A Bézier Curve-Based Approach for Path Planning in Robot Soccer

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)


This paper presents an efficient, Bézier curve-based path planning approach for robot soccer, which combines the function of path planning, obstacle avoidance, path smoothing and posture adjustment together. The locations of obstacles are considered as control points of Bézier curve, then according to the velocity and orientation of end points, a smooth curvilinear path can be planned in real time. For the sake of rapid reaching, it is necessary to decrease the turning radius. Therefore a new construction of curve is proposed to optimize the shape of Bézier path.


Bézier curve path planning robot soccer 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bruce, J., Veloso, M.: Real-time randomized path planning for robot navigation. In: International Conference on Intelligent Robots and Systems, IROS 2002, Lausanne, Switzerland, vol. 3, pp. 2383–2388. IEEE (September 2002)Google Scholar
  2. 2.
    Cheng, G., Gu, J., Bai, T., Majdalawieh, O.: A new efficient control algorithm using potential field: extension to robot path tracking. In: Electrical and Computer Engineering, CCECE 2004, Niagara Falls, Canada, vol. 4, pp. 2035–2040. IEEE (May 2004)Google Scholar
  3. 3.
    Farin, G., Hoschek, J., Kim, M.S.: Handbook of Computer Aided Geometric Design, 1st edn. North Holland (August 2002)Google Scholar
  4. 4.
    Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer Graphics: Principles and Practice, 2nd edn. Addison-Wesley (September 2004)Google Scholar
  5. 5.
    Ge, S.S., Cui, Y.J.: New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation 16(5), 615–620 (2000)CrossRefGoogle Scholar
  6. 6.
    Jolly, K.G., Sreerama Kumar, R., Vijayakumar, R.: A bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robotics and Autonomous Systems 57, 23–33 (2009)CrossRefGoogle Scholar
  7. 7.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5(1), 90–98 (1986)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kim, J., Ostrowski, J.P.: Motion planning a aerial robot using rapidly-exploring random trees with dynamic constraints. In: IEEE International Conference on Robotics and Automation, ICRA 2003, Taipei, China, vol. 2, pp. 2200–2205. IEEE (September 2003)Google Scholar
  9. 9.
    Kim, J.H., Kim, D.H., Kim, Y.J., Seow, K.T.: Soccer Robotics, vol. 11. Springer (2004)Google Scholar
  10. 10.
    Kim, J.H., Kim, K.C., Kim, D.H., Kim, Y.J., Vadakkepat, P.: Path planning and role selection mechanism for soccer robots. In: IEEE International Conference on Robotics and Automation, ICRA 1998, Leuven, Belgium, vol. 4, pp. 3216–3221. IEEE (May 1998)Google Scholar
  11. 11.
    Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: IEEE International Conference on Robotics and Automation, Sacramento, CA, USA, pp. 1398–1404. IEEE (April 1991)Google Scholar
  12. 12.
    Park, M.G., Jeon, J.H., Lee, M.C.: Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In: IEEE International Symposium on Industrial Electronics, ISIE 2001, Pusan, Korea, vol. 3, pp. 1530–1535. IEEE (June 2001)Google Scholar
  13. 13.
    Park, M.G., Lee, M.C.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: International Conference on Advanced Intelligent Mechatronics, AIM 2003, Kobe, Japan, vol. 2, pp. 735–740. IEEE (July 2003)Google Scholar
  14. 14.
    Rimon, E., Koditschek, D.E.: Exact robot navigation using artificial potential functions. IEEE Transactions on Robotics and Automation 8(5), 501–518 (1992)CrossRefGoogle Scholar
  15. 15.
    Rodriguez, S., Tang, X., Lien, J.M., Amato, N.M.: An obstacle-based rapidly-exploring random tree. In: IEEE International Conference on Robotics and Automation, ICRA 2006, Orlando, USA, pp. 895–900. IEEE (May 2006)Google Scholar
  16. 16.
    Vadakkepat, P., Tan, K.C., Wang, M.L.: Evolutionary artificial potential fields and their application in real time robot path planning. In: Evolutionary Computation, CEC 2000, La Jolla, CA, USA, vol. 1, pp. 256–263. IEEE (July 2000)Google Scholar
  17. 17.
    Warren, C.W.: Multiple robot path coordination using artificial potential fields. In: IEEE International Conference on Robotics and Automation, ICRA 1990, Cincinnati, Ohio, USA, vol. 1, pp. 500–505. IEEE (May 1990)Google Scholar
  18. 18.
    Yan, L., Liang, J.: An extension of the bézier model. Applied Mathematics and Computation 218(6), 2863–2879 (2011)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Yi, Z., Heng, P.A., Vadakkepat, P.: Absolute periodicity and absolute stability of delayed neural networks. IEEE Transactions on Circuits and Systems 49(2), 256–261 (2002)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Yun, X., Tan, K.C.: A wall-following method for escaping local minima in potential field based motion planning. In: International Conference on Advanced Robotics, ICAR 1997, Monterey, Canada, pp. 421–426. IEEE (July 1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical Engineering, School of Electrical and Electronic EngineeringHubei University of TechnologyWuhanChina
  2. 2.Department of Computer Science, Faculty of Electrical Engineering and Computer ScienceVŠB – Technical University of OstravaOstravaCzech Republic

Personalised recommendations