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

  • Xu Zhang
  • Felix Lütteke
  • Christian Ziegler
  • Jörg Franke
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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*.


Motion planning RRT* Biased sampling Mobile robot 


  1. 1.
    Choset, H.M.: Principles of robot motion: theory, algorithms, and implementation. MIT press (2005)Google Scholar
  2. 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) 566–580Google Scholar
  3. 3.
    LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. The Int’l J. of Robotics Research 20(5) (2001) 378–400Google Scholar
  4. 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) 2851–2856Google Scholar
  5. 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) 127–132Google Scholar
  6. 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) 3706–3712Google Scholar
  7. 7.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int’l J. of Robotics Research 30(7) (2011) 846–894Google Scholar
  8. 8.
    Zhang, L., Manocha, D.: An efficient retraction-based rrt planner. In: Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, IEEE (2008) 3743–3750Google Scholar
  9. 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) 1–6Google Scholar
  10. 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) 4307–4313Google Scholar
  11. 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) 1478–1483Google Scholar
  12. 12.
    Kiesel, S., Burns, E., Ruml, W.: Abstraction-guided sampling for motion planning. In: SOCS. (2012)Google Scholar
  13. 13.
    Urmson, C., Simmons, R.G.: Approaches for heuristically biasing rrt growth. In: IROS. (2003) 1178–1183Google Scholar
  14. 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) 895–900Google Scholar
  15. 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) 100–107Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xu Zhang
    • 1
  • Felix Lütteke
    • 1
  • Christian Ziegler
    • 1
  • Jörg Franke
    • 1
  1. 1.Institute for Factory Automation and Production Systems (FAPS)University of Erlangen-NurembergErlangenGermany

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