Swarming Intelligence of 1-Trailer Systems

  • Jai RajEmail author
  • Krishna Raghuwaiya
  • Shonal Singh
  • Bibhya Sharma
  • Jito Vanualailai
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)


In this paper, we propose a new solution to motion planning and control problem for a flock of 1-trailer systems. A set of artificial potential field functions is proposed for the flock of 1-trailer robots via the Lyapunov-based control scheme for the avoidance of swarm of boids and attraction to their designated targets. The dynamic environment for the first time includes a swarm of boids, which is governed separately by a system of ODE’s. The swarm exhibits collective emergent behaviors in the vicinity of the workspace while the flock of 1-trailer systems safely maneuver from their initial configuration to designated targets. The effectiveness of the control laws is demonstrated via computer simulations. The novelty of the paper lies in the simplicity of the controllers and the ease in the treatment of the dynamic environment.


Swarm Obstacle avoidance 1-trailer system Emergent Stability 


  1. 1.
    Berenson, D., Srinivasa, S.S., Ferguson, D., Collet, A., Kuffner, J.J.: Manipulation planning with workspace goal regions. In: IEEE International Conference on Robotics and Automation, 2009. ICRA’09, pp. 618–624. IEEE, 2009Google Scholar
  2. 2.
    Erdmann, M., Lozano-Perez, T.: On multiple moving objects. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1419–1424, 1986Google Scholar
  3. 3.
    Alami, R., Fleury, S., Herrb, M., Ingrand, F., Robert, F.: Multi-robot cooperation in the martha project. IEEE Robot. Autom. Mag. 5, 36–47 (1998)CrossRefGoogle Scholar
  4. 4.
    Gerkey, B.P., Mataric, M.J.: Auction methods for multirobot coordination. IEEE Trans. Robot. Autom. 18, 758–768 (2002)CrossRefGoogle Scholar
  5. 5.
    Kostic, D., Adinandra, S., Caarls, J., Nijmeijer, H.: Collision-free motion coordination of unicycle multi-agent systems. In: 2010 American Control Conference, America, 2010Google Scholar
  6. 6.
    Latombe, J.C.: Robot motion planning. Kluwer Academic Publishers, USA (1991)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kant, K., Zucker, S.W.: Toward efficiency trajectory planning: the path-velocity decomposition. Int. J. Robot. Res. 5(3), 72–89 (1986)CrossRefGoogle Scholar
  8. 8.
    Prasad, A., Sharma, A., Vanualailai, J.: A solution to the motion planning and control problem of a car—like robot via a single—layer perceptron. Robotica 32(6), 935–952. ISSN 0263–5747, 2014Google Scholar
  9. 9.
    Prasad, A., Sharma, A., Vanualailai, J.: A new stabilizing solution for motion planning and control of multiple robots. Robotica, First V, 1–19. ISSN 0263–5747, 2014Google Scholar
  10. 10.
    Sharma, B., Vanualailai, J., Raghuwaiya, K., Prasad, A.: New potential field functions for motion planning and posture control of 1-trailer systems. Int. J. Math. Comput. Sci. 3(1), 45–71 (2008)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Mogilner, A., Edelstein-Keshet, L., Bent, L., Spiros, A.: Mutual interactions, potentials, and individual distance in a social aggregation. J. Math. Biol. 47, 352–389 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model, in computer graphics. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp 25–34. New York, USA (1987)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jai Raj
    • 1
    Email author
  • Krishna Raghuwaiya
    • 1
  • Shonal Singh
    • 1
  • Bibhya Sharma
    • 1
  • Jito Vanualailai
    • 1
  1. 1.School of Computing, Information and Mathematical SciencesThe University of the South PacificSuvaFiji

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