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<p>This article presents a Delta–RBR–2P hybrid robot architecture designed for complex curved-surface fragment intelligent assembly tasks. A general hierarchical kinematic modeling framework is established for this class of hybrid mechanisms, enabling decoupled motion representation and analytical formulation across three layers: the Delta stage, the 3R RBR wrist, and the parallel 2P micro-stage. A Dual-Phase Simplex–Sequential Quadratic Programming (DPSSQP) algorithm is proposed for geometric parameter optimization, integrating global exploration, constrained local refinement, intelligent result selection, and adaptive iteration control to improve optimization accuracy. Experimental validation using a laser tracker demonstrates that the DPSSQP algorithm reduces the overall mean end-effector positioning error across three terminal points (<i>G</i>, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(G_1\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(G_2\)</EquationSource> </InlineEquation>) from 14.7782 mm to 1.1195 mm (a 92.42% improvement), significantly outperforming conventional unified optimization algorithms. Furthermore, evaluations on an independent validation set confirm the strong generalization ability of the identified parameters without overfitting. These results confirm that the proposed kinematic modeling and geometric parameter calibration framework for the hybrid robot is both feasible and effective for complex curved-surface fragment intelligent assembly tasks.</p>

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Hierarchical kinematic modeling and dual-phase geometric parameter identification of a hybrid robot for complex curved-surface fragment intelligent assembly

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Binbin Ci,
  • Xiaolong Yang,
  • Chengjie Gu,
  • Changming Luo,
  • Yibin Xu,
  • Xiaoyang Mao,
  • Yulin Wang

摘要

This article presents a Delta–RBR–2P hybrid robot architecture designed for complex curved-surface fragment intelligent assembly tasks. A general hierarchical kinematic modeling framework is established for this class of hybrid mechanisms, enabling decoupled motion representation and analytical formulation across three layers: the Delta stage, the 3R RBR wrist, and the parallel 2P micro-stage. A Dual-Phase Simplex–Sequential Quadratic Programming (DPSSQP) algorithm is proposed for geometric parameter optimization, integrating global exploration, constrained local refinement, intelligent result selection, and adaptive iteration control to improve optimization accuracy. Experimental validation using a laser tracker demonstrates that the DPSSQP algorithm reduces the overall mean end-effector positioning error across three terminal points (G, \(G_1\) and \(G_2\) ) from 14.7782 mm to 1.1195 mm (a 92.42% improvement), significantly outperforming conventional unified optimization algorithms. Furthermore, evaluations on an independent validation set confirm the strong generalization ability of the identified parameters without overfitting. These results confirm that the proposed kinematic modeling and geometric parameter calibration framework for the hybrid robot is both feasible and effective for complex curved-surface fragment intelligent assembly tasks.