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An Improved Robot Path Planning Algorithm for a Novel Self-adapting Intelligent Machine Tending Robotic System

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Industrial and Robotic Systems (LASIRS 2019)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 86))

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Abstract

Autonomous industrial mobile manipulation systems (AIMMS) are widely used in manufacturing processes. AIMMS can help with part handling and delivering, part insertion and extraction, loading and unloading, and some other auxiliary tasks in a machine tending workshop environment. However, nowadays most AIMMS cannot truly realize the complete automation because the path for the mobile platform needs setting in advance in terms of different environment and inspection need to be executed to ensure safety due to lack of proper path planning algorithms. Therefore, this paper proposes an improved path-planning algorithm based on Rapidly-exploring Random Tree (RRT) and the quintic B-spline curve technique to generate a collision-free and smoother path for our designed Novel Self-adapting Intelligent Machine Tending Robotic System in the workspace. In the end, the proposed algorithm is demonstrated to generate paths for five different scenarios to test its performance and reliability.

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Authors and Affiliations

  1. University of Alberta, Edmonton, AB, T6G 2R3, Canada

    Feiyu Jia, Juan Tzintzun & Rafiq Ahmad

Authors
  1. Feiyu Jia
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  2. Juan Tzintzun
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  3. Rafiq Ahmad
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Corresponding author

Correspondence to Rafiq Ahmad .

Editor information

Editors and Affiliations

  1. ESIME Ticoman, Instituto Politecnico Nacional, Mexico City, Mexico

    Eusebio E. Hernandez

  2. School of Engineering and Science, Tecnologico de Monterrey, Altamira, Mexico

    Sajjad Keshtkar

  3. CONACYT-CENTROGEO, Mexico City, Mexico

    S. Ivvan Valdez

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Jia, F., Tzintzun, J., Ahmad, R. (2020). An Improved Robot Path Planning Algorithm for a Novel Self-adapting Intelligent Machine Tending Robotic System. In: Hernandez, E., Keshtkar, S., Valdez, S. (eds) Industrial and Robotic Systems. LASIRS 2019. Mechanisms and Machine Science, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-030-45402-9_7

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