Wireless Personal Communications

, Volume 106, Issue 2, pp 577–592 | Cite as

Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot

  • Patience I. Adamu
  • Hilary I. OkagbueEmail author
  • Pelumi E. Oguntunde


Motion planning problem though widely studied in robotics is a difficult problem. It finds a feasible path from an initial position to a final position in an environment with obstacles. Recent researches do not just aim to find feasible paths but to find paths that are optimal in respect to time, distance and safety of the robots. Optimization based techniques have been proposed to solve this problem but some of them used techniques that may converge to local minimum and they seldom consider the speed of the technique. Hence this paper presents a fast and global motion planning algorithm for a mobile robot in a known environment with static obstacles. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. The coordinate values when generated by the customized algorithm are passed to the PSO algorithm which uses them to determine the shortest path between the two given end positions. We perform our experiments using four different environments with population sizes 100, 50, 20 and 10 in a 10 × 10 grid and our results are favorable.


Motion planning Optimization Particle swarm optimization Algorithm Robotics 



Covenant University is acknowledged for sponsoring this research.

Author Contributions

All the authors contributed immensely to this research. The paper is a product of optimization sub cluster of the Department of Mathematics, Covenant University, Ota.

Compliance with ethical standards

Conflicts of interest

The authors declare no conflict of interest.


  1. 1.
    Bayat, F., Najafinia, S., & Aliyari, M. (2018). Mobile robots path planning: Electrostatic potential field approach. Expert Systems with Applications, 100, 68–78.CrossRefGoogle Scholar
  2. 2.
    Denny, J., Greco, E., Thomas, S. L., & Amato, N. M. (2014). MARRT: Medial axis biased rapidly-exploring random trees. In Proceedings of IEEE international conference of robotics automation (ICRA) (pp. 90–97). Hong Kong, China.Google Scholar
  3. 3.
    Ekenna, C., Jacobs, S. A., Thomas, S. L., & Amato N. M. (2013). Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism. In: Proceedings of IEEE international conference on intelligent robotics systems (IROS), Tokyo, Japan.Google Scholar
  4. 4.
    Kim, J. J., & Lee, J. J. (2015). Trajectory optimization with particle swarm optimization for manipulator motion planning. IEEE Transactions on Industrial Informatics, 11(3), 620–631.CrossRefGoogle Scholar
  5. 5.
    LaValle, S. M., & Kuffner, J. J. (2001). Randomized kinodynamic planning. International Journal of Robotics Research, 20(5), 378–400.CrossRefGoogle Scholar
  6. 6.
    Lien, J.-M., Bayazit, O. B., Sowell, R.-T., Rodriguez, S., & Amato, N. M. (2004). Shepherding behaviors. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 4159–4164).Google Scholar
  7. 7.
    Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2017). A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Applied Soft Computing, 59, 68–76.CrossRefGoogle Scholar
  8. 8.
    Paquet, U., & Engelbrecht, A. P. (2003). Training support vector machines with particle swarms. In Proceedings of international joint conference on neural networks (IJCNN) conference (pp. 1593–1598).Google Scholar
  9. 9.
    Reif, J. H. (1979). Complexity of the mover’s problem and generalizations. In: Proceedings of IEEE symposium on foundations of computer science (FOCS) (pp. 421–427). San Juan, Puerto Rico.Google Scholar
  10. 10.
    Song, G., & Amato, N. M. (2001). Using motion planning to study protein folding pathways. In Proceedings of international conference Computer Molecular Biology (RECOMB) (pp. 287–296).Google Scholar
  11. 11.
    van den Bergh, F. (2002). An analysis of particle swarm optimizers (pp. 15–30). Ph.D. Thesis, Department of Computer Science, University of Pretoria.Google Scholar
  12. 12.
    Venu, G. G., & Ganesh, K. V. (2003). Evolving digital circuits using particle swarm. In Proceedings of international joint conference on neural networks (IJCNN) conference (pp. 468–471).Google Scholar
  13. 13.
    Wang, B., Li, S., Guo, J., & Chen, Q. (2018). Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm. Neurocomputing, 282, 42–51.CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Chen, C., & Liu, Q. (2016). Mobile robot path planning using ant colony algorithm. International Journal of Control and Automation, 9(9), 19–28.CrossRefGoogle Scholar
  15. 15.
    Zhou, Z., Wang, J., Zhu, Z., Yang, D., & Wu, J. (2018). Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik, 158, 639–651.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsCovenant UniversityOtaNigeria

Personalised recommendations