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Cyclic pursuit in a multi-agent robotic system with double-integrator dynamics under linear interactions

Published online by Cambridge University Press:  23 April 2013

Balaji R. Sharma
Affiliation:
Cooperative Distributed Systems Laboratory, School of Dynamic Systems, University of Cincinnati, Cincinnati, OH 45220, USA E-mail: [email protected]
Subramanian Ramakrishnan*
Affiliation:
Center for Nonlinear Dynamics and Control, Villanova University, Villanova, PA 19085, USA
Manish Kumar
Affiliation:
Cooperative Distributed Systems Laboratory, Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, OH 43606, USA E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

We investigate the controlled realization of a stable circular pursuit model in a multi-agent robotic system described by double-integrator dynamics with homogeneous controller gains. The dynamic convergence of the system starting from a randomly chosen, non-overlapping initial configuration to a sustained, stable pursuit configuration satisfying velocity matching and uniform inter-agent separation is demonstrated using the proposed control framework. The cyclic pursuit configuration emerges from local, linear, inter-agent interactions and is shown to be robust under stochastic perturbations of small and moderate intensities. The stability criterion discussed in this work is independent of the number of agents, permitting dynamic addition/deletion of agents without affecting overall system stability. Experimental results that validate the key theoretical results are also presented. Potential applications of the results obtained include cooperative perimeter tracking and resource distribution applications such as border patrol and wildfire monitoring.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Brocard, H., Nouvo Corres. Math. 3 (280) (1877).Google Scholar
2.Brocard, H., Nouvo Corres. Math. 6 (211) (1880).Google Scholar
3.Lucas, E., Nouvo Corres. Math. 3 (175) (1877).Google Scholar
4.Bernhart, H., “Polygons of pursuit,” Scr. Math. 24, 2350 (1959).Google Scholar
5.Watton, A. and Kydon, D. W., “Analytical aspects of the n-bug problem,” Am. J. Phys. 37, 220221 (1969).CrossRefGoogle Scholar
6.Klamkin, M. S. and Newman, D. J., “Cyclic pursuit or the ‘three bugs problem’,” Am. Math. Mon. 78 (6), 631639 (1971).Google Scholar
7.Behroozi, F. and Gagnon, R., “Cyclic pursuit in a plane,” J. Math. Phys. 20 (11), 22122216 (1979).CrossRefGoogle Scholar
8.Yamaguchi, H., “A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations,” Robot. Auton. Syst. 43 (4), 257282 (2003).CrossRefGoogle Scholar
9.Ren, W., Beard, R. W. and Atkins, E., “Information consensus in multivehicle cooperative control: Collective group behavior through local interaction,” IEEE Control Syst. Mag. 27 (2), 7182 (2007).Google Scholar
10.Joordens, M. A. and Jamshidi, M., “Consensus control for a system of underwater swarm robots,” IEEE Syst. J. 4 (1), 6573 (2010).CrossRefGoogle Scholar
11.Kumar, M., Garg, D. P. and Kumar, V., “Segregation of heterogeneous units in a swarm of robotic agents,” IEEE Trans. Autom. Control 55 (3), 743748 (2010).CrossRefGoogle Scholar
12.Gazi, V., Fidan, B., Hanay, Y. S. and Koksal, M. I., “Aggregation, foraging, and formation control of swarms with non-holonomic agents using potential functions and sliding mode techniques,” Turk. J. Electr. Eng. 15 (2), 149168 (2007).Google Scholar
13.Marshall, J. A., Broucke, M. E. and Francis, B. A., “Formations of vehicles in cyclic pursuit,” IEEE Trans. Autom. Control 49 (11), 19631974 (2004).CrossRefGoogle Scholar
14.Marshall, J. A., Broucke, M. E. and Francis, B. A., “Pursuit formations of unicycles,” Automatica 42, 312 (2006).CrossRefGoogle Scholar
15.Sinha, A. and Ghose, D., “Control of multiagent systems using linear cyclic pursuit with heterogenous controller gains,” J. Dyn. Syst. Meas. Control 129, 742749 (2007).CrossRefGoogle Scholar
16.Sinha, A. and Ghose, D., “Generalization of linear cyclic pursuit with application to rendezvous of multiple autonomous agents,” IEEE Trans. Autom. Control 51 (11), 18191824 (2006).CrossRefGoogle Scholar
17.Sinha, A. and Ghose, D., “Generalization of nonlinear cyclic pursuit,” Automatica 43, 19541960 (2007).CrossRefGoogle Scholar
18.Samiloglu, A., Gazi, V. and Koku, B., “Asynchronous cyclic pursuit,” Lecture Notes in Computer Science 4095, 667678 (2006).CrossRefGoogle Scholar
19.Kim, T. and Hara, S., “Stabilization of Multi-Agent Dynamical Systems for Cyclic Pursuit Behavior,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico (Dec. 9–11, 2008) pp. 43704375.Google Scholar
20.Pavani, R., “D-stability from a Numerical Point of View,” SIAM Conference on Applied Algebra, Williamsburg, VA, USA (Jul. 15–19, 2003).Google Scholar
21.Ramirez, J. L., Pavone, M., Frazzoli, E. and Miller, D. W., “Distributed Control of Spacecraft Formation via Cyclic Pursuit: Theory and Experiments,” Proceedings of the American Control Conference, St. Louis, MO, USA (Jun. 10–12, 2009) pp. 48114817.Google Scholar
22.Ramirez, J. L., Pavone, M., Frazzoli, E. and Miller, D. W., “Distributed control of spacecraft formations via cyclic pursuit: Theory and experiments,” J. Guid. Control Dyn. 33 (5), 16551669 (2010).CrossRefGoogle Scholar
23.Ding, W., Yan, G. and Lin, Z., “Collective motions and formations under pursuit strategies on directed acyclic graphs,” Automatica 46 (1), 174181, 2010.CrossRefGoogle Scholar
24.Rogge, J. A. and Aeyels, D., “Vehicle platoons through ring coupling,” IEEE Trans. Autom. Control 53 (6), 13701377 (2008).CrossRefGoogle Scholar
25.Cao, Y., Stuart, D., Ren, W. and Meng, Z., “Distributed Containment Control for Double-Integrator Dynamics: Algorithms and Experiments,” Proceedings of the American Control Conference, Baltimore, MD, USA (Jun. 30–Jul. 2, 2010) pp. 38303835.Google Scholar
26.Regmi, A., Sandoval, R., Byrne, R., Tanner, H. and Abdallah, C., “Experimental implementation of flocking algorithms in wheeled mobile robots,” Proc. Am. Control Conf. 2, 49174922 (2005).Google Scholar
27.Delsart, V. and Fraichard, T., “Reactive Trajectory Deformation to Navigate Dynamic Environments,” Proceedings of the European Robotics Symposium, Prague, Czech Republic (Mar. 26–28, 2008) pp. 233241.Google Scholar
28.Morbidi, F., Ray, C. and Mariottin, G., “Cooperative Active Target Tracking for Heterogeneous Robots with Application to Gait Monitoring,” IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA (Sep. 25–30, 2011) pp. 36083613.Google Scholar
29.Varga, R. S., “Gershgorin disks, Brauer ovals of Cassini (a vindication) and Brualdi sets,” Information 4 (2), 171178 (2001).Google Scholar
30.Melnikov, V. G., “About root-clustering in sophisticated regions,” Latest Trends on Systems 1, 297300 (2010).Google Scholar
31.de Paor, A., “The root locus method: Famous curves, control designs and non-control applications,” Int. J. Electr. Eng. Educ. 37 (4), 344356 (2000).CrossRefGoogle Scholar
32.Mondada, F., Franzi, E. and Guignard, A., “The Development of Khepera,” Experiments with the Mini-Robot Khepera, Proceedings of the First International Khepera Workshop, HNI-Verlagsschriftenreihe, Heinz Nixdorf Institut, Paderborn, Germany (1999) pp. 714.Google Scholar
33.Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J. and Martinoli, A., “Swistrack - A Flexible Open Source Tracking Software for Multi-Agent Systems,” Intelligent Robots and Systems, 2008 (IROS 2008). IEEE/RSJ International Conference on, Nice, France (Sep. 22–26, 2008) pp. 40044010.CrossRefGoogle Scholar