Skip to main content
SpringerLink
Log in
Book cover

Intelligent Autonomous Systems 13 pp 57–69Cite as

Self-learning RRT* Algorithm for Mobile Robot Motion Planning in Complex Environments

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

RRT* is a practical and efficient incremental sampling-based motion planning algorithm. However, its searching ability is quite inefficient in some cases, due to relying on uniform random sampling like other RRT-based algorithms without taking the environment information and prior knowledge into account, which particularly leads to many sampling failures or generation of useless nodes in complex environments. In this paper, we propose an extension of RRT* based on a self-learning strategy and a hybrid-biased sampling scheme to improve the planning efficiency. By taking advantage of the prior knowledge accumulation and cost estimation, the searching tree has higher probability and success rate to extend in difficult areas. We also demonstrate the performance of our algorithm by building some simulation environments for our mobile robot and conclude with the results compared with RRT*.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Choset, H.M.: Principles of robot motion: theory, algorithms, and implementation. MIT press (2005)

    Google Scholar 

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

    Google Scholar 

  3. LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. The Int’l J. of Robotics Research 20(5) (2001) 378–400

    Google Scholar 

  4. Jaillet, L., Yershova, A., La Valle, S.M., Siméon, T.: Adaptive tuning of the sampling domain for dynamic-domain rrts. In: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on, IEEE (2005) 2851–2856

    Google Scholar 

  5. Abbasi-Yadkori, Y., Modayil, J., Szepesvari, C.: Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning. In: Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS). (2010) 127–132

    Google Scholar 

  6. Alterovitz, R., Patil, S., Derbakova, A.: Rapidly-exploring roadmaps: Weighing exploration vs. refinement in optimal motion planning. In: Proc. IEEE Int’l Conf. on Robotics and Automation (ICRA). (2011) 3706–3712

    Google Scholar 

  7. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int’l J. of Robotics Research 30(7) (2011) 846–894

    Google Scholar 

  8. Zhang, L., Manocha, D.: An efficient retraction-based rrt planner. In: Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, IEEE (2008) 3743–3750

    Google Scholar 

  9. Lütteke, F., Zhang, X., Franke, J.: Implementation of the hungarian method for object tracking on a camera monitored transportation system. In: Robotics; Proceedings of ROBOTIK 2012; 7th German Conference on, VDE (2012) 1–6

    Google Scholar 

  10. Perez, A., Karaman, S., Shkolnik, A., Frazzoli, E., Teller, S., Walter, M.R.: Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms. In: Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS). (2011) 4307–4313

    Google Scholar 

  11. Karaman, S., Walter, M.R., Perez, A., Frazzoli, E., Teller, S.: Anytime motion planning using the RRT*. In: Proc. IEEE Int’l Conf. on Robotics and Automation (ICRA). (2011) 1478–1483

    Google Scholar 

  12. Kiesel, S., Burns, E., Ruml, W.: Abstraction-guided sampling for motion planning. In: SOCS. (2012)

    Google Scholar 

  13. Urmson, C., Simmons, R.G.: Approaches for heuristically biasing rrt growth. In: IROS. (2003) 1178–1183

    Google Scholar 

  14. Rodriguez, S., Tang, X., Lien, J.M., Amato, N.M.: An obstacle-based rapidly-exploring random tree. In: Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, IEEE (2006) 895–900

    Google Scholar 

  15. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. Systems Science and Cybernetics, IEEE Transactions on 4(2) (1968) 100–107

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Factory Automation and Production Systems (FAPS), University of Erlangen-Nuremberg, Erlangen, 91058, Germany

    Xu Zhang, Felix Lütteke, Christian Ziegler & Jörg Franke

Authors
  1. Xu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix Lütteke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Ziegler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jörg Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu Zhang .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, X., Lütteke, F., Ziegler, C., Franke, J. (2016). Self-learning RRT* Algorithm for Mobile Robot Motion Planning in Complex Environments. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08338-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation