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A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning.

Di Wu1,2, Zhi Yu1,2, Alimasi Adili1,2

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces a novel deep neural network (DLNet) combined with the Gilbert-Johnson-Keerthi (GJK) algorithm for efficient and accurate self-collision detection in dual-manipulator systems. The DLGJK algorithm significantly reduces detection time, enhancing operational safety and performance.

Keywords:
GJK algorithmartificial intelligencedeep neural networkdual-manipulator systemself-collision detection

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Safe operation of multi-manipulator systems requires robust self-collision detection, especially in dynamic environments.
  • Current methods struggle to balance detection efficiency and accuracy simultaneously.
  • Integrating artificial intelligence offers a promising avenue for improving detection capabilities.

Purpose of the Study:

  • To develop a novel two-level self-collision detection algorithm (DLGJK) for dual-manipulator systems.
  • To enhance detection efficiency and accuracy using artificial intelligence and the GJK algorithm.
  • To address real-time self-collision detection challenges in fast-continuous and high-precision robotic applications.

Main Methods:

  • Generated a dataset and trained a deep neural network (DLNet) based on the Gilbert-Johnson-Keerthi (GJK) algorithm.
  • Proposed the DLGJK algorithm, a two-level approach combining DLNet for initial risk assessment and GJK for precise detection.
  • Implemented DLNet to filter non-risky states, reducing unnecessary computations.

Main Results:

  • The DLGJK algorithm achieved a 97.7% reduction in the expected time for single self-collision detection within the workspace.
  • DLNet effectively identified states with no self-collision risk, significantly improving overall detection efficiency.
  • The GJK algorithm provided accurate fine detection for states identified as potentially risky by DLNet.

Conclusions:

  • The DLGJK algorithm offers a significant improvement in self-collision detection efficiency and accuracy for dual-manipulator systems.
  • This AI-driven approach reduces system overhead and enhances performance in path planning.
  • The algorithm demonstrates scalability for broader multi-manipulator system applications.