10.1016/j.ast.2019.105398
[69]	Nudds R L, Rayner J M V. Scaling of body frontal area and body width in birds. Journal of Morphology, 2006, 267(3): 341-346 doi: 10.1002/jmor.10409
[70]	Pennycuick C J, Fast P L F, Ballerst?dt N, Rattenborg N. The effect of an external transmitter on the drag coefficient of a bird’s body, and hence on migration range, and energy reserves after migration. Journal of Ornithology, 2012, 153(3): 633-644 doi: 10.1007/s10336-011-0781-3
[71]	Cutts C J, Metcalfe N B, Taylor A C. Aggression and growth depression in juvenile atlantic salmon: The consequences of individual variation in standard metabolic rate. Journal of Fish Biology, 1998, 52(5): 1026-1037
[72]	Andersson M, Wallander J. Kin selection and reciprocity in flight formation?. Behavioral Ecology, 2004, 15(1): 158-162 doi: 10.1093/beheco/arg109
[73]	Higdon J J L, Corrsin S. Induced drag of a bird flock. The American Naturalist, 1978, 112(986): 727-744 doi: 10.1086/283314
[74]	Usherwood J R, Stavrou M, Lowe J C, Roskilly K, Wilson A M. Flying in a flock comes at a cost in pigeons. Nature, 2011, 474(7352): 494-497 doi: 10.1038/nature10164
[75]	張天棟, 王睿, 程龍, 王宇, 王碩. 魚集群游動的節能機理研究綜述. 自動化學報, 2021, 47(3): 475-488 Zhang Tian-Dong, Wang Rui, Cheng Long, Wang Yu, Wang Shuo. Research on energy-saving mechanism of fish schooling: A review. Acta Automatica Sinica, 2021, 47(3): 475-488
[76]	Spedding G. The cost of flight in flocks. Nature, 2011, 474(7352): 458-459 doi: 10.1038/474458a
[77]	Breder C M. Ortices and fish schools. Zoologica New York, 1965, 50(16): 97-114
[78]	Weihs D. Some hydrodynamical aspects of fish schooling. Swimming and Flying in Nature. New York: Springer, 1975. 703?718
[79]	Partridge B L, Pitcher T J. Evidence against a hydrodynamic function for fish schools. Nature, 1979, 279(5712): 418-419 doi: 10.1038/279418a0
[80]	Blake R W. Fish Locomotion. Cambridge: Cambridge University Press, 1983.
[81]	Abrahams M V, Colgan P W. Fish schools and their hydrodynamic function: A reanalysis. Environmental Biology of Fishes, 1987, 20(1): 79-80 doi: 10.1007/BF00002028
[82]	Zuyev G V. Belyayev V V. An experimental study of the swimming of fish in groups as examplified by the horsemackerel[Trachurus mediterraneus ponticus Aleev]. Journal of Ichthyology, 1970, 10(21): 545-549
[83]	Herskin J, Steffensen J F. Energy savings in sea bass swimming in a school: Measurements of tail beat frequency and oxygen consumption at different swimming speeds. Journal of Fish Biology, 1998, 53(2): 366-376 doi: 10.1111/j.1095-8649.1998.tb00986.x
[84]	Gerstner C L. Use of substratum ripples for flow refuging by Atlantic cod, Gadus morhua. Environmental Biology of Fishes, 1998, 51(4): 455-460 doi: 10.1023/A:1007449630601
[85]	Webb P W. The effect of solid and porous channel walls on steady swimming of steelhead trout Oncorhynchus mykiss. Journal of Experimental Biology, 1993, 178(1): 97-108 doi: 10.1242/jeb.178.1.97
[86]	Liao J C. A review of fish swimming mechanics and behaviour in altered flows. Philosophical Transactions of the Royal Society B: Biological Sciences, 2007, 362(1487): 1973-1993 doi: 10.1098/rstb.2007.2082
[87]	Marras S, Killen S S, Lindstr?m J, McKenzie D J, Steffensen J F, Domenici P. Fish swimming in schools save energy regardless of their spatial position. Behavioral Ecology and Sociobiology, 2015, 69(2): 219-226 doi: 10.1007/s00265-014-1834-4
[88]	Hemelrijk C, Reid D, Hildenbrandt H, Padding J. The increased efficiency of fish swimming in a school. Fish and Fisheries, 2015, 16(3): 511-521 doi: 10.1111/faf.12072
[89]	Dai L Z, He G W, Zhang X, Zhang X. Stable formations of self-propelled fish-like swimmers induced by hydrodynamic interactions. Journal of the Royal Society Interface, 2018, 15(147): Article No. 20180490 doi: 10.1098/rsif.2018.0490
[90]	Chen S Y, Fei Y H J, Chen Y C, Chi K J, Yang J T. The swimming patterns and energy-saving mechanism revealed from three fish in a school. Ocean Engineering, 2016, 122: 22-31 doi: 10.1016/j.oceaneng.2016.06.018
[91]	Tesch F W, Thorpe J E. The Eel. Oxford: Blackwell Science Ltd, 2003.
[92]	Burgerhout E, Manabe R, Brittijn S A, Aoyama J, Tsukamoto K, van den Thillart G E E J M. Dramatic effect of pop-up satellite tags on eel swimming. Naturwissenschaften, 2011, 98(7): 631-634 doi: 10.1007/s00114-011-0805-0
[93]	Palstra A, van Ginneken V, van den Thillart G. Cost of transport and optimal swimming speed in farmed and wild European silver eels (Anguilla anguilla). Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 2008, 151(1): 37-44
[94]	Sébert P, Scaion D, Belhomme M. High hydrostatic pressure improves the swimming efficiency of European migrating silver eel. Respiratory Physiology & Neurobiology, 2009, 165(1): 112-114
[95]	van Ginneken V, Antonissen E, Müller U K, Booms R, Eding E, Verreth J, et al. Eel migration to the Sargasso: Remarkably high swimming efficiency and low energy costs. Journal of Experimental Biology, 2005, 208(7): 1329-1335 doi: 10.1242/jeb.01524
[96]	Burgerhout E, Tudorache C, Brittijn S A, Palstra A P, Dirks R P, van den Thillart G E E J M. Schooling reduces energy consumption in swimming male European eels, Anguilla anguilla L. Journal of Experimental Marine Biology and Ecology, 2013, 448: 66-71 doi: 10.1016/j.jembe.2013.05.015
[97]	周夢園, 吳君欽, 夏樂, 黃敏. 智能仿生魚系統的設計與實現. 計算機工程與設計, 2022, 43(5): 1467-1476 Zhou Meng-Yuan, Wu Jun-Qin, Xia Le, Huang Min. Design and implementation of intelligent bionic fish system. Computer Engineering and Design, 2022, 43(5): 1467-1476
[98]	秦偉偉, 張建, 左新龍, 唐文獻, 王子航. 基于錦鯉BCF擺動推進特性分析. 科學技術與工程, 2023, 23(8): 3200-3206 Qin Wei-Wei, Zhang Jian, Zuo Xin-Long, Tang Wen-Xian, Wang Zi-Hang. Based on the analysis of the oscillating propulsion characteristics of koi BCF. Science Technology and Engineering, 2023, 23(8): 3200-3206
[99]	李廣浩, 馮娜, 劉貴杰. 基于仿生魚體結構的平板減阻方法. 水下無人系統學報, 2021, 29(1): 80-87 Li Guang-Hao, Feng Na, Liu Gui-Jie. Flat drag reduction method based on biomimetic fish-body structure. Journal of Unmanned Undersea Systems, 2021, 29(1): 80-87
[100]	郝天澤, 肖華平, 劉書海, 張超, 馬豪. 集成化智能軟體機器人研究進展. 浙江大學學報(工學版), 2021, 55(2): 229-243 Hao Tian-Ze, Xiao Hua-Ping, Liu Shu-Hai, Zhang Chao, Ma Hao. Research status of integrated intelligent soft robots. Journal of Zhejiang University (Engineering Science), 2021, 55(2): 229-243
[101]	教柳, 張保成, 張開升, 趙波. 兩關節壓力驅動柔性仿生機器魚的設計與仿真. 力學學報, 2020, 52(3): 817-827 Jiao Liu, Zhang Bao-Cheng, Zhang Kai-Sheng, Zhao Bo. Design and simulation of two-joint pressure-driven soft bionic fish. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(3): 817-827
[102]	Borazjani I, Sotiropoulos F. Numerical investigation of the hydrodynamics of carangiform swimming in the transitional and inertial flow regimes. Journal of Experimental Biology, 2008, 211(10): 1541-1558 doi: 10.1242/jeb.015644
[103]	Gon?alves R T, Hirabayashi S, Suzuki H. Experimental study on flow around an array of four circular cylinders. In: Proceedings of the Techno-Ocean (Techno-Ocean). Kobe, Japan: IEEE, 2016. 660?667
[104]	Ritz D A. Is social aggregation in aquatic crustaceans a strategy to conserve energy? Canadian Journal of Fisheries and Aquatic Sciences, 2000, 57(S3): 59-67 doi: 10.1139/f00-170
[105]	Patria M P, Wiese K. Swimming in formation in krill (Euphausiacea), a hypothesis: Dynamics of the flow field, properties of antennular sensor systems and a sensory-motor link. Journal of Plankton Research, 2004, 26(11): 1315-1325 doi: 10.1093/plankt/fbh122
[106]	Hamner W M. Aspects of schooling in Euphausia superba. Journal of Crustacean Biology, 1984, 4(5): 67-74 doi: 10.1163/1937240X84X00507
[107]	Kanda K, Takagi K, Seki Y. Movement of the larger swarms of Antarctic krill Euphausia superba population off enderby land during 1976-1977 season. Journal of the Tokyo University of Fisheries, 1982, 68(1-2): 25-45
[108]	Yen J, Brown J, Webster D R. Analysis of the flow field of the krill, Euphausia pacifica. Marine and Freshwater Behaviour and Physiology, 2003, 36(4): 307-319 doi: 10.1080/10236240310001614439
[109]	Murphy D W, Olsen D, Kanagawa M, King R, Kawaguchi S, Osborn J, et al. The three dimensional spatial structure of Antarctic krill schools in the laboratory. Scientific Reports, 2019, 9(1): Article No. 381 doi: 10.1038/s41598-018-37379-9
[110]	Burns A L, Schaerf T M, Lizier J, Kawaguchi S, Cox M, King R, et al. Self-organization and information transfer in Antarctic krill swarms. Proceedings of the Royal Society B: Biological Sciences, 2022, 289(1969): Article No. 20212361 doi: 10.1098/rspb.2021.2361
[111]	Tarling G A, Fielding S. Swarming and behaviour in Antarctic krill. Biology and Ecology of Antarctic Krill. Cham: Springer, 2016. 279?319
[112]	Terushkin M, Fridman E. Network-based deployment of nonlinear multi agents over open curves: A PDE approach. Automatica, 2021, 129: Article No. 109697 doi: 10.1016/j.automatica.2021.109697
[113]	Khan M W, Wang J. Multi-agents based optimal energy scheduling technique for electric vehicles aggregator in microgrids. International Journal of Electrical Power & Energy Systems, 2022, 134: Article No. 107346
[114]	Herrnkind W F, Childress M J, Lavalli K L. Cooperative defence and other benefits among exposed spiny lobsters: Inferences from group size and behaviour. Marine and Freshwater Research, 2001, 52(8): 1113-1124 doi: 10.1071/MF01044
[115]	Heeremans O, Rubie E, King M, Oviedo-Trespalacios O. Group cycling safety behaviours: A systematic review. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 91: 26-44 doi: 10.1016/j.trf.2022.09.013
[116]	Pérez-Zuriaga A M, Moll S, López G, García A. Driver behavior when overtaking cyclists riding in different group configurations on two-lane rural roads. International Journal of Environmental Research and Public Health, 2021, 18(23): Article No. 12797 doi: 10.3390/ijerph182312797
[117]	Bill R G, Herrnkind W F. Drag reduction by formation movement in spiny lobsters. Science, 1976, 193(4258): 1146-1148 doi: 10.1126/science.193.4258.1146
[118]	Kanciruk P, Herrnkind W. Mass migration of spiny lobster, Panulirus argus (Crustacea: Palinuridae): Behavior and environmental correlates. Bulletin of Marine Science, 1978, 28(4): 601-623
[119]	Herrnkind W F. Evolution and mechanisms of mass single-file migration in spiny lobster: Synopsis. Contributions in Marine Science, 1985, 27(S1): 197-211
[120]	Herrnkind W F. Spiny lobsters: Patterns of movement. Biology and management of lobsters. Physiology and behavior, 1980, 1(12): 349-407
[121]	Brice?o F A, Polymeropoulos E T, Fitzgibbon Q P, Dambacher J M, Pecl G T. Changes in metabolic rate of spiny lobster under predation risk. Marine Ecology Progress Series, 2018, 598: 71-84 doi: 10.3354/meps12644
[122]	Voelkl B, Portugal S J, Uns?ld M, Usherwood J R, Wilson A M, Fritz J. Matching times of leading and following suggest cooperation through direct reciprocity during V-formation flight in ibis. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(7): 2115-2120
[123]	Waters A, Blanchette F, Kim A D. Modeling huddling penguins. PLoS One, 2012, 7(11): Article No. e50277 doi: 10.1371/journal.pone.0050277
[124]	Corrales-García A, Esteve J, Zhao Y L, Yang X L. Synchronized moulting behaviour in trilobites from the Cambrian Series 2 of South China. Scientific Reports, 2020, 10(1): Article No. 14099 doi: 10.1038/s41598-020-70883-5
[125]	Mángano M G, Buatois L A, Waisfeld B G, Mu?oz D F, Vaccari N E, Astini R A. Were all trilobites fully marine? Trilobite expansion into brackish water during the early Palaeozoic. Proceedings of the Royal Society B: Biological Sciences, 2021, 288(1944): Article No. 20202263 doi: 10.1098/rspb.2020.2263
[126]	Bicknell R D C, Holmes J D, García-Bellido D C, Paterson J R. Malformed individuals of the trilobite Estaingia bilobata from the Cambrian Emu Bay Shale and their palaeobiological implications. Geological Magazine, 2023, 160(4): 803-812 doi: 10.1017/S0016756822001261
[127]	Hou X G, Siveter D J, Aldridge R J, Siveter D J. Collective behavior in an early Cambrian arthropod. Science, 2008, 322(5899): 224-224 doi: 10.1126/science.1162794
[128]	Radwański A, Kin A, Radwańska U. Queues of blind phacopid trilobites trimerocephalus: A case of frozen behaviour of early Famennian age from the Holy Cross Mountains, Central Poland. Acta Geologica Polonica, 2009, 59(4): 459-481
[129]	Kin A, B?a?ejowski B. A new Trimerocephalus species (Trilobita, Phacopidae) from the late Devonian (early Famennian) of Poland. Zootaxa, 2013, 3626(3): 345-355 doi: 10.11646/zootaxa.3626.3.3
[130]	Gutiérrez-Marco J C, Sá A A, García-Bellido D C, Rábano I, Valério M. Giant trilobites and trilobite clusters from the Ordovician of Portugal. Geology, 2009, 37(5): 443-446 doi: 10.1130/G25513A.1
[131]	Fornarelli F, Oresta P, Lippolis A. Flow patterns and heat transfer around six in-line circular cylinders at low Reynolds number. JP Journal of Heat and Mass Transfer, 2015, 11(1): 1-28 doi: 10.17654/JPHMTFeb2015_001_028
[132]	Gilbert C, Blanc S, Le Maho Y, Ancel A. Energy saving processes in huddling emperor penguins: From experiments to theory. Journal of Experimental Biology, 2008, 211(1): 1-8 doi: 10.1242/jeb.005785
[133]	Gilbert C, Le Maho Y, Perret M, Ancel A. Body temperature changes induced by huddling in breeding male emperor penguins. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2007, 292(1): R176-R185 doi: 10.1152/ajpregu.00912.2005
[134]	Ancel A, Gilbert C, Poulin N, Beaulieu M, Thierry B. New insights into the huddling dynamics of emperor penguins. Animal Behaviour, 2015, 110: 91-98 doi: 10.1016/j.anbehav.2015.09.019
[135]	Alberts J R. Huddling by rat pups: Group behavioral mechanisms of temperature regulation and energy conservation. Journal of Comparative and Physiological Psychology, 1978, 92(2): 231-245 doi: 10.1037/h0077459
[136]	Hayes J P, Speakman J R, Racey P A. The contributions of local heating and reducing exposed surface area to the energetic benefits of huddling by short-tailed field voles (Microtus agrestis). Physiological Zoology, 1992, 65(4): 742-762 doi: 10.1086/physzool.65.4.30158537
[137]	Haim A, Van Aarde R J, Skinner J D. Burrowing and huddling in newborn porcupine: The effect on thermoregulation. Physiology & Behavior, 1992, 52(2): 247-250
[138]	Gilbert C, McCafferty D, Le Maho Y, Martrette J M, Giroud S, Blanc S, et al. One for all and all for one: The energetic benefits of huddling in endotherms. Biological Reviews, 2010, 85(3): 545-569 doi: 10.1111/j.1469-185X.2009.00115.x
[139]	Gilbert C, Robertson G, Le Maho Y, Naito Y, Ancel A. Huddling behavior in emperor penguins: Dynamics of huddling. Physiology & Behavior, 2006, 88(4-5): 479-488
[140]	Gilbert C, Robertson G, Le Maho Y, Ancel A. How do weather conditions affect the huddling behaviour of emperor penguins? Polar Biology, 2008, 31(2): 163-169
[141]	Ancel A, Beaulieu M, Le Maho Y, Gilbert C. Emperor penguin mates: Keeping together in the crowd. Proceedings of the Royal Society B: Biological Sciences, 2009, 276(1665): 2163-2169 doi: 10.1098/rspb.2009.0140
[142]	Zitterbart D P, Wienecke B, Butler J P, Fabry B. Coordinated movements prevent jamming in an emperor penguin huddle. PLoS One, 2011, 6(6): Article No. e20260 doi: 10.1371/journal.pone.0020260
[143]	Alberts J R. Huddling by rat pups: Multisensory control of contact behavior. Journal of Comparative and Physiological Psychology, 1978, 92(2): 220-230 doi: 10.1037/h0077458
[144]	Canals M, Bozinovic F. Huddling behavior as critical phase transition triggered by low temperatures. Complexity, 2011, 17(1): 35-43 doi: 10.1002/cplx.20370
[145]	Conklin P, Heggeness F W. Maturation of tempeature homeostasis in the rat. American Journal of Physiology-Legacy Content, 1971, 220(2): 333-336 doi: 10.1152/ajplegacy.1971.220.2.333
[146]	Malik S S, Fewell J E. Thermoregulation in rats during early postnatal maturation: Importance of nitric oxide. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 2003, 285(6): R1366-R1372 doi: 10.1152/ajpregu.00280.2003
[147]	Richter C P. Animal behavior and internal drives. The Quarterly Review of Biology, 1927, 2(3): 307-343 doi: 10.1086/394279
[148]	Sealander Jr J A. The relationship of nest protection and huddling to survival of Peromyscus at low temperature. Ecology, 1952, 33(1): 63-71 doi: 10.2307/1931252
[149]	Glancy J, Gro? R, Stone J V, Wilson S P. A self-organising model of thermoregulatory huddling. PLoS Computational Biology, 2015, 11(9): Article No. e1004283 doi: 10.1371/journal.pcbi.1004283
[150]	Glancy J, Gro? R, Wilson S P. A minimal model of the phase transition into thermoregulatory huddling. In: Proceedings of the 2nd International Conference on Biomimetic and Biohybrid Systems. London, UK: Springer, 2013. 381?383
[151]	賀威, 丁施強, 孫長銀. 撲翼飛行器的建模與控制研究進展. 自動化學報, 2017, 43(5): 685-696 He Wei, Ding Shi-Qiang, Sun Chang-Yin. Research progress on modeling and control of flapping-wing air vehicles. Acta Automatica Sinica, 2017, 43(5): 685-696
[152]	尹曌, 賀威, 鄒堯, 穆新星, 孫長銀. 基于“雁陣效應”的撲翼飛行機器人高效集群編隊研究. 自動化學報, 2021, 47(6): 1355-1367 Yin Zhao, He Wei, Zou Yao, Mu Xin-Xing, Sun Chang-Yin. Efficient formation of flapping-wing aerial vehicles based on wild geese queue effect. Acta Automatica Sinica, 2021, 47(6): 1355-1367
[153]	付強, 張祥, 趙民, 張春華, 賀威. 仿生撲翼飛行器風洞實驗研究進展. 工程科學學報, 2022, 44(4): 767-779 Fu Qiang, Zhang Xiang, Zhao Min, Zhang Chun-Hua, He Wei. Research progress on the wind tunnel experiment of a bionic flapping-wing aerial vehicle. Chinese Journal of Engineering, 2022, 44(4): 767-779
[154]	汪婷婷, 何修宇, 鄒堯, 付強, 賀威. 面向撲翼飛行機器人的飛行控制研究進展綜述. 工程科學學報, 2023, 45(10): 1630-1640 Wang Ting-Ting, He Xiu-Yu, Zou Yao, Fu Qiang, He Wei. Research progress on the flight control of flapping-wing aerial vehicles. Chinese Journal of Engineering, 2023, 45(10): 1630-1640
[155]	錢辰, 方勇純, 李友朋. 面向撲翼飛行控制的建模與奇異攝動分析. 自動化學報, 2022, 48(2): 434-443 Qian Chen, Fang Yong-Chun, Li You-Peng. Control oriented modeling and singular perturbation analysis in flapping-wing flight. Acta Automatica Sinica, 2022, 48(2): 434-443