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Stereo Vision-Based Optimal Path Planning with Stochastic Maps for Mobile Robot Navigation

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Intelligent Autonomous Systems 15 (IAS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 867))

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Abstract

This paper addresses the problem of stereo vision-based environment mapping and optimal path planning for an autonomous mobile robot by using the methodology of getting 3D point cloud from disparity images, its transformation to 2D stochastic navigation map with occupancy grid cell values assigned from the set {obstacle, unoccupied, occupied}. We re-examined and extended this methodology with a combination of A-Star with binary heap algorithms for obstacle avoidance and indoor navigation of the Innopolis autonomous mobile robot with ZED stereo camera.

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Acknowledgement

This work has been supported by the Russian Ministry of education and science with the project “Development of anthropomorphic robotic complexes with variable stiffness actuators for movement on the flat and the rugged terrains” (agreement: No14.606.21.0007, ID: RFMEFI60617X0007).

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Authors and Affiliations

  1. Institute of Robotics, Innopolis University, Universitetskaya Str. 1, 420500, Innopolis, Russia

    Taimoor Shakeel Sheikh & Ilya M. Afanasyev

Authors
  1. Taimoor Shakeel Sheikh
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  2. Ilya M. Afanasyev
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Corresponding author

Correspondence to Ilya M. Afanasyev .

Editor information

Editors and Affiliations

  1. Baden-Wuerttemberg Cooperative State University, Karlsruhe, Germany

    Marcus Strand

  2. Humanoids and Intelligence Systems Lab, KIT - Karlsruher Institut für Technologie, Karlsruhe, Germany

    Rüdiger Dillmann

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. University of Padua, Padua, Italy

    Stefano Ghidoni

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Sheikh, T.S., Afanasyev, I.M. (2019). Stereo Vision-Based Optimal Path Planning with Stochastic Maps for Mobile Robot Navigation. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_4

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