Skip to main content
SpringerLink
Log in

Simple Global Path Planning Algorithm Using a Ray-Casting and Tracking Method

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper proposes a simple global path planning algorithm using a ray’s feature of straight in nature with a random reflection model. The ray-casting and tracking (RCT) method is designed to solve global single-query path planning problems with fast convergence time. It is a random sampling-based algorithm that reflects rays with the maximum search length, which is a line of sight restricted only by the obstacles blocking the rays. RCT guarantees a competent path that follows an obstacle’s edges like a path generated by a visibility graph (VG). We demonstrated RCT’s superior performance in terms of both convergence time and path length on various environments that have their own features compared to other well-known path planning algorithms such as the A*, rapidly-exploring random trees, and VG.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Siméon, T., Laumond, J.P., Nissoux, C.: Visibility-based probabilistic roadmaps for motion planning. Adv. Robot. 14(6), 477–493 (2000)

    Article  Google Scholar 

  2. Bhattacharya, P., Gavrilova, M.L.: Voronoi diagram in optimal path planning. In: Proc. IEEE Int. Symp. Voronoi Diagrams in Sci. Eng., pp. 38–47 (2007)

  3. Sleumer, N.H., Tschichold-Gürman, N.: Exact cell decomposition of arrangements used for path planning in robotics. Technical report. Switzerland: Institute of Theoretical Computer Science Swiss Federal Institute of Technology Zurich (1999)

  4. Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proc. IEEE Int. Conf. Robot. Autom., vol. 2, pp. 1398–1404 (1991)

  5. Hwang, Y.K., Ahuja, N.: A potential field approach to path planning. IEEE Trans. Robot. Autom. 8(1), 23–32 (1992)

    Article  Google Scholar 

  6. Kim, Y.J., Kim, J.H., Kwon, D.S.: Evolutionary programming-based univector field navigation method for past mobile robots. IEEE Trans. Syst., Man, Cybern. B. Cybern. 31(3), 450–458 (2001)

    Article  Google Scholar 

  7. Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)

    Article  Google Scholar 

  8. Kuffner, J.J., LaValle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: Proc. IEEE Int. Conf. Robot. Autom., 2, pp. 995–1001 (2000)

  9. Devaurs, D., Siméon, T., Cortés, J.: Parallelizing RRT on large-scale distributed-memory architectures. IEEE Trans. Robot. 29(2), 571–571 (2013)

    Article  Google Scholar 

  10. LaValle, S.M., Kuffner, J.J. Jr.: Rapidly-exploring random trees: progress and prospects. In: Donald, B.R., Lynch, K., Rus, D., Wellesley, M.A. (eds.) New Directions in Algorithmic and Computational Robotics, pp. 293–308. A. K. Peters, Natick (2001)

  11. Sun, Z., Hsu, D., Jiang, T., Kurniawati, H., Reif, J.H.: Narrow passage sampling for probabilistic roadmap planning. IEEE Trans. Robot. 21(6), 1105–1115 (2005)

    Article  Google Scholar 

  12. Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. In: Robot. Sci. and Syst. VI (2010)

  13. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)

    Article  MATH  Google Scholar 

  14. Marble, J.D., Bekris, K.E.: Asymptotically near-optimal planning with probabilistic roadmap spanners. IEEE Trans. Robot. 29(2), 432–444 (2013)

    Article  Google Scholar 

  15. Koenig, S., Likhachev, M.: Fast replanning for navigation in unknown terrain. IEEE Trans. Robot. 21(3), 354–363 (2005)

    Article  Google Scholar 

  16. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Sys. Sci. Cybern., SSC-4(2), pp. 100–107 (1968)

  17. Nash, A., Daniel, K., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1177–1183 (2007)

  18. Garrido, S., Moreno, L., Blanco, D.: Voronoi diagram and fast marching applied to path planning. In: Proc. Robot. Autom., ICRA IEEE Int. Conf., pp. 3049–3054 (2006)

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1C1B1006691).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ajou University, Suwon, 443-749, Korea

    In-Seok Kim, Woong-Ki Lee & Young-Dae Hong

Authors
  1. In-Seok Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Woong-Ki Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young-Dae Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young-Dae Hong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, IS., Lee, WK. & Hong, YD. Simple Global Path Planning Algorithm Using a Ray-Casting and Tracking Method. J Intell Robot Syst 90, 101–111 (2018). https://doi.org/10.1007/s10846-017-0642-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-017-0642-2

Keywords

Search

Navigation