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This paper presents a motion planning strategy for powered-caster omnidirectional robots within the ROS framework that is driven by Gaussian Processes and integrates cubic spline interpolation to enhance both navigation precision and motion smoothness. By dynamically adjusting the Gaussian kernel based on trajectory costs and collision risks, the algorithm achieves superior positional tracking accuracy and computational efficiency, operating at up to 1 kHz. Experimental validation on the robot demonstrates improved performance over conventional techniques, especially in accuracy and computational speed.

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Motion Planing of Powered-Caster Vehicle Based on Gaussian Process

  • 会议论文

  • 发表日期：

    2025年

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  • Xiao Wang,
  • Fei Zhao,
  • Heyuan Li,
  • Baolin Liu,
  • Chenwei Gong,
  • Xuesong Mei

摘要

This paper presents a motion planning strategy for powered-caster omnidirectional robots within the ROS framework that is driven by Gaussian Processes and integrates cubic spline interpolation to enhance both navigation precision and motion smoothness. By dynamically adjusting the Gaussian kernel based on trajectory costs and collision risks, the algorithm achieves superior positional tracking accuracy and computational efficiency, operating at up to 1 kHz. Experimental validation on the robot demonstrates improved performance over conventional techniques, especially in accuracy and computational speed.