Advertisement

Neuro-Fuzzy Sampling: Safe and Fast Multi-query Randomized Path Planning for Mobile Robots

  • Weria KhaksarEmail author
  • Md Zia Uddin
  • Jim Torresen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1015)

Abstract

Despite the proven advantages of probabilistic motion planning algorithms in solving complex navigation problems, their performance is restricted by the selected nodes in the configuration space. Furthermore, the choice of selecting the neighbor nodes and expanding the graph structure is limited by a set of deterministic measures such as Euclidean distance. In this paper, an improved version of multi-query planners is proposed which utilizes a neuro-fuzzy structure in the sampling stage to achieve a higher level of effectiveness including safety and applicability. This planner employs a set of expert rules concerning the distance to the surrounding obstacles and constructs a fuzzy controller. Then, parameters of the resulting fuzzy system are optimized based on a hybrid learning technique. The outcome of the neuro-fuzzy system is implemented on a multi-query planner to improve the quality of the selected nodes. The planner is further improved by adding a post-processing step which shortens the path by removing the redundant segments and by smoothing the resulting path through the inscribed circle of any two consecutive segments. The planner was tested through simulation in different planning problems and was compared to a set of benchmark algorithms. Furthermore, the proposed planner was implemented on a robotic system. Simulation and experimental results indicate the superior performance of the planner in terms of safety and applicability.

Keywords

Path planning Mobile robot Sampling-based Multi-query Safety Neuro-fuzzy system 

Notes

Acknowledgment

This work is supported by the Research Council of Norway as a part of Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Kala, R.: Increased visibility sampling for probabilistic roadmaps. In: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pp. 87–92 (2018)Google Scholar
  3. 3.
    LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)CrossRefGoogle Scholar
  4. 4.
    Neto, A.A., Macharet, D.G., Campos, M.F.M.: Multi-agent rapidly-exploring pseudo-random tree. J. Intell. Robot. Syst. 89(1–2), 69–85 (2018)CrossRefGoogle Scholar
  5. 5.
    Hsu, D., Latombe, J., Motwani, R.: Path planning in expansive configuration spaces. In: Proceedings of International Conference on Robotics and Automation, vol. 3, pp. 2719–2726 (1997)Google Scholar
  6. 6.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefGoogle Scholar
  7. 7.
    Schmerling, E., Janson, L., Pavone, M.: Optimal sampling-based motion planning under differential constraints: the driftless case. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2368–2375 (2015)Google Scholar
  8. 8.
    Bemelmans, R., Gelderblom, G.J., Jonker, P., de Witte, L.: Socially assistive robots in elderly care: a systematic review into effects and effectiveness. J. Am. Med. Dir. Assoc. 13(2), 114–120.e1 (2012)CrossRefGoogle Scholar
  9. 9.
    Yazdani, F., Brieber, B., Beetz, M.: Cognition-enabled robot control for mixed human-robot rescue teams. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds.) Intelligent Autonomous Systems 13, vol. 302, pp. 1357–1369. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-08338-4_98CrossRefGoogle Scholar
  10. 10.
    Perdoch, M., Bradley, D.M., Chang, J.K.: Herman, H., Rander, P., Stentz, A.: Leader tracking for a walking logistics robot. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2994–3001 (2015)Google Scholar
  11. 11.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)CrossRefGoogle Scholar
  12. 12.
    Tsianos, K.I., Sucan, I.A., Kavraki, L.E.: Sampling-based robot motion planning: towards realistic applications. Comput. Sci. Rev. 1(1), 2–11 (2007)CrossRefGoogle Scholar
  13. 13.
    Bohlin, R., Kavraki, L.E.: Path planning using lazy PRM. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), vol. 1, pp. 521–528 (2000)Google Scholar
  14. 14.
    Khaksar, W., Hong, T.S., Khaksar, M., Motlagh, O.: A low dispersion probabilistic roadmaps (LD-PRM) algorithm for fast and efficient sampling-based motion planning. Int. J. Adv. Robot. Syst. 10(11), 397 (2013)CrossRefGoogle Scholar
  15. 15.
    Hsu, D., Jiang, T., Reif, J., Sun, Z.: The bridge test for sampling narrow passages with probabilistic roadmap planners. In: 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), vol. 3, pp. 4420–4426 (2003)Google Scholar
  16. 16.
    Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol. 2, pp. 1018–1023 (1999)Google Scholar
  17. 17.
    Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  18. 18.
    Chen, Y., Chang, C.: An intelligent ANFIS controller design for a mobile robot. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 445–448 (2018)Google Scholar
  19. 19.
    Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)Google Scholar
  20. 20.
    Karaboga, D., Kaya, E.: Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif. Intell. Rev. 1–31 (2018)Google Scholar
  21. 21.
    Fleury, S., Soueres, P., Laumond, J.P., Chatila, R.: Primitives for smoothing mobile robot trajectories. IEEE Trans. Robot. Autom. 11(3), 441–448 (1995)CrossRefGoogle Scholar
  22. 22.
    Ravankar, A., Ravankar, A.A., Kobayashi, Y., Emaru, T.: Path smoothing extension for various robot path planners. In: 2016 16th International Conference on Control, Automation and Systems (ICCAS), pp. 263–268 (2016)Google Scholar
  23. 23.
    Su, K.-H., Phan, T.-P.: Robot path planning and smoothing based on fuzzy inference. In: 2014 IEEE International Conference on System Science and Engineering (ICSSE), pp. 64–68 (2014)Google Scholar
  24. 24.
    Yang, K., Sukkarieh, S.: An analytical continuous-curvature path-smoothing algorithm. IEEE Trans. Robot. 26(3), 561–568 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Robotics and Intelligent Systems Group, Department of InformaticsUniversity of OsloOsloNorway

Personalised recommendations