Formation Control of Multiple Rectangular Agents with Limited Communication Ranges

  • Thang Nguyen
  • Hung Manh La
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)


Formation control of multiple agents has attracted many robotic and control researchers recently because of its potential applications in various fields. This paper presents a novel approach to the formation control of multiple rectangular agents with limited communication ranges. The proposed distributed control algorithm is designed by utilizing an artificial potential function. The proposed control algorithm can guarantee fast formation performance and no collision among agents. As a result, the rectangular agents can move together and quickly form a pre-defined shape of formation such as straight line and circle, etc. Simulation results are conducted to demonstrate the effectiveness of the proposed algorithm.


Formation Control Task Allocation Intelligent Transportation System Mobile Sensor Network Propose Control Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fierro, R., Das, A., Spletzer, J., Esposito, J., Kumar, V., Ostrowski, J.P., Pappas, G., Taylor, C.J., Hur, Y., Alur, R., Lee, I., Grudic, G., Southall, B.: A framework and architecture for multi-robot coordination. Inter. J. of Robotics Research 21(10-11), 977–995 (2002)CrossRefGoogle Scholar
  2. 2.
    Cruz, D., McClintock, J., Perteet, B., Orqueda, O., Cao, Y., Fierro, R.: Decentralized cooperative control - a multivehicle platform for research in networked embedded systems. IEEE on Control Systems 27(3), 58–78 (2007)CrossRefGoogle Scholar
  3. 3.
    Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. on Automatic Control 51(3), 401–420 (2006)CrossRefMathSciNetGoogle Scholar
  4. 4.
    La, H.M., Sheng, W.: Dynamic targets tracking and observing in a mobile sensor network. J. of Robotics and Autonomous Sys. 60(7), 996–1009 (2012)CrossRefGoogle Scholar
  5. 5.
    La, H.M., Sheng, W.: Flocking control of a mobile sensor network to track and observe a moving target. In: IEEE Inter. Conf. on Robotics and Automation (ICRA), pp. 3129–3134 (2009)Google Scholar
  6. 6.
    Lynch, K.M., Yang, P., Freeman, R.A.: Decentralized environmental modeling by mobile sensor networks. IEEE Trans. on Robotics 24(3), 710–724 (2008)CrossRefGoogle Scholar
  7. 7.
    Cortes, J.: Distributed Kriged Kalman filter for spatial estimation. IEEE Trans. on Automatic Control 54(12), 2816–2827 (2009)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Lilienthal, A.J., Reggente, M., Trincavelli, M., Blanco, J.L., Gonzalez, J.: A statistical approach to gas distribution modeling with mobile robots-the kernel dm+v algorithm. In: IEEE Inter. Conf. on Intell. Robot Sys., pp. 570–576 (2009)Google Scholar
  9. 9.
    La, H.M., Sheng, W.: Distributed sensor fusion for scalar field mapping using mobile sensor networks. IEEE Trans. on Cybernetics 43(2), 766–778 (2013)CrossRefGoogle Scholar
  10. 10.
    La, H.M., Sheng, W., Chen, J.: Cooperative and active sensing in mobile sensor networks for scalar field mapping. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems (99), 1–12 (May 2014)Google Scholar
  11. 11.
    La, H.M., Lim, R.S., Du, J., Zhang, S., Yan, G., Sheng, W.: Development of a small-scale research platform for intelligent transportation systems. IEEE Trans. on Intelligent Transportation Systems 13(4), 1753–1762 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., De Silva, C.W.: Sequential q -learning with kalman filtering for multirobot cooperative transportation. IEEE/ASME Trans. on Mechatronics 15(2), 261–268 (2010)CrossRefGoogle Scholar
  13. 13.
    Chen, J., Sun, D.: Coalition-based approach to task allocation of multiple robots with resource constraints. IEEE Trans. on Automation Science and Engineering 9(3), 516–528 (2012)CrossRefGoogle Scholar
  14. 14.
    Binetti, G., Naso, D., Turchiano, B.: Decentralized task allocation for surveillance systems with critical tasks. Robotics and Autonomous Systems 61(12), 1653–1664 (2013)CrossRefGoogle Scholar
  15. 15.
    Vig, L., Adams, J.A.: Multi-robot coalition formation. IEEE Trans. on Robotics 22(4), 637–649 (2006)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Parker, L.E.: Iq-asymtre: Forming executable coalitions for tightly coupled multirobot tasks. IEEE Trans. on Robotics 29(2), 400–416 (2013)CrossRefGoogle Scholar
  17. 17.
    Korsah, G.A., Stentz, A., Dias, M.B.: A comprehensive taxonomy for multi-robot task allocation. The International Journal of Robotics Research 32(12), 1495–1512 (2013)CrossRefGoogle Scholar
  18. 18.
    La, H.M., Lim, R., Sheng, W.: Multi-robot cooperative learning for predator avoidance. IEEE Trans. on Control Systems Technology (99), 1–12 (2014)Google Scholar
  19. 19.
    La, H.M., Sheng, W.: Flocking control of multiple agents in noisy environments. In: IEEE Inter. Conf. on Robotics and Automation (ICRA), pp. 4964–4969 (2010)Google Scholar
  20. 20.
    Hu, J., Feng, G.: Distributed tracking control of leader–follower multi–agent systems under noisy measurement. Automatica 46(8), 1382–1387 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Li, W., Spong, M.W.: Stability of general coupled inertial agents. IEEE Trans. on Automatic Control 55(6), 1411–1416 (2010)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Li, Z., Liu, X., Ren, W., Xie, L.: Distributed tracking control for linear multiagent systems with a leader of bounded unknown input. IEEE Transactions on Automatic Control 58(2), 518–523 (2013)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Li, W., Spong, M.W.: Analysis of flocking of cooperative multiple inertial agents via a geometric decomposition technique. IEEE Trans. on Systems, Man, and Cybernetics: Systems PP(99), 1 (2014)Google Scholar
  24. 24.
    La, H.M., Sheng, W.: Multi-agent motion control in cluttered and noisy environments. J. of Communications 8(1), 32–46 (2013)CrossRefGoogle Scholar
  25. 25.
    Do, K.D.: Flocking for multiple elliptical agents with limited communication ranges. IEEE Trans. on Robotics 27(5), 931–942 (2011)CrossRefGoogle Scholar
  26. 26.
    Do, K.D.: Formation control of multiple elliptical agents with limited sensing ranges. Automatica 48(7), 1330–1338 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  27. 27.
    Dimarogonas, D.V., Kyriakopoulos, K.J.: Connectedness preserving distributed swarm aggregation for multiple kinematic robots. IEEE Transactions on 24(5), 1213–1223 (2008)Google Scholar
  28. 28.
    Zavlanos, M.M., Tanner, H.G., Jadbabaie, A., Pappas, G.J.: Hybrid control for connectivity preserving flocking. IEEE Trans. on Automatic Control 54(12), 2869–2875 (2009)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Kan, Z., Dani, A.P., Shea, J.M., Dixon, W.E.: Network connectivity preserving formation stabilization and obstacle avoidance via a decentralized controller. IEEE Trans. on Automatic Control 57(7), 1827–1832 (2012)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Do, K.D.: Bounded assignment formation control of second-order dynamic agents. IEEE/ASME Transactions on Mechatronics 19(2), 477–489 (2014)CrossRefGoogle Scholar
  31. 31.
    La, H.M., Sheng, W.: Adaptive flocking control for dynamic target tracking in a mobile sensor network. In: IEEE Inter. Conf. on Intell. Robots and Sys (IROS), pp. 4843–4848.Google Scholar
  32. 32.
    Cao, Y., Yu, W., Ren, W., Chen, G.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Transactions on Industrial Informatics 9(1), 427–438 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thang Nguyen
  • Hung Manh La

There are no affiliations available

Personalised recommendations