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Robust Trajectory Planning for Robotic Communications Under Fading Channels

  • Daniel Bonilla Licea
  • Vineeth S. Varma
  • Samson Lasaulce
  • Jamal Daafouz
  • Mounir Ghogho
  • Des McLernonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10542)

Abstract

We consider a new problem of robust trajectory planning for robots that have a physical destination and a communication constraint. Specifically, the robot or automatic vehicle must move from a given starting point to a target point while uploading/downloading a given amount of data within a given time, while accounting for the energy cost and the time taken to download. However, this trajectory is assumed to be planned in advance (e.g., because online computation cannot be performed). Due to wireless channel fluctuations, it is essential for the planned trajectory to be robust to packet losses and meet the communication target with a sufficiently high probability. This optimization problem contrasts with the classical mobile communications paradigm in which communication aspects are assumed to be independent from the motion aspects. This setup is formalized here and leads us to determining non-trivial trajectories for the mobile, which are highlighted in the numerical result.

References

  1. 1.
    Licea, D.B., Varma, V.S., Lasaulce, S., Daafouz, J., Ghogho, M.: Trajectory planning for energy-efficient vehicles with communications constraints. In: Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), October 2016Google Scholar
  2. 2.
    Ooi, C.C., Schindelhauer, C.: Minimal energy path planning for wireless robots. Mob. Netw. Appl. 14(3), 309–321 (2009)CrossRefGoogle Scholar
  3. 3.
    Cai, X., Giannakis, G.B.: A two-dimensional channel simulation model for shadowing processes. IEEE Trans. Veh. Technol. 52(6), 1558–1567 (2003)CrossRefGoogle Scholar
  4. 4.
    Simon, M.K., Alouini, M.S.: Digital Communications over Fading Channels. Wiley, New york (2005)Google Scholar
  5. 5.
    Malmirchegini, M., Mostofi, Y.: On the spatial predictability of communication channels. IEEE Trans. Wirel. Commun. 11(3), 964–978 (2012)CrossRefGoogle Scholar
  6. 6.
    Kirk, D.E.: Optimal Control Theory: An Introduction. Dover Publications Inc. (2004)Google Scholar
  7. 7.
    Russell, S., Norving, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Bonilla Licea
    • 1
  • Vineeth S. Varma
    • 2
  • Samson Lasaulce
    • 3
  • Jamal Daafouz
    • 2
  • Mounir Ghogho
    • 1
    • 4
  • Des McLernon
    • 1
    Email author
  1. 1.School of Electronic and Electrical EngineeringUniversity of LeedsLeedsUK
  2. 2.CNRS and Université de Lorraine, CRAN, UMR 7039Vandœuvre-lès-NancyFrance
  3. 3.L2S (CNRS-CentraleSupelec-Univ. Paris Sud)Gif-sur-yvetteFrance
  4. 4.International University of RabatRabatMorocco

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