Abstract
In this paper, we develop a planar Lagrangian swarm model using the Direct Method of Lyapunov to construct the instantaneous velocity of each individual in the swarm. The velocity controllers ensure the cohesion and therefore the stability of the swarm. We introduce novel Lyapunov functions which allow the swarm to navigate in obstacle-free and obstacle-cluttered environments. We apply the methodology to a swarm of planar nonholonomic vehicles. Via computer simulations, we illustrate several self-organizations such as parallel formation, emergent leader, split/rejoin maneuver, and tunnelling for obstacle avoidance.
Similar content being viewed by others
References
Lee, L.F., Krovi, V.: A Standardized Testing-Ground for Artificial-Field Based Motion Planning for Robot Collectives. Gaithersburg (2006)
Desai J.P.: A graph theoretic approach for modeling mobile robot team formations. J. Robot. Syst. 19, 511–525 (2002)
Barfoot, T.D., Clark, C.M.: Motion Planning for Formations of Mobile Robots, vol. 46, pp. 65–78. Elsevier, Amsterdam (2004)
Yamaguchi H.: A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations. Robot. Auton. Syst. 43(4), 257–282 (2003)
Song, P., Kumar, V.: A potential field based approach to multi-robot manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC (2002)
Tabuada P., Pappas G.J., Lima P.: Motion feasibility of multi-agent formations. IEEE Trans. Robot. 21, 387–392 (2005)
Dirafzoon, A., Lobaton, E.: Topological mapping of unknown environments using an unlocalized robotic swarm. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5545–5551 (2013)
Vanualailai, J., Sharan, A., Sharma, B.: A swarm model for planar formations of multiple autonomous unmanned aerial vehicles. In: Proceedings of the IEEE International Symposium on Intelligent Control (2013)
Meng, Y., Kazeem, O., Muller, J.C.: A hybrid aco/pso control algorithm for distributed swarm robotics. In: Proceedings of 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pp. 273–280 (2007)
Kumar, V., Sahin, F.: Cognitive maps in swarm robots for the mine detection application. In: IEEE International Conference on Systems, Man and Cybernetics 2003, vol. 4, pp. 3364–3369 (2003)
Bonebeau E., Theraulaz G., Deneubourg J., Aron S., Camazine S.: Self organisation in social insects. Trends Ecol. Evol. 12(5), 6 (1997)
Vanualailai, J.: Emergent spirograph-like patterns from artificial swarming. In: Proceedings of Bridges 2014: Mathematics, Music, Art, Architecture, Culture, pp. 465–468 (2014)
Fleischer, M.: Foundations of swarm intelligence: from principles to practice. In: Proceedings of the Conference on Swarming: Network Enabled C4ISR, Mclean, Virginia (2003)
Garnier S., Gautrais J., Theraulaz G.: The biological principle of swarm intelligence. Swarm Intell. 1(1), 3–31 (2007)
Theraulaz G., Bonabeau E.: Modelling the collective building of complex architectures in social insects with lattice swarms. J. Theor. Biol. 177(4), 381–400 (1995)
Theraulaz G., Gautrais J., Camazine S., Deneubourg J.: The formation of spatial patterns in social insects: from simple behaviours to complex structures. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 361(1807), 1263–1282 (2003)
Theraulaz G., Bonabeau E., Deneubourg J.: The origin of nest complexity in social insects. Complexity 3(6), 15–25 (1998)
Beckers, R., Holland, O., Deneubourg, J.: From local actions to global tasks: stimergy and collective robotics. In: Proceedings of Artificial life IV, pp. 181–189. MIT Press, Cambridge (1994)
Martinez, S., Cortes, J., Bullo, F.: Motion coordination with distributed information. IEEE Control Syst. 27(4) (2007)
Bullo F., Cort’es S., Martinez S.: Distributed Control of Robotic Networks. Princeton University Press, Princeton (2009)
Okubo A., Levin S.A.: Diffusion and Ecological Problems: Modern Perspectives. Interdisciplinary Applied Mathematics. Springer, Berlin (2001)
Edelstein-Keshet, L.: Mathematical models of swarming and social aggregation. In: Proceedings of 2001 International Symposium on Nonlinear Theory and Its Applications, Miyagi, Japan, pp. 1–7 (2001)
Grünbaum, D., Okubo, A.: Modelling social animal aggregations. In: Levin, S.A. (ed) Frontiers in Mathematical Biology, Berlin, pp.296–325 (1994)
Mogilner A., Edelstein-Keshet L.: A non-local model for a swarm. J. Math. Biol. 38, 534–570 (1999)
Levin S.A.: Complex adaptive systems: exploring the known, the unknown and the unknowable. Bull. Am. Math. Soc. 40(1), 3–19 (2002)
Mogilner A., Edelstein-Keshet L., Bent L., Spiros A.: Mutual interactions, potentials, and individual distance in a social aggregation. J. Math. Biol. 47, 353–389 (2003)
Gazi V., Passino K.M.: Stability analysis of social foraging swarms. IEEE Trans. Syst. Man Cybern. Part B 34(1), 539–557 (2004)
Merrifield, A.J.: An Investigation of Mathematical Models For Animal Group Movement, Using Classical And Statistical Approaches. PhD thesis, University of Sydney, NSW, Australia (2006)
Sharma B., Vanualailai J.: Lyapunov stability of a nonholonomic car-like robotic system. Nonlinear Stud. 14(2), 143–160 (2007)
Sharma B., Vanualailai J., Singh S.: Tunnel passing maneuvers of prescribed formations. Int. J. Robust Nonlinear Control 24(5), 876–901 (2014)
Pappas G.J., Kyriakopoulos K.J: Stabilisation of non-holonomic vehicle under kinematic constraints. Int. J. Control 61(4), 933–947 (1995)
Raghuwaiya K., Singh S.: Formation types of multiple steerable 1-trailer mobile robots via split/rejoin maneuvers. N. Z. J. Math. 43, 7–21 (2013)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kumar, S.A., Vanualailai, J. & Sharma, B. Lyapunov-Based Control for a Swarm of Planar Nonholonomic Vehicles. Math.Comput.Sci. 9, 461–475 (2015). https://doi.org/10.1007/s11786-015-0243-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11786-015-0243-z
Keywords
- Lagrangian swarm model
- Lyapunov stability
- Lyapunov function
- Nonholonomic mobile robots
- Control of multiple robots
- Collision avoidance
- Swarm intelligence