基于鱼类交互行为的鱼类集群规则

doi:10.7527/S1000-6893.2023.29793

Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (S2): 729793-729793.doi: 10.7527/S1000-6893.2023.29793

• Swarm Intelligence and Cooperative Control • Previous Articles Next Articles

Fish schooling rules based on fish interaction behavior

Yi ZHOU¹, Jian CHEN², Yu HAN¹(), Yue ZHANG¹, Fengcong JIA¹

^1.College of Water Resources and Civil Engineering，China Agricultural University，Beijing 100083，China
^2.College of Engineering，China Agricultural University，Beijing 100083，China

Received:2023-10-30 Revised:2023-11-07 Accepted:2023-11-22 Online:2023-12-19 Published:2023-12-07
Contact: Yu HAN E-mail:yhan@cau.edu.cn
Supported by:
Research and Demonstration of Radar Flow Measurement Technology for High Sand-Containing Channels in Irrigation Areas(2023AB060);National Natural Science Foundation of China(51979275);Research on Key Technologies for Integrated Management of Water System Connectivity in Inland River Basins in Northwest China(202305510910168);Joint Open Research Fund Program of State key Laboratory of Hydroscience and Engineering and Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance(sklhse2022-Iow08);Tarim University-China Agricultural University Joint Fund(ZNLH202205)

Abstract

Abstract:

Group behaviors formed by organisms or objects due to cluster properties are common in nature. The study of group behavior helps human beings to deeply understand the natural world and provides theoretical guidance for intelligent cluster control. Individual interactions of fish schools can induce neighboring fish bodies to share information， thus improving the success rate of fish migration. In this study， fish upstream behavior experiments were carried out under the self-constructed ecological fish passage， and individual and school upstream experiments were carried out by setting semicircular obstacles to create a low-turbulence flow field. Based on the YOLOv5 algorithm， we captured the high-precision fish movement trajectories of the fish group under different flow conditions， and combined them with the results of numerical modeling to obtain hydraulic indicators. Track the passive swimming behavior of fish in the experimental area， and count the characteristic parameters of each interaction behavior. Combined with the hydraulic indicators at the location where the behaviors occurred， the response relationship between the movement strategy of the fish throughout the upstream journey and the perceived flow field was comprehensively derived. Through the analysis of fish interaction behaviors， flow field preference， and the influence of sidewalls， a two-dimensional cluster rule model of the fish population was established based on the principle of minimum potential energy. The results of this study are helpful in deconstructing the dynamic upstream process of fish in specific migratory scenarios. It will provide a system of quantitative indexes for the interactive movement of carp， which can be applied to specific behavioral scenarios such as the test of the swimming ability of fish， the deconstruction of upstreaming behaviors， and also provides inspiration for the design and control of future robotic swarming formations.

Key words: fish schooling, bionic algorithms, columns, fish migration, ecological fish passage

CLC Number:

V249

Yi ZHOU, Jian CHEN, Yu HAN, Yue ZHANG, Fengcong JIA. Fish schooling rules based on fish interaction behavior[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 729793-729793.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

References 21

1	HERBERT-READ J E， PERNA A， MANN R P， et al. Inferring the rules of interaction of shoaling fish［J］. Proceedings of the National Academy of Sciences of the United States of America， 2011， 108（46）： 18726-18731.
2	GYLLINGBERG L， SZORKOVSZKY A， SUMPTER D. Using neuronal models to capture burst-and-glide motion and leadership in fish［J］. Journal of the Royal Society Interface， 2023， 20（204）： 20230212.
3	LI M N， CHEN M， WU W X， et al. Differences in the natural swimming behavior of Schizothorax Prenanti individual and schooling in spatially heterogeneous turbulent flows［J］. Animals， 2023， 13（6）： 1025.
4	MÚGICA J， TORRENTS J， CRISTÍN J， et al. Scale-free behavioral cascades and effective leadership in schooling fish［J］. Scientific Reports， 2022， 12（1）：10783.
5	CHEN S Y， FEI Y， CHEN Y C， et al. The swimming patterns and energy-saving mechanism revealed from three fish in a school［J］. Ocean Engineering， 2016， 122： 22-31.
6	CALOVI D S， LITCHINKO A， LECHEVAL V， et al. Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors［J］. Plos Computational Biology， 2018， 14（1）： e1005933.
7	ROSENTHAL S B， TWOMEY C R， HARTNETT A T， et al. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion［J］. Proceedings of the National Academy of Sciences of the United States of America， 2015， 112（15）： 4690-4695.
8	WEIHS D. Hydromechanics of fish schooling［J］. Nature， 1973， 241（5387）： 290-291.
9	胡鑫一，蔡振宇，左可文，等. 机器鱼的运动控制以及路径规划算法研究进展［J］. 船舶工程， 2022， 44（S1）： 455-458.
	HU X Y， CAI Z Y， ZUO K W， et al. 2022， 44（S1）： 455-458 （in Chinese）.
10	周梦园，吴君钦，夏乐，等. 智能仿生鱼系统的设计与实现［J］. 计算机工程与设计， 2022， 43（5）： 1467-1476.
	ZHOU M Y， WU J Q， XIA L， et al. Design and implementation of intelligent bionic fish system［J］. Computer Engineering and Design， 2022， 43（5）： 1467-1476 （in Chinese）.
11	刘磊，孙卓文，陈令仪，等. 基于深度学习的仿生集群运动智能控制［J］. 控制与决策， 2021， 36（9）： 2195-2202.
	LIU L， SUN Z W， CHEN L Y， et al. Intelligent control of bionic collective motion based on deep learning ［J］. Control and Decision， 2021， 36（9）： 2195-2202 （in Chinese）.
12	吴坤，谭劭昌. 基于改进鲸鱼优化算法的无人机航路规划［J］. 航空学报， 2020， 41（S2）： 724286.
	WU K， TAN S C. Path planning of UAVs based on improved whale optimization algorithm［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（S2）： 724286 （in Chinese）.
13	张天栋，王睿，程龙，等. 鱼集群游动的节能机理研究综述［J］. 自动化学报， 2021， 47（03）： 475-488.
	ZHANG T D， WANG R， CHENG L， et al. Research on energy-saving mechanism of fish schooling： A review［J］. Acta Automatica Sinica， 2021， 47（03）： 475-488 （in Chinese）.
14	AOKI I. A simulation study on the schooling mechanism in fish［J］. Bulletin of the Japanese Society of Scientific Fisheries， 1982， 48（8）： 1081-1088.
15	JOLLES J W， BOOGERT N J， SRIDHAR V H， et al. Consistent individual differences drive collective behavior and group functioning of schooling fish［J］. Current Biology， 2017， 27（18）： 2862.
16	SMITH J A， MARTIN A M. Comparison of hard-core and soft-core potentials for modelling flocking in free space［DB/OL］. arXiv preprints： 0905.2260， 2009.
17	LI G N， SUN S K， LIU H T， et al. Schizothorax prenanti swimming behavior in response to different flow patterns in vertical slot fishways with different slot positions［J］. Science of the Total Environment， 2021， 754： 142142.
18	VOWLES A S， ANDERSON J J， GESSEL M H， et al. Effects of avoidance behaviour on downstream fish passage through areas of accelerating flow when light and dark［J］. Animal Behaviour， 2014， 92： 101-109.
19	VOWLES A S， KEMP P S. Effects of light on the behaviour of brown trout （Salmo trutta） encountering accelerating flow： Application to downstream fish passage［J］. Ecological Engineering， 2012， 47： 247-253.
20	欧昌雪，张羽，王二平，等. 面向鱼道设计的模型鱼洄游特征流速试验研究［J］. 中国农村水利水电， 2018（10）： 69-72.
	OU C X， ZHANG Y， WANG E P， et al. Experimental research on the characteristic water-flow velocity for the upstream migration of model fish oriented to fishway design［J］. China Rural Water and Hydropower， 2018（10）： 69-72 （in Chinese）.
21	LIN C Y， DENG Z D， SHI X T， et al. Mutu-ally promoting or constraining？ Disentangling the superimposed effect of velocity and illuminance on fish motion in low-velocity flows with a novel metric［J］. Freshwater Biology， 2022， 67（8）： 1468-1480.

流量/（m³∙h^-1）	期望位置	相对分布随机性	聚集程度	偏好区间	偏好流速范围/（m∙s^-1）	偏好区间长度	偏好程度
60	0.492	0.158	0.170	0.087~0.113	0.116	0.026	1.061
70	0.492	0.174	0.101	0.033~0.153	0.125	0.120	1.086
80	0.337	0.236	0.102	0.033~0.14	0.132	0.107	1.731

Fish schooling rules based on fish interaction behavior

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 21

Related Articles 1

Recommended Articles

Metrics

Comments