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Localization Issues for an Autonomous Robot Moving in a Potentially Adverse Environment

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Intelligent Autonomous Systems 14 (IAS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 531))

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Abstract

The aim of this paper is to face with the problem of localizing a robot during the navigation in a partially unknown environment. This feature becomes particularly noteworthy especially in the case of a colony of robots, possibly working with humans, inside a scenario where motion issues are crucial. Within this context the focus on self-localization through GPS and INS/SINS integration overtakes merely questions about algorithm efficiency because self-localization is a relevant part of the task. Thus, unlike other approaches, we have focalised on this behavior as an attitude an autonomous system should enhance during the task execution. The tight coupling of GPS and INS sensors is understood as a mechanism which provides the autonomous robot with a refinement of INS use by comparing and/or adjusting the INS performance by exploiting the GPS-INS integration.

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Notes

  1. 1.

    Strapdown Inertial Navigation System.

  2. 2.

    Micro- electro-mechanical systems.

  3. 3.

    Unscented Kalman filter.

  4. 4.

    Military applications, rescue, hazardous environments such as demining places, pivotal constraints of various nature, and so on.

  5. 5.

    Diluition of Precision.

  6. 6.

    5 m in our case.

  7. 7.

    Lost due to lack of GPS signal.

References

  1. Azimi-Sadjadi, B., Krishnaprasad, P.S.: Approximate nonlinear filtering and its application in navigation. Automatica 41(6), 945–956 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bin, W., Jian, W., Jianping, W., Baigen, C.: Study on adaptive gps/ins integrated navigation system. In: IEEE Intelligent Transportation Systems. vol. 2, pp. 1016–1021. Shanghai, China (2003)

    Google Scholar 

  3. Chiang, K.W., Chang, H.W., Li, C.Y., Huang, Y.W.: An artificial neural network embedded position and orientation determination algorithm for low cost mems ins/gps integrated sensors. Sensors 9, 2586–2610 (2009)

    Article  Google Scholar 

  4. Degl’Innocenti, D.: Sviluppo di una Soluzione integrata GPS/INS per Dispositivi Embedded e Android. Master’s thesis, Laurea Specialistica in Informatica, Universitá di Udine (2013)

    Google Scholar 

  5. Kevin, J., Michael, C., Nechyba, S., Antonio, A.: Embedded low cost inertial navigation system. In: Florida Conference on Recent Advances in Robotics. FAU, Dania Beach (FL) (2003)

    Google Scholar 

  6. Mao, G., Drake, S., Anderson, B.: Design of an extended kalman filter for uav localization. In: IEEE Information, Decision and Control, pp. 224–229. Adelaide, Qld (2007)

    Google Scholar 

  7. Mao, X., Masaki, W., Hideki, H.: Nonlinear gps models for position estimate using low-cost gps receiver. In: IEEE Intelligent Transportation Systems, vol. 1, pp. 637–642 (2003)

    Google Scholar 

  8. Mayhew, D.M.: Multi-rate Sensor Fusion for GPS Navigation using Kalman Filtering. Master’s thesis, Virginia Polytechnic Institute and State University, Electrical Engineering Department (1999)

    Google Scholar 

  9. Sato, G., Asai, T., Sakamoto, T., Hase, T.: Improvement of the positioning of a software-based gps receiver using a 32-bit embedded microprocessor. IEEE Trans. Consum. Electr. 46(3), 521–530 (2000)

    Article  Google Scholar 

  10. Shin, E.H.: Accuracy Improvement of Low Cost INS/GPS for land application. Master’s thesis, University of Calgary, Geomatic Engineering Department (2001)

    Google Scholar 

  11. Tan, C.W.: Design of accelerometer-based inertial navigation systems. IEEE Trans. Instrum. Meas. 54(6), 2520–2530 (2005)

    Article  Google Scholar 

  12. Wu, C.H., Chou, H.J., Su, W.H.: A genetic approach for coordinate transformation test of gps positioning. IEEE Geosci. Remote Sens. Lett. 4(2), 297–301 (2007)

    Article  Google Scholar 

  13. Xiaojuan, O., Wei, Z., Jianguo, Y.: Study on gps common-view observation data with multiscale kalman filter based on correlation structure of the discrete wavelet coefficients. In: IEEE International Frequency Control Symposium and Exposition, pp. 685–690. Vancouver, BC (2005)

    Google Scholar 

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Acknowledgements

This work was partially supported by a grant of the University of Padua’s Special Project on Mobility, Perception, and Coordination for a Team of Autonomous Robots and also by a special grant of ABTrack Limited Corporation.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Udine University, Udine, Italy

    Antonio D’Angelo & Dante Degl’Innocenti

Authors
  1. Antonio D’Angelo
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  2. Dante Degl’Innocenti
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Corresponding author

Correspondence to Antonio D’Angelo .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University , Shanghai, China

    Weidong Chen

  2. Osaka University , Osaka, Japan

    Koh Hosoda

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. Osaka University , Osaka, Japan

    Masahiro Shimizu

  5. Shanghai Jiao Tong University , Shanghai, China

    Hesheng Wang

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D’Angelo, A., Degl’Innocenti, D. (2017). Localization Issues for an Autonomous Robot Moving in a Potentially Adverse Environment. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-48036-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48035-0

  • Online ISBN: 978-3-319-48036-7

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