Abstract
Two prominent categories of algorithms for achieving coordinated multi-robot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. Although typically they are treated separately, we believe that a combination of flock and formation control represents a promising algorithmic solution. Such an algorithm could take advantage of a combination of characteristics of both categories that are best suited for a given situation. Therefore, in this work, we propose two distributed algorithms capable of gradually and reversibly shifting between flocking and formation behaviors using a single parameter \(\mathcal {W}\). We evaluate them using both simulated and real robots with and without the presence of obstacles. We find that both algorithms successfully trade off flock density for formation error. Furthermore, leveraging a narrow passage experiment as an application case study, we demonstrate that an adaptive shift between flock and formation behavior, adopting a simple method to define \(\mathcal {W}\) in real-time using exclusively on-board resources, results in a statistically relevant reduction of traversing time in comparison to a non-adaptive formation control algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: ACM Annual Conference on Computer Graphics and Interactive Techniques, pp. 25–34 (1987)
Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51, 401–420 (2006)
Liu, Y., Bucknall, R.: A survey of formation control and motion planning of multiple unmanned vehicles. Robotica 36(7), 1019–1047 (2018)
Wang, P.K.C.: Navigation strategies for multiple autonomous mobile robots moving in formation. J. Robot. Syst. 8(2), 177–195 (1991)
Tan, K.-H., Lewis, M.A.: Virtual structures for high-precision cooperative mobile robotic control. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 132–139 (1996)
Balch, T., Arkin, R.: Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)
Wang, H., Rubenstein, M.: Shape formation in homogeneous swarms using local task swap**. IEEE Trans. Rob. 36(3), 597–612 (2020)
Antonelli, G., Arrichiello, F., Chiaverini, S.: The NSB control: a behavior-based approach for multi-robot systems. Paladyn, J. Behav. Robot. 1, 48–56 (2010)
Monteiro, S., Bicho, E.: Attractor dynamics approach to formation control: theory and application. Auton. Robots 29, 331–355 (2010)
Vilca, J., Adouane, L., Mezouar, Y.: Adaptive leader-follower formation in cluttered environment using dynamic target reconfiguration. In: Chong, N.-Y., Cho, Y.-J. (eds.) Distributed Autonomous Robotic Systems. STAR, vol. 112, pp. 237–254. Springer, Tokyo (2016). https://doi.org/10.1007/978-4-431-55879-8_17
Wasik, A., Pereira, J.N., Ventura, R., Lima, P.U., Martinoli, A.: Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1721–1728 (2016)
Seng, W.L., Barca, J.C., Şekercioğlu, Y.A.: Distributed formation control of networked mobile robots in environments with obstacles. Robotica 34(6), 1403–1415 (2016)
Soares, J.M., Aguiar, A.P., Pascoal, A.M., Martinoli, A.: A distributed formation-based odor source localization algorithm - design, implementation, and wind tunnel evaluation. In: IEEE International Conference on Robotics and Automation, pp. 1830–1836 (2015)
Falconi, R., Gowal, S., Martinoli, A.: Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions. In: IEEE International Conference on Robotics and Automation, pp. 3207–3214 (2010)
Soria, E., Schiano, F., Floreano, D.: The influence of limited visual sensing on the reynolds flocking algorithm. In: Third IEEE International Conference on Robotic Computing, pp. 138–145 (2019)
Baumann, C., Martinoli, A.: A modular functional framework for the design and evaluation of multi-robot navigation. Robot. Auton. Syst. 144, 103849 (2021)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)
Mesbahi, M., Egerstedt, M.: Graph Theoretic Methods in Multiagent Networks. Princeton Series in Applied Mathematics (2010)
Soares, J.M., Navarro, I., Martinoli, A.: The Khepera IV mobile robot: performance evaluation, sensory data and software toolbox. In: Robot 2015: Second Iberian Robotics Conference. AISC, vol. 417, pp. 767–781. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27146-0_59
Pugh, J., Raemy, X., Favre, C., Falconi, R., Martinoli, A.: A fast onboard relative positioning module for multirobot systems. IEEE/ASME Trans. Mechatron. 14(2), 151–162 (2009)
Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J., Martinoli, A.: SwisTrack - a flexible open source tracking software for multi-agent systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4004–4010 (2008)
Michel, O.: WebotsTM: professional mobile robot simulation. Int. J. Adv. Rob. Syst. 1, 39–42 (2004)
Tsai, R.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)
Acknowledgements
We would like to thank Masaki Haruna, Masahiko Kurishige and Tomoki Emmei of the Advanced Technology R &D Center, Mitsubishi Electric Corporation, Amagasaki City, Japan for the fruitful technical discussions all along this work. This work is financially supported by Mitsubishi Electric Corporation, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
A video of the experiments performed for this contribution can be found on our research web page: www.epfl.ch/labs/disal/research/mrsmodelingcontrol.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Baumann, C., Perolini, J., Tourki, E., Martinoli, A. (2024). Hybrid Flock - Formation Control Algorithms. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-51497-5_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51496-8
Online ISBN: 978-3-031-51497-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)