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Semantic Trajectory Planning for Industrial Robotics

Author

Listed:
  • Zhou Li

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Gengming Xie

    (State Grid Hunan Electric Power Company Limited, China)

  • Varsha Arya

    (Department of Business Administration, Asia University, Taiwan, & Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon, & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India, & Chandigarh University, Chandigarh, India)

  • Kwok Tai Chui

    (Hong Kong Metropolitan University, Hong Kong, China)

Abstract

The implementation of industrial robots across various sectors has ushered in unparalleled advancements in efficiency, productivity, and safety. This paper explores the domain of semantic trajectory planning in the area of industrial robotics. By adeptly merging physical constraints and semantic knowledge of environments, the proposed methodology enables robots to navigate complex surroundings with utmost precision and efficiency. In a landscape marked by dynamic challenges, the research positions semantic trajectory planning as a linchpin in fostering adaptability. It ensures robots interact safely with their surroundings, providing vital object detection and recognition capabilities. The proposed ResNet model exhibits remarkable classification performance, bolstering overall productivity. The study underscores the significance of this approach in addressing real-world industrial applications while emphasizing accuracy, precision, and enhanced productivity.

Suggested Citation

  • Zhou Li & Gengming Xie & Varsha Arya & Kwok Tai Chui, 2024. "Semantic Trajectory Planning for Industrial Robotics," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-10, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-10
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.334556
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    References listed on IDEAS

    as
    1. Khan Md. Hasib & Nurul Akter Towhid & Md Rafiqul Islam, 2021. "HSDLM: A Hybrid Sampling With Deep Learning Method for Imbalanced Data Classification," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 11(4), pages 1-13, October.
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