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Path Planning for a Multi-robot System with Decentralized Control Architecture

  • Fethi Metoui
  • Boumedyen BoussaidEmail author
  • Mohamed Naceur Abdelkrim
Chapter
  • 26 Downloads
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 270)

Abstract

In this chapter we have studied the path planning problem of a group of autonomous Wheeled Mobile Robots in a very dynamic workspace. The work focuses on the combination of artificial potential field approach with the decentralized architecture to coordinate the movements of robots. The technique implemented is adapted to solve the path planning problem for a multi-robot system. So, the problem of coordination of robots at the intersection zone is solved by the use of the priority allocation method. We also used the neighborhood detection technique to reduce the influence area of each robot and to optimize the time of calculation. The solution is tested and simulated with Matlab/Simulink.

Keywords

Wheeled mobile robot Multi-robot system Decentralized control architecture Artificial potential field Priority conflict management 

References

  1. 1.
    Arrichiello, F.: Coordination control of multiple mobile robots. Dipartimento di Automazione, Elettromagnetismo, Ingegneria Dell’informazione e Matematica Industriale (2006)Google Scholar
  2. 2.
    Baxter, J.L., Burke, E., Garibaldi, J.M., Norman, M.: Multi-robot search and rescue: a potential field based approach. Autonomous Robots and Agents, pp. 9–16. Springer, Berlin (2007)Google Scholar
  3. 3.
    Bennewitz, M., Burgard, W., Thrun, S.: Optimizing schedules for prioritized path planning of multi-robot systems. 271–276 (2001)Google Scholar
  4. 4.
    Benzerrouk, A., Adouane, L., Lequievre, L., Martinet, P.: Navigation of multi-robot formation in unstructured environment using dynamical virtual structures. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5589–5594. IEEE (2010)Google Scholar
  5. 5.
    Dahl, T.S., Matarić, M., Sukhatme, G.S.: Multi-robot task allocation through vacancy chain scheduling. Robot. Auton. Syst. 57(6–7), 674–687 (2009)CrossRefGoogle Scholar
  6. 6.
    Defoort, M., Kokosy, A., Floquet, T., Perruquetti, W., Palos, J.: Motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: a distributed receding horizon approach. Robot. Auton. Syst. 57(11), 1094–1106 (2009)CrossRefGoogle Scholar
  7. 7.
    Dhaouadi, R., Hatab, A.: Dynamic modelling of differential-drive mobile robots using Lagrange and Newton-Euler methodologies: a unified framework. Adv. Robot. Autom. 2(2) (2013)Google Scholar
  8. 8.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 23–33 (1997)Google Scholar
  9. 9.
    Gaillard, F.: Approche cognitive pour la planification de trajectoire sous contraintes. PhD thesis, Universite des Sciences et Technologie de Lille-Lille I (2012)Google Scholar
  10. 10.
    Glavaski, D., Volf, M., Bonkovic, M.: Robot motion planning using exact cell decomposition and potential field methods. In: Proceedings of the 9th WSEAS International Conference on Simulation, vol. 8, pp. 126–131 (2009)Google Scholar
  11. 11.
    Guys, L.: Aircraft trajectory planning without conflict: biharmonic functions and harmonic navigation function. PhD thesis, Université Toulouse 3 Paul Sabatier (2014)Google Scholar
  12. 12.
    Haj Darwish, A., Joukhadar, A., Kashkash, M.: Using the bees algorithm for wheeled mobile robot path planning in an indoor dynamic environment. Cogent Eng. 5(1), 1426539 (2018)CrossRefGoogle Scholar
  13. 13.
    Hassan, A.M., Elias, C.M., Shehata, O.M., Morgan, E.I.: A global integrated artificial potential field/virtual obstacles path planning algorithm for multi-robot system applications (2017)Google Scholar
  14. 14.
    Kuo, P.-H., Li, T.-H.S., Chen, G.-Y., Ho, Y.-F., Lin, C.-J.: A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. Knowl. Eng. Rev. 32 (2017)Google Scholar
  15. 15.
    Li, G., Chou, W.: Path planning for mobile robot using self-adaptive learning particle swarm optimization. Sci. China Inf. Sci. 61(5), 052204 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ma, Y., Zheng, G., Perruquetti, W., Qiu, Z.: Motion planning for non-holonomic mobile robots using the i-PID controller and potential field. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 3618–3623 (2014)Google Scholar
  17. 17.
    Matoui, F., Boussaid, B., Abdelkrim, M.N.: Local minimum solution for the potential field method in multiple robot motion planning task. In: 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 452–457. IEEE (2015)Google Scholar
  18. 18.
    Matoui, F., Boussaid, B., Abdelkrim, M.N.: Path planning of two robots in dynamic workspace based on potential field. In: 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 267–272. IEEE (2017)Google Scholar
  19. 19.
    Matoui, F., Boussaid, B., Metoui, B., Frej, G., Abdelkrim, M.N.: Path planning of a group of robots with potential field approach: decentralized architecture. IFAC-PapersOnLine 50(1), 11473–11478 (2017)Google Scholar
  20. 20.
    Matoui, F., Boussaid, B., Abdelkrim, M.N.: Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach. SIMULATION: Trans. Soc. Model. Simul. Int. SAGE Publications, Sage UK, London, England (2018)Google Scholar
  21. 21.
    Mohanty, P.K., Parhi, D.R.: Controlling the motion of an autonomous mobile robot using various techniques: a review. J. Adv. Mech. Eng. 1(1), 24–39 (2013)Google Scholar
  22. 22.
    Montiel, O., Orozco-Rosas, U., Sepúlveda, R.: Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst. Appl. 42(12), 5177–5191 (2015)CrossRefGoogle Scholar
  23. 23.
    Moon, H., Luntz, J.: Prediction of equilibria of lifted logarithmic radial potential fields. Int. J. Robot. Res. 23(7–8), 747–762 (2004)CrossRefGoogle Scholar
  24. 24.
    Neto, A.A., Macharet, D.G., Campos, M.F.: Multi-agent rapidly-exploring pseudo-random tree. J. Intell. Robot. Syst. 89(1–2), 69–85 (2018)CrossRefGoogle Scholar
  25. 25.
    Parasuraman, S., Ganapathy, V., Shirinzadeh, B.: Behaviour based mobile robot navigation technique using AI system: experimental investigation on active media pioneer robot. IIUM Eng. J. 6(2) (2005)Google Scholar
  26. 26.
    Parker, L.E.: Multiple mobile robot systems. Springer Handbook of Robotics, pp. 921–941. Springer, Berlin (2008)Google Scholar
  27. 27.
    Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge (2011)Google Scholar
  28. 28.
    Solana, Y., Furci, M., Cortés, J., Franchi, A.: Multi-robot path planning with maintenance of generalized connectivity. In: 2017 IEEE 1st International Symposium on Multi-Robot and Multi-Agent Systems (2017)Google Scholar
  29. 29.
    Song, P., Kumar, V.: A potential field based approach to multi-robot manipulation. In: 2002 Proceedings IEEE International Conference on Robotics and Automation (ICRA’02), vol. 2, pp. 1217–1222. IEEE (2002)Google Scholar
  30. 30.
    Tan, J., Zhao, L., Wang, Y., Zhang, Y., Li, L.: The 3D path planning based on a* algorithm and artificial potential field for the rotary-wing flying robot. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 551–556. IEEE (2016)Google Scholar
  31. 31.
    Tzafestas, S.G.: Introduction to Mobile Robot Control (2013)Google Scholar
  32. 32.
    Varsos, K., Moon, H., Luntz, J.: Generation of quadratic potential force fields from flow fields for distributed manipulation. IEEE Trans. Robot. 22(1), 108–118 (2006)CrossRefGoogle Scholar
  33. 33.
    Yingchong, M.: Path planning and control of non-holonomic mobile robots. PhD thesis, Ecole Centrale de Lille (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Fethi Metoui
    • 1
  • Boumedyen Boussaid
    • 1
    Email author
  • Mohamed Naceur Abdelkrim
    • 1
  1. 1.National School of Engineers of GabesUniversity of GabesGabesTunisia

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