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<p>The collaborative manipulation of large-scale components, represented by high-precision tasks such as aerospace drilling, requires optimized kinematic properties to ensure reachable workspace and motion dexterity. This paper presents a novel hybrid collaborative robot system (HCRS) comprising two 5-degree-of-freedom hybrid robots that share the same XY-RPU&amp;2RPS topological structure but have different dimensional parameters. To establish a theoretical foundation for the system’s design, an inverse kinematic model is developed, and the pose correlation between the robots is defined using homogeneous coordinate transformations. To evaluate the collaborative performance, three indices are proposed: collaborative workspace, collaborative transmission index, and collaborative dexterity index. To overcome the computational cost of evaluating the high-dimensional, nonlinear optimization problem, an integrated MLP-NSGA-III framework is constructed. A multilayer perceptron (MLP) neural network is employed as a surrogate model to efficiently approximate the complex relationships between dimensional parameters and performance indices, significantly accelerating the multi-objective optimization process. This yields a well-distributed Pareto optimal set. Furthermore, a hybrid multi-criteria decision-making method, combining subjective and objective preferences via game theory, is employed to select the optimal configurations for various manufacturing scenarios. The results demonstrate that the proposed methodology effectively enhances system performance, with the balanced scenario increasing the workspace volume by 138.9%. This study provides a systematic dimensional design framework that serves as a fundamental prerequisite for subsequent physical prototype development and process control in advanced manufacturing.</p>

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Multi-objective dimensional design and performance evaluation of a hybrid collaborative robot system for large-scale component manufacturing

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Tengfei Tang,
  • Zaiqing Wu,
  • Zhen Wu,
  • Shenglong Xie

摘要

The collaborative manipulation of large-scale components, represented by high-precision tasks such as aerospace drilling, requires optimized kinematic properties to ensure reachable workspace and motion dexterity. This paper presents a novel hybrid collaborative robot system (HCRS) comprising two 5-degree-of-freedom hybrid robots that share the same XY-RPU&2RPS topological structure but have different dimensional parameters. To establish a theoretical foundation for the system’s design, an inverse kinematic model is developed, and the pose correlation between the robots is defined using homogeneous coordinate transformations. To evaluate the collaborative performance, three indices are proposed: collaborative workspace, collaborative transmission index, and collaborative dexterity index. To overcome the computational cost of evaluating the high-dimensional, nonlinear optimization problem, an integrated MLP-NSGA-III framework is constructed. A multilayer perceptron (MLP) neural network is employed as a surrogate model to efficiently approximate the complex relationships between dimensional parameters and performance indices, significantly accelerating the multi-objective optimization process. This yields a well-distributed Pareto optimal set. Furthermore, a hybrid multi-criteria decision-making method, combining subjective and objective preferences via game theory, is employed to select the optimal configurations for various manufacturing scenarios. The results demonstrate that the proposed methodology effectively enhances system performance, with the balanced scenario increasing the workspace volume by 138.9%. This study provides a systematic dimensional design framework that serves as a fundamental prerequisite for subsequent physical prototype development and process control in advanced manufacturing.