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<p>To fulfill the high-precision assembly requirements of large structural components, this study integrates Finite and Instantaneous Screw theory, Response Surface Methodology (RSM), and the minimum distance method to develop a systematic design and performance enhancement strategy for the 2PRS&amp;2PSS parallel assembly robot. The kinematic model and velocity Jacobian matrix of the mechanism are established to quantitatively analyze multiple performance indices, including dexterity, anti-deformation capability, virtual work transmissibility, and workspace. Innovatively, a response surface model (RSM) is introduced to construct surrogate models between the performance parameters and scale parameters, significantly improving optimization efficiency. The collaborative optimization of parameters in a four-dimensional performance space is achieved by combining the Pareto front with the minimum distance method. After optimization, the mechanism demonstrates significant improvements in dexterity, anti-deformation capability, virtual work transmissibility, and workspace, thereby validating the effectiveness of the proposed method and providing a theoretical foundation for its future engineering application. This study provides a systematic theoretical framework for the optimal design of high-payload, high-precision parallel assembly robots.</p>

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Multi-objective collaborative optimization design of a 2PRS&2PSS parallel robot for high-performance assembly

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Yang Qi,
  • Xinyu Jia,
  • Ruoyi Zhang,
  • Shijie Guo

摘要

To fulfill the high-precision assembly requirements of large structural components, this study integrates Finite and Instantaneous Screw theory, Response Surface Methodology (RSM), and the minimum distance method to develop a systematic design and performance enhancement strategy for the 2PRS&2PSS parallel assembly robot. The kinematic model and velocity Jacobian matrix of the mechanism are established to quantitatively analyze multiple performance indices, including dexterity, anti-deformation capability, virtual work transmissibility, and workspace. Innovatively, a response surface model (RSM) is introduced to construct surrogate models between the performance parameters and scale parameters, significantly improving optimization efficiency. The collaborative optimization of parameters in a four-dimensional performance space is achieved by combining the Pareto front with the minimum distance method. After optimization, the mechanism demonstrates significant improvements in dexterity, anti-deformation capability, virtual work transmissibility, and workspace, thereby validating the effectiveness of the proposed method and providing a theoretical foundation for its future engineering application. This study provides a systematic theoretical framework for the optimal design of high-payload, high-precision parallel assembly robots.