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

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Robot Intelligence Technology and Applications (RiTA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1015))

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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.

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References

  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)

    Article  Google Scholar 

  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. LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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_98

    Chapter  Google Scholar 

  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. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  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. Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)

    Google Scholar 

  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. Fleury, S., Soueres, P., Laumond, J.P., Chatila, R.: Primitives for smoothing mobile robot trajectories. IEEE Trans. Robot. Autom. 11(3), 441–448 (1995)

    Article  Google Scholar 

  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. 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. Yang, K., Sukkarieh, S.: An analytical continuous-curvature path-smoothing algorithm. IEEE Trans. Robot. 26(3), 561–568 (2010)

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. Robotics and Intelligent Systems Group, Department of Informatics, University of Oslo, Blindern, 0316, Oslo, Norway

    Weria Khaksar, Md Zia Uddin & Jim Torresen

Authors
  1. Weria Khaksar
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  2. Md Zia Uddin
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  3. Jim Torresen
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Corresponding author

Correspondence to Weria Khaksar .

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Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    Hyung Myung

  3. Keimyung University, Daegu, Korea (Republic of)

    Seung-Mok Lee

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Khaksar, W., Uddin, M.Z., Torresen, J. (2019). Neuro-Fuzzy Sampling: Safe and Fast Multi-query Randomized Path Planning for Mobile Robots. In: Kim, JH., Myung, H., Lee, SM. (eds) Robot Intelligence Technology and Applications. RiTA 2018. Communications in Computer and Information Science, vol 1015. Springer, Singapore. https://doi.org/10.1007/978-981-13-7780-8_2

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  • DOI: https://doi.org/10.1007/978-981-13-7780-8_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7779-2

  • Online ISBN: 978-981-13-7780-8

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