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Journal of Intelligent & Robotic Systems

, Volume 71, Issue 1, pp 109–123 | Cite as

Closed-Curve Path Tracking for Decentralized Systems of Multiple Mobile Robots

  • Lorenzo Sabattini
  • Cristian Secchi
  • Cesare Fantuzzi
Article

Abstract

In this paper we address the problem of making a group of mobile robots cooperatively track an assigned path. We consider paths described by completely arbitrarily shaped closed curves. The proposed control strategy is a fully decentralized algorithm and it does not require any global synchronization. The desired behavior is obtained by means of some properly designed artificial potential functions. Analytical proofs are provided to show the asymptotic convergence of the system to the desired behavior. Matlab simulations and experiments on real robots are described as well for validation purposes.

Keywords

Multi-robot systems Cooperative path tracking Multi-robot coordination 

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References

  1. 1.
    Beard, R.W., Stepanyan, V.: Synchronization of information in distributed multiple vehicle coordinated control. In: In Proceedings of IEEE Conference on Decision and Control, pp. 2029–2034 (2003)Google Scholar
  2. 2.
    Bellingham, J., Tillerson, M., Richards, A., How, J.P.: Multi-task allocation and path planning for cooperative UAVs. In: Cooperative Control: Models, Applications, and Algorithms, pp. 23–41 (2003)Google Scholar
  3. 3.
    Buck, S., Weber, U., Beetz, M., Schmitt, T.: Multi-robot path planning for dynamic environments: a case study. In: Proceedings of International Conference on Intelligent Robots and Systems, vol. 3, pp. 1245–1250. IEEE/RSJ (2001)Google Scholar
  4. 4.
    Bullo, F., Cortés, J., Martínez, S.: Distributed Control of Robotic Networks. Applied Mathematics Series. Princeton University Press (2009). Electronically available at http://coordinationbook.info
  5. 5.
    Cao, Y., Fierro, R.: Dynamic boundary tracking using dynamic sensor nets. In: Proceedings of the 45th IEEE Conference on Decision and Control (2006)Google Scholar
  6. 6.
    Choi, H.L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics 25(4), 912–926 (2009)CrossRefGoogle Scholar
  7. 7.
    Choudhury, B.B., Biswal, B.B.: An optimized multirobot task allocation. In: Proceedings of the First International Conference on Emerging Trends in Engineering and Technology, pp. 320–325 (2008)Google Scholar
  8. 8.
    Clark, C.M., Rock, S.M., Latombe, J.C.: In: Robotics and Automation. In: Proceedings of the IEEE International Conference on ICRA ’03, vol. 3, pp. 4222–4227 (2003)Google Scholar
  9. 9.
    Dias, M.B., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: A survey and analysis. Proc. IEEE 97(7), 1257–1270 (2006)CrossRefGoogle Scholar
  10. 10.
    Dimarogonas, D.V., Zavlanos, M.M., Loizou, S.G., Kyriakopoulos, K.J.: Decentralized motion control of multiple holonomic agents under input constraints. In: Proceedings of the 42nd IEEE Conference on Decision and Control, vol. 4, pp. 3390–3395 (2003)Google Scholar
  11. 11.
    Do, K.D.: Formation tracking control of unicycle-type mobile robots with limited sensing ranges. IEEE Trans. Control Syst. Technol. 16, 527–538 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Egerstedt, M., Hu, X., Stotsky, A.: Control of mobile platforms using a virtual vehicle approach. IEEE Trans. Automat. Contr. 46(11), 1777–1782 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Fax, J.A., Murray, R.M.: Information flow and cooperative control of vehicle formations. IEEE Trans. Automat. Contr. 49(9), 1465–1476 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hsieh, M.A., Loizou, S., Kumar, V.: Stabilization of multiple robots on stable orbits via local sensing. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2312–2317 (2007)Google Scholar
  15. 15.
    Jang, S., Song, G., Hong, S.K.: Dynamic boundary tracking in active sensor networks. In: Proceedings of the International Conference on Control, Automation and Systems (2007)Google Scholar
  16. 16.
    Jin, Z., Bertozzi, A.L.: Environmental boundary tracking and estimation using multiple autonomous vehicles. In: Proceedings of the 46th IEEE Conference on Decision and Control (2007)Google Scholar
  17. 17.
    Khalil, H.K.: Nonlinear Systems, 3rd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  18. 18.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Rob. Res. 5(1), 90–98 (1986)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Lin, M.C., Sud, A., Van den Berg, J., Gayle, R., Curtis, S., Yeh, H., Guy, S., Andersen, E., Patil, S., Sewall, J., Manocha, D.: Real-time path planning and navigation for multi-agent and crowd simulations. In: Lecture Notes in Computer Science, Motion in Games, pp. 23–32. Springer, Berlin (2008)Google Scholar
  20. 20.
    Marcolino, L.S., Chaimowicz, L.: No robot left behind: Coordination to overcome local minima in swarm navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation (2008)Google Scholar
  21. 21.
    Mercker, T., Casbeer, D.W., Millet, P.T., Akella, M.R.: An extension of consensus-based auction algorithms for decentralized, time-constrained task assignment. In: Proceedings of the American Control Conference, pp. 6324–6329 (2010)Google Scholar
  22. 22.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, pp. 59–65 (2009)Google Scholar
  23. 23.
    Oriolo, G., Luca, A.D., Vendittelli, M.: WMR control via dynamic feedback linearization: Design, implementation, and experimental validation. In: IEEE Transactions On Control Systems Technology (2002)Google Scholar
  24. 24.
    Piege, L., Tiller, W.: The NURBS Book. Springer, Berlin (1995–1997)Google Scholar
  25. 25.
    Ronzoni, D., Olmi, R., Secchi, C., Fantuzzi, C.: AGV global localization using indistinguishable artificial landmarks. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 287–292 (2011)Google Scholar
  26. 26.
    Sabattini, L., Secchi, C., Fantuzzi, C.: Potential based control strategy for arbitrary shape formations of mobile robots. In: Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems, pp. 3762–3767 (2009)Google Scholar
  27. 27.
    Sabattini, L., Secchi, C., Fantuzzi, C., Possamai, D.: Tracking of closed-curve trajectories for multi-robot systems. In: Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems, pp. 6089–6094 (2010)Google Scholar
  28. 28.
    Sabattini, L., Secchi, C., Fantuzzi, C.: Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation. Auton. Robots 30(4), 385–397 (2011)CrossRefGoogle Scholar
  29. 29.
    Schumacher, C., Chandler, P., Rasmussen, S.: Task allocation for wid area search munition. In: Proceedings of the American Control Conference, pp. 1917–1922 (2002)Google Scholar
  30. 30.
    Susca, S., Bullo, F., Martínez, S.: Synchronization of beads on a ring. In: Proceedings of the 46th IEEE Conference on Decision and Control (2007)Google Scholar
  31. 31.
    Tsalatsanis, A., Yalcin, A., Valavanis, K.: Optimized task allocation in cooperative robot teams. In: Proceedings of the IEEE Mediterranean Conference on Control and Automation, pp. 270–275 (2009)Google Scholar
  32. 32.
    Warren, C.W.: Multiple robot path coordination using artificial potential fields. IEEE Int. Conf. Robot. Autom. 1, 500–505 (1990)CrossRefGoogle Scholar
  33. 33.
    Wolf, M.T., Burdick, J.W.: Artificial potential functions for highway driving with collision avoidance. In: Proceedings of the IEEE International Conference on Robotics and Automation (2008)Google Scholar
  34. 34.
    Wurman, P., D’Andrea, R., Mountz, M.: Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 29(1), 9–20 (2008)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Lorenzo Sabattini
    • 1
  • Cristian Secchi
    • 1
  • Cesare Fantuzzi
    • 1
  1. 1.Department of Engineering Sciences and Methods (DISMI)University of Modena and Reggio EmiliaReggio EmiliaItaly

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