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<p>Improving the energy efficiency of industrial robots has become increasingly essential as modern manufacturing moves toward sustainable and low-carbon production. Although S-curve trajectory planning is widely adopted to reduce mechanical stress, its high-dimensional motion parameters often lead to suboptimal control settings when tuned manually. This study proposes a systematic multi-objective optimization framework that integrates jerk-limited S-curve motion modeling with a Taguchi–PCA synthesis method to simultaneously improve energy consumption and execution time in six-axis industrial robotic operations. Using an L18(2<sup>1</sup> × 3<sup>7</sup>) orthogonal array, eight S-curve and motion-planning factors were evaluated. Principal Component Analysis was applied to compress multiple responses into a unified performance index, enabling simultaneous optimization while preserving the statistical significance of each quality characteristic. Results revealed that motion command type and running speed dominate overall performance, contributing 56.64 and 38.44%, respectively. Compared with baseline parameters, the optimal configuration reduced power consumption by 26.39% and execution time by 26.20%, demonstrating that substantial performance gains can be achieved solely through motion-parameter tuning without hardware modification. The proposed framework provides a generalizable, data-driven, and easily deployable optimization method, offering practical insights for energy-efficient robot programming in industrial applications.</p>

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Energy and Efficiency Optimization of Six-Axis Robotic Arms Using Taguchi Coupled Principal Component Analysis

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Chin-Chia Liu,
  • Po-Wei Wang,
  • Chi-Hua Hsu

摘要

Improving the energy efficiency of industrial robots has become increasingly essential as modern manufacturing moves toward sustainable and low-carbon production. Although S-curve trajectory planning is widely adopted to reduce mechanical stress, its high-dimensional motion parameters often lead to suboptimal control settings when tuned manually. This study proposes a systematic multi-objective optimization framework that integrates jerk-limited S-curve motion modeling with a Taguchi–PCA synthesis method to simultaneously improve energy consumption and execution time in six-axis industrial robotic operations. Using an L18(21 × 37) orthogonal array, eight S-curve and motion-planning factors were evaluated. Principal Component Analysis was applied to compress multiple responses into a unified performance index, enabling simultaneous optimization while preserving the statistical significance of each quality characteristic. Results revealed that motion command type and running speed dominate overall performance, contributing 56.64 and 38.44%, respectively. Compared with baseline parameters, the optimal configuration reduced power consumption by 26.39% and execution time by 26.20%, demonstrating that substantial performance gains can be achieved solely through motion-parameter tuning without hardware modification. The proposed framework provides a generalizable, data-driven, and easily deployable optimization method, offering practical insights for energy-efficient robot programming in industrial applications.