仿鸟群自推进机制的无人机集群相变控制

段海滨; 尤灵辰; 范彦铭; 李明

doi:10.16383/j.aas.c240598

仿鸟群自推进机制的无人机集群相变控制

doi: 10.16383/j.aas.c240598 cstr: 32138.14.j.aas.c240598

1.
北京航空航天大学自动化科学与电气工程学院飞行器一体化控制全国重点实验室北京 100083
2.
中国航空工业集团公司沈阳飞机设计研究所沈阳 110035

基金项目: 国家自然科学基金 (62350048, 624B2013, T2121003, U20B2071)资助

详细信息

作者简介:
段海滨：北京航空航天大学自动化科学与电气工程学院教授. 主要研究方向为无人机集群仿生自主飞行控制. 本文通信作者. E-mail: hbduan@buaa.edu.cn

尤灵辰：北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为仿生集群自主飞行控制. E-mail: lcyou@buaa.edu.cn

范彦铭：沈阳飞机设计研究所专业领域首席专家. 主要研究方向为先进飞行控制技术研究与系统研制. E-mail: michaelfan@yeah.net

李明：中国工程院院士, 航空工业沈阳飞机设计研究所首席专家. 主要研究方向为飞机自动化、无人机自主飞行控制. E-mail: mingli@mail.sy.ln.cn

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出版历程
- 收稿日期: 2024-08-28
- 录用日期: 2024-12-23
- 网络出版日期: 2025-02-26

Phase transition Control of UAV Swarm Based on Bird-inspired Self-propulsion Mechanism

1.
National Key Laboratory of Aircraft Integrated Flight Control, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083
2.
Shenyang Aircraft Design and Research Institute, Aviation Industry Corporation of China, Shenyang 110035

Funds: Supported by National Natural Science Foundation of China (62350048, 624B2013, T2121003, U20B2071)

More Information

Author Bio:
DUAN Hai-bin　Professor at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers the bionics autonomous flight control of unmanned aerial vehicle swarms.Corresponding author of this paper

YOU Ling-Chen　Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers the bionics autonomous flight control

FAN Yan-Ming　Chief Expert in the Professional Field at Shenyang Aircraft Design and Research Institute. His research interest covers the study and system development of advanced flight control technology

LI Ming　Academician of the Chinese Academy of Engineering, and Chief Expert at Shenyang Aircraft Design and Research Institute. His research interest covers aircraft automation and autonomous flight control of unmanned aerial vehicles

摘要

摘要: 针对无人机集群的运动相态转换问题, 提出了一种基于仿鸟群自推进粒子模型的无人机集群相变控制方法. 首先, 从鸟群运动行为中获得启发, 通过设计速度保持项和势能梯度项构建仿鸟群运动模型, 并设计相变控制项模拟巢穴对鸟群的吸引, 以实现集群在不同相态之间的转换. 然后, 讨论了集群在设计的相变控制律作用下的运动相态, 证明无人机集群能够实现两种稳定的运动相态并进行相互转换. 最后, 仿真验证了集群存在的两种稳定运动构型, 所提出相变控制律能够实现两种集群运动相态的互相转换.
- 无人机集群 /
- 相变控制 /
- 自组织 /
- 自推进粒子
Abstract: A phase transition control method for UAV(unmanned aerial vehicle) swarms based on the bird-inspired self-propelled particle model has been proposed. Firstly, inspired from bird flock behavior, a bird-like motion model is constructed by designing a velocity maintenance term and a potential gradient term, and a phase transition control term is designed to simulate the attraction of the nest to the flock to achieve transition between different phases. Subsequently, the motion modalities of the swarm under the designed phase transition control law were discussed, demonstrating that the UAV swarm is capable of achieving two stable motion phases and reversible transition between them. Ultimately, simulations proved the existence of two stable motion phases within the swarm, and the proposed phase transition control protocol was validated to enable mutual transitions between the two motion phases.
- Swarm of unmanned aerial vehicle /
- phase transition control /
- self-organization /
- self-propelled particles

HTML全文

图 1 涡旋半径积分示意图

Fig. 1 Illustration for calculating the radius of swarm milling phase

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图 2 集群涡旋相态转变. (a) t = 0 s; (b) t = 57 s; (c) t = 93 s; (d) t = 116 s

Fig. 2 Milling transition for UAV swarm. (a) t = 0 s; (b) t = 57 s; (c) t = 93 s; (d) t = 116 s

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图 3 涡旋相形成过程中的序参量变化情况

Fig. 3 Order parameter during the formation of milling phase

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图 4 单个无人机状态随时间变化曲线

Fig. 4 Flight status of one UAV in the swarm

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图 5 集群涡旋半径随平衡距离d变化情况

Fig. 5 The variation of milling radius with equilibrium distance d

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图 6 相变仿真流程图

Fig. 6 Flowchart of phase transition simulation.

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图 7 集群相态转换结果. (a)集群序参量变化情况(第1、2条垂直虚线之间和第3、4条垂直虚线之间为相变控制项不为0的时间段. 第3、4条虚线由于距离过近在显示上略有重合, 在小图中进行了放大); (b) ~ (f) $t=180,\;205,\;300,\;405,\;500\;\text{s} $时的集群运动相态

Fig. 7 Results of phase transition. (a) Order parameter in phase transition process. (b) ~ (f) Group motion phase at $ t=180,\;205,\;300,\;405,\;500\;\text{s}$

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图 8 集群中心速度曲线

Fig. 8 Speed of the swarm center

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图 9 文献[33]中模型的集群相态转换结果. (a)集群序参量变化情况; (b) ~ (d) $t=50,\;102,\;150,\;202,\;270\;\text{s} $时的集群运动相态

Fig. 9 Results of phase transition using model in He^[33]. (a) Order parameter in phase transition process. (b) ~ (d) Group motion phase at $ t=50,\;102,\;150,\;202,\;270\;\text{s}$

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参考文献(33)

[1]	蔡运颂, 许璟, 牛玉刚. 基于自适应多尺度超螺旋算法的无人机集群姿态同步控制. 自动化学报, 2023, 49(8): 1656−1666 Cai Y S, Xu J, Niu Y G. Attitude consensus control of UAV swarm based on adaptive multi-scale super-twisting lgorithm. Acta Automatica Sinica, 2023, 49(8): 1656−1666
[2]	王峰, 黄子路, 韩孟臣, 邢立宁, 王凌. 基于KnCMPSO算法的异构无人机协同多任务分配. 自动化学报, 2023, 49(2): 399−414 Wang F, Huang Z L, Han M C, Xing L N, Wang L. A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation. Acta Automatica Sinica, 2023, 49(2): 399−414
[3]	Luo L, Wang X, Ma J, and Ong Y S. Grpavoid: Multigroup collision-avoidance control and optimization for uav swarm. IEEE Transactions on Cybernetics, 2023, 53(3): 1776−1789 doi: 10.1109/TCYB.2021.3132044
[4]	韦宸越, 何明, 韩伟, 徐昕, 高宏. 无人机集群弹性评估及重构技术研究. 计算机工程与应用, 2024, 60(15): 1−10 doi: 10.3778/j.issn.1002-8331.2401-0452 Liu J, Gu Y, Cheng Y H, Wang X S. Prediction of breast cancer pathogenic genes based on multi-agent reinforcement learning. Computer Engineering and Applications, 2024, 60(15): 1−10 doi: 10.3778/j.issn.1002-8331.2401-0452
[5]	何明, 陈浩天, 韩伟, 邓成, 段海滨. 无人机仿鸟群协同控制发展现状及关键技术.航空学报, 2024.待出版 He M, Chen H T, Han W, Deng C, Duan H B, Development status and key technologies of cooperative control of bird-inspired UAV swarms. Acta Aeronautica et Astronautica Sinica, 2024. doi: 10.7527/S1000-6893.2024.29946. to be published.
[6]	Xue T, Li X, Grassberger P, and Chen L. Swarming transitions in hierarchical societies. Physical Review Research, 2020, 2(4): 042017 doi: 10.1103/PhysRevResearch.2.042017
[7]	Hindes J, Edwards V, Kasraie K S, Stantchev G, and Schwartz I B. Swarm shedding in networks of self-propelled agents. Scientific Reports, 2021, 11(1): 13544 doi: 10.1038/s41598-021-92748-1
[8]	Cheng Z, Chen Z Y, Vicsek T, Chen D X, and Zhang H T. Pattern phase transitions of self-propelled particles: Gases, crystals, liquids, and mills. New Journal of Physics, 2016, 18: 103005 doi: 10.1088/1367-2630/18/10/103005
[9]	Mier-y-Teran-Romero L, Forgoston E, and Schwartz I B. Coherent pattern prediction in swarms of delay-coupled agents. IEEE Transactions on Robotics, 2012, 28(5): 1034−1044 doi: 10.1109/TRO.2012.2198511
[10]	Duan H B and Zhang X Y. Phase transition of vortexlike self-propelled particles induced by a hostile particle. Physical Review E, 2015, 92(1): 012701
[11]	Hindes J, Edwards V, Hsieh M A, and Schwartz I B. Critical transition for colliding swarms. Physical Review E, 2021, 103(6): 062602 doi: 10.1103/PhysRevE.103.062602
[12]	Edwards V, deZonia P, Hsieh M A, Hindes J, Triandaf I, and Schwartz I B. Delay induced swarm pattern bifurcations in mixed reality experiments. Chaos, 2020, 30(7): 073126 doi: 10.1063/1.5142849
[13]	Lei X K, Xiang Y L, Duan M Y, and Peng X G. Exploring the criticality hypothesis using programmable swarm robots with vicsek-like interactions. Journal of the Royal Society Interface, 2023, 20(204): 20230176 doi: 10.1098/rsif.2023.0176
[14]	Xie H, et al. Reconfigurable magnetic microrobot swarm: Multimode transformation, locomotion, and manipulation. Science Robotics, 2019, 4(28): eaav8006 doi: 10.1126/scirobotics.aav8006
[15]	Hao Z J, Mayya S, Notomista G, Hutchinson S, Egerstedt M, and Ansari A. Controlling collision-induced aggregations in a swarm of micro bristle robots. IEEE Transactions on Robotics, 2023, 39(1): 590−604 doi: 10.1109/TRO.2022.3189846
[16]	Guilford T, Roberts S, Biro D, and Rezek I. Positional entropy during pigeon homing ii: Navigational interpretation of bayesian latent state models. Journal of theoretical biology, 2004, 227(1): 25−38 doi: 10.1016/j.jtbi.2003.07.003
[17]	Riehl C and LaPergola J B. Inclusive fitness explains behavioral diversity in a social bird. Proceedings of the National Academy of Sciences, 2023, 120(21): p.:e2305610120 doi: 10.1073/pnas.2305610120
[18]	Vicsek T, Czirók A, Ben-Jacob E, Cohen I, and Shochet O. Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 1995, 75(6): 1226−1229 doi: 10.1103/PhysRevLett.75.1226
[19]	Nagy M, Ákos Z, Biro D, and Vicsek T. Hierarchical group dynamics in pigeon flocks. Nature, 2010, 464(7290): 890−893 doi: 10.1038/nature08891
[20]	Ballerini M, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences, 2008, 105(4): 1232−1237 doi: 10.1073/pnas.0711437105
[21]	Cavagna A, et al. Flocking and turning: A new model for self-organized collective motion. Journal of Statistical Physics, 2015, 158: 601−627 doi: 10.1007/s10955-014-1119-3
[22]	Ling H, et al. Collective turns in jackdaw flocks: Kinematics and information transfer. Journal of the Royal Society Interface, 2019, 16(159): 20190450 doi: 10.1098/rsif.2019.0450
[23]	D'Orsogna M R, Chuang Y L, Bertozzi A L, and Chayes L S. Self-propelled particles with soft-core interactions: Patterns, stability, and collapse. Physical Review Letters, 2006, 96(10): 104302 doi: 10.1103/PhysRevLett.96.104302
[24]	Wu P F, Guo W C, Ai B Q, and He L. Scaling behavior of transient dynamics of vortex-like states in self-propelled particles. Physica A-Statistical Mechanics and Its Applications, 2023, 622: 128822 doi: 10.1016/j.physa.2023.128822
[25]	Berlinger F, Gauci M, and Nagpal R. Implicit coordination for 3d underwater collective behaviors in a fish-inspired robot swarm. Science Robotics, 2021, 6(50): 8668 doi: 10.1126/scirobotics.abd8668
[26]	陈琳, 郭炳晖, 段海滨, 吕卫锋. 基于群体熵度量的无人机集群目标合围控制. 中国科学: 技术科学, 2023, 53: 177−186 doi: 10.1360/SST-2021-0284 Chen L, Guo B H, Duan H B, Lü W F. Target enclosing control of multiple unmanned aerial vehicles based on crowd entropy. Scientia Sinica Technologica, 2023, 53: 177−186 doi: 10.1360/SST-2021-0284
[27]	Attanasi A, et al. Information transfer and behavioural inertia in starling flocks. Nature Physics, 2014, 10(9): 692−697
[28]	Chen D X, Sun Y Z, Shao G B, Yu W W, Zhang H T, and Lin W. Coordinating directional switches in pigeon flocks: The role of nonlinear interactions. Royal Society Open Science, 2021, 8(9): 210649 doi: 10.1098/rsos.210649
[29]	Reynolds C W, Flocks, herds and schools: A distributed behavioral model, In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, 1987. 25–34.
[30]	Couzin I D, Krause J, James R, Ruxton G D, and Franks N R. Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 2002, 218(1): 1−11 doi: 10.1006/jtbi.2002.3065
[31]	Biro D, Sumpter D J, Meade J, and Guilford T. From compromise to leadership in pigeon homing. Current biology, 2006, 16(21): 2123−2128 doi: 10.1016/j.cub.2006.08.087
[32]	Chen D X, Xu B W, Zhu T, Zhou T, and Zhang H T. Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning. Physical Review E, 2017, 96(2): 0224119
[33]	何亚琦. 基于社会力模型的集群系统的调控算法[硕士论文]. 华南理工大学, 2023. He Y Q, Control Algorithms for Social Force Model based Swarm Systems[M. S. dissertation], South China University of Technology, 2023.