Robot Path Planning Using Cloud Computing for Large Grid Maps

  • Anis Koubaa
  • Hachemi Bennaceur
  • Imen Chaari
  • Sahar Trigui
  • Adel Ammar
  • Mohamed-Foued Sriti
  • Maram Alajlan
  • Omar Cheikhrouhou
  • Yasir Javed
Part of the Studies in Computational Intelligence book series (SCI, volume 772)


As discussed in Chap.  3, \(A^{*}\) algorithm and its variants are the main mechanisms used for grid path planning. On the other hand, with the emergence of cloud robotics, recent studies have proposed to offload heavy computation from robots to the cloud, to save robot energy and leverage abundant storage and computing resources in the cloud. In this chapter, we investigate the benefits of offloading path planning algorithms to be executed in the cloud rather than in the robot. The contribution consists in developing a vertex-centric implementation of the \(RA^{*}\), a version of \(A^{*}\) that we developed for grid maps and that was proven to be much faster than \(A^{*}\) (refer to Chap.  3), using the distributed graph processing framework Giraph that rely on Hadoop. We also developed a centralized cloud-based C++ implementation of the algorithm for benchmarking and comparison purposes.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anis Koubaa
    • 1
  • Hachemi Bennaceur
    • 2
  • Imen Chaari
    • 3
  • Sahar Trigui
    • 3
  • Adel Ammar
    • 2
  • Mohamed-Foued Sriti
    • 2
  • Maram Alajlan
    • 2
  • Omar Cheikhrouhou
    • 4
  • Yasir Javed
    • 5
  1. 1.Prince Sultan UniversityRiyadhSaudi Arabia
  2. 2.College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia
  3. 3.University Campus of ManoubaManoubaTunisia
  4. 4.College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia
  5. 5.College of Computer and Information SciencesPrince Sultan UniversityRiyadhSaudi Arabia

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