Advertisement

Cooperative FSEIF SLAM of Omnidirectional Mobile Multirobots

  • Ching-Chih TsaiEmail author
  • Ying-Che Lai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

Abstract

The paper presents a cooperative fuzzy sparse extended information filtering (FSEIF) method for simultaneous localization and mapping SLAM) of multiple three-wheeled omnidirectional mobile multirobots in a given indoor environment. After brief description of our previous fuzzy SEIF SLAM (FSEIF SLAM) algorithm for a single mobile robot, a cooperative Fuzzy SEIF SLAM approach is presented for a group of omnidirectional mobile multirobots, where the optimal path searching method is devised by incorporating with K-means and Dijkstra algorithm under the assumption of known map and correspondence conditions. The effectiveness and merits of the proposed cooperative FSEIF SLAM in a large-scale environment are well illustrated by carrying out comparative simulations for multiple mobile robots.

Keywords

Cooperative Fuzzy logics Sparse extended information filtering (SEIF) Omnidirectional mobile robot Simultaneous localization and mapping (SLAM) Swedish wheel 

Notes

Acknowledgment

The authors gratefully acknowledge financial support from the Ministry of Science and Technology (MOST), Taiwan, ROC, under contract MOST 107-2221-E-005 -073-MY2.

References

  1. 1.
    Dissanayake, G., Newman, P., Clark, S., et al.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transact. Robot. Autom. 17(3), 229–241 (2001)CrossRefGoogle Scholar
  2. 2.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2006)zbMATHGoogle Scholar
  3. 3.
    Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Z., Durrant-Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. Int. J. Robot. Res. 23(7), 693–716 (2004)CrossRefGoogle Scholar
  4. 4.
    Gauo, J.H., Zhao, C.X.: An improved algorithm with sparse extended information filters. Pattern Recogn. Artif. Intell. 22(2), 269 (2009)Google Scholar
  5. 5.
    Eustice, R., Walter, M., Leonard, J.: Sparse extended information filters insights into sparsification. In Proceedings of the IEEE/RSJ Interactional Conference on Intelligent Robots and Systems, Edmonton, Canada, pp. 3281–3288 (2005)Google Scholar
  6. 6.
    Lin, H.H., Tsai, C.C.: Ultrasonic localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filtering. IEEE Transact. Instrum. Meas. 57(9), 2024–2034 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Tai, F.C., Tsai, C.C.: Decentralized EIF-based global localization using dead-reckoning, KINECT and laser scanning for autonomous omnidirectional mobile robot. In: Proceedings of 2014 International Conference on Advanced robotics and intelligent systems, ARIS 2014, Taipei, Taiwan, pp. 85–90 (2014)Google Scholar
  8. 8.
    Begum, M., Mann, G.K.I., Gosine, R.G.: Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots. Appl. Soft Comput. 8(1), 15–65 (2008)CrossRefGoogle Scholar
  9. 9.
    Lai, Y.-C., Tsai, C.C.: Fuzzy sparse EIF simultaneous localization and mapping of omnidirectional mobile robots. In: Proceedings of 2016 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2016. Taipei Nangang Exhibition Center, Taipei, Taiwan (2016)Google Scholar
  10. 10.
    Tsai, C.C., Lai, Y.C.: Fuzzy SEIF SLAM for omnidirectional mobile robots. In: Proceedings of 2018 International Conference on Fuzzy Theory with Its Applications, iFuzzy 2018. EXCO, Daegu, Republic of Korea (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Chung Hsing UniversityTaichungTaiwan

Personalised recommendations