Abstract
Swarming behaviors play an eminent role in both biological and engineering research, and show great potential applications in many emerging fields. Traditional swarming models still lack integrity, uniformity, and stability in swarm forming processes, resulting in fragmentation and void phenomena. Inspired by the shepherding behaviors observed in nature, we propose an integrated negotiation-control scheme for distributed swarm control of massive robots. The core idea of this scheme is that the robots at the boundary of the group herd the internal robots to form an equilibrium swarm. For this purpose, we introduce a concept of virtual group center towards which boundary robots herd internal robots. Then, a distributed negotiation mechanism is designed to allow each robot to negotiate the virtual group center only through local interactions with its neighbors. After that, we propose a shepherding-inspired swarm control law to drive a group of robots to form an integrated, uniform, and stable configuration from any initial states. Both numerical and flight simulations are presented to verify the effectiveness of our proposed swarm control scheme.
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References
Vicsek T, Zafeiris A. Collective motion. Phys Rep, 2012, 517: 71–140
Chen Y, Kolokolnikov T. A minimal model of predator-swarm interactions. J R Soc Interface, 2014, 11: 20131208
Chuang Y L, Huang Y R, D’Orsogna M R, et al. Multi-vehicle flocking: Scalability of cooperative control algorithms using pairwise potentials. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation. Rome: IEEE, 2007. 2291–2299
Yang J C, Lu Q S, Lang X F. Flocking shape analysis of multi-agent systems. Sci China Tech Sci, 2010, 53: 741–747
Tang Y, Hu Y, Cui J, et al. Vision-aided multi-UAV autonomous flocking in GPS-denied environment. IEEE Trans Ind Electron, 2019, 66: 616–626
Xu Y, Luo D L, You Y C, et al. New advances in multiple autonomous aerial robots formation control technology. Sci China Tech Sci, 2019, 62: 1871–1872
Wang X, Lu J. Collective behaviors through social interactions in bird flocks. IEEE Circuits Syst Mag, 2019, 19: 6–22
Wang X X, Liu Z X, Chen Z Q. Event-triggered fault-tolerant consensus control with control allocation in leader-following multi-agent systems. Sci China Tech Sci, 2021, 64: 879–889
Yu Y P, Liu J C, Wei C. Hawk and pigeons intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization. Sci China Tech Sci, 2022, 65: 1072–1086
Li J, Li L, Zhao S. Predator-prey survival pressure is sufficient to evolve swarming behaviors. New J Phys, 2023, 25: 092001
Sun G, Zhou R, Di B, et al. A physicochemically inspired approach to flocking control of multiagent system. Nonlinear Dyn, 2020, 102: 2627–2648
Reynolds C W. Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput Graph, 1987, 21: 25–34
Vicsek T, Czirók A, Ben-Jacob E, et al. Novel type of phase transition in a system of self-driven particles. Phys Rev Lett, 1995, 75: 1226–1229
Vásárhelyi G, Virágh C, Somorjai G, et al. Optimized flocking of autonomous drones in confined environments. Sci Robot, 2018, 3: eaat3536
Gómez-Nava L, Bon R, Peruani F. Intermittent collective motion in sheep results from alternating the role of leader and follower. Nat Phys, 2022, 18: 1494–1501
Romanczuk P, Couzin I D, Schimansky-Geier L. Collective motion due to individual escape and pursuit response. Phys Rev Lett, 2009, 102: 010602
Luo Q, Duan H. Distributed UAV flocking control based on homing pigeon hierarchical strategies. Aerosp Sci Tech, 2017, 70: 257–264
Cai H, Zhang T Y, Gao H L, et al. Cooperative driven algorithm for Couzin model based fish school by multiple predators. Wireless Commun Mobile Comput, 2022, 2022: 1494–1501
Cucker F, Smale S. Emergent behavior in flocks. IEEE Trans Autom Control, 2007, 52: 852–862
Yin X, Yue D, Chen Z. Asymptotic behavior and collision avoidance in the Cucker-Smale model. IEEE Trans Autom Control, 2020, 65: 3112–3119
Zhang Z, Yin X, Gao Z. Non-flocking and flocking for the Cucker-Smale model with distributed time delays. J Franklin Inst, 2023, 360: 8788–8805
Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans Autom Control, 2006, 51: 401–420
Saif O, Fantoni I, Zavala-Río A. Distributed integral control of multiple UAVs: Precise flocking and navigation. IET Control Theory Appl, 2019, 13: 2008–2017
Levine H, Rappel W J, Cohen I. Self-organization in systems of self-propelled particles. Phys Rev E, 2000, 63: 017101
Katz Y, Tunstrøm K, Ioannou C C, et al. Inferring the structure and dynamics of interactions in schooling fish. Proc Natl Acad Sci USA, 2011, 108: 18720–18725
Ling H, Mclvor G E, van der Vaart K, et al. Costs and benefits of social relationships in the collective motion of bird flocks. Nat Ecol Evol, 2019, 3: 943–948
Koren Y, Borenstein J. Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation. Sacramento: IEEE, 1991. 1398–1404
Wang Z Y, Gu D B, Hu H S. Leader-follower flocking experiments using estimated flocking center. In: Proceedings of the 2009 International Conference on Mechatronics and Automation. Changchun: IEEE, 2009. 3733–3738
Gu D, Wang Z. Leader-follower flocking: Algorithms and experiments. IEEE Trans Control Syst Tech, 2009, 17: 1211–1219
Bhowmick C, Behera L, Shukla A, et al. Flocking control of multi-agent system with leader-follower architecture using consensus based estimated flocking center. In: Proceedings of the IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. Florence: IEEE, 2016. 166–171
Zhao S, Dimarogonas D V, Sun Z, et al. A general approach to coordination control of mobile agents with motion constraints. IEEE Trans Autom Control, 2018, 63: 1509–1516
Sun G, Zhou R, Ma Z, et al. Mean-shift exploration in shape assembly of robot swarms. Nat Commun, 2023, 14: 3476
Li J, Ning Z, He S, et al. Three-dimensional bearing-only target following via observability-enhanced helical guidance. IEEE Trans Robot, 2023, 39: 1509–1526
Ma X, Jiao Z, Wang Z, et al. 3-D decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles. IEEE Trans Control Syst Tech, 2018, 26: 939–953
Rubenstein M, Cornejo A, Nagpal R. Programmable self-assembly in a thousand-robot swarm. Science, 2014, 345: 795–799
Zhu G L, Liu K X, Gu H B, et al. Neural-network-based fully distributed formation control for nonlinear multi-agent systems with event-triggered communication. Sci China Tech Sci, 2024, 67: 209–220
Yuan G S, Duan H B. Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization. Sci China Tech Sci, 2024, 67: 435–448
Beaver L E, Malikopoulos A A. An overview on optimal flocking. Annu Rev Control, 2021, 51: 88–99
Fang H, Wei Y, Chen J, et al. Flocking of second-order multiagent systems with connectivity preservation based on algebraic connectivity estimation. IEEE Trans Cybern, 2017, 47: 1067–1077
Li X, Zhou R, Sun G, et al. Connectivity-preserving flocking of multiagent systems via selecting critical neighbors. IEEE Trans Network Sci Eng, 2023, 10: 3779–3792
Ren W, Atkins E. Distributed multi-vehicle coordinated control via local information exchange. Int J Robust NOnlinear Control, 2007, 17: 1002–1033
Zhang X, Jia S, Li X. Improving the synchronization speed of self-propelled particles with restricted vision via randomly changing the line of sight. NOnlinear Dyn, 2017, 90: 43–51
Ballerini M, Cabibbo N, Candelier R, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc Natl Acad Sci USA, 2008, 105: 1232–1237
Shah S, Dey D, Lovett C, et al. AirSim: High-fidelity visual and physical simulation for autonomous vehicles. In: Hutter M, Siegwart R (eds.). Field and Service Robotics. Springer Proceedings in Advanced Robotics. Vol. 5. Cham: Springer, 2018. 621–635
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This work was supported by the National Key R&D Program of China (Grant No. 2022YFB3305600), the National Natural Science Foundation of China (Grant Nos. 62103015 and 62141604), the China Postdoctoral Science Foundation (Grant No. 2023M740185), and the Postdoctoral Fellows of Beihang “Zhuoyue” Program.
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Sun, G., Gu, H. & Lü, J. Distributed swarm control for multi-robot systems inspired by shepherding behaviors. Sci. China Technol. Sci. 67, 2191–2202 (2024). https://doi.org/10.1007/s11431-023-2651-6
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DOI: https://doi.org/10.1007/s11431-023-2651-6