Industrial robot, as one class of digitalized intelligent equipment, plays a significant role in enhancing production efficiency and quality through implementing desired kinematic precision and reliable performance for modern high-tech industries. This study proposes a robust optimization framework to account for the kinematic and dynamic uncertainties in industrial robotic systems. The design objective is established with the kinematic extremes and means of motion by incorporating the statistical moments of positioning accuracy and torque error in the joints. The nondeterministic kinematic and dynamic optimization is carried out by integrating the moment-based method and computational optimization techniques. Specifically, the mixed-degree cubature formula is adopted to evaluate the objective function under given design parameters. The optimization algorithm is utilized to achieve a best possible design. The moment sensitivity of motion error with respect to the uncertain parameters is derived by numerical integration. In this study, three practical design examples are provided to demonstrate the effectiveness of the proposed kinematic and dynamic robust optimization methods. The computational and experimental results indicate that the proposed robust optimization framework enables to reduce motion error and lower its sensitivity to uncertainties, thereby achieving reliable improvements in both motion accuracy and system robustness.

中文翻译：

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用于设计具有运动学和动态标准的机器人系统的稳健优化框架

工业机器人作为一类数字化智能装备，通过实现现代高科技产业所需的运动精度和可靠性能，对提高生产效率和质量发挥着重要作用。这项研究提出了一个强大的优化框架来解决工业机器人系统中的运动学和动态不确定性。通过结合关节中定位精度和扭矩误差的统计力矩，根据运动学极限和运动方式建立设计目标。通过集成基于矩的方法和计算优化技术来进行非确定性运动学和动态优化。具体来说，采用混合度立方公式来评估给定设计参数下的目标函数。利用优化算法来实现最佳的设计。通过数值积分得出运动误差对不确定参数的力矩敏感性。在本研究中，提供了三个实际设计实例来证明所提出的运动学和动态鲁棒优化方法的有效性。计算和实验结果表明，所提出的鲁棒优化框架能够减少运动误差并降低其对不确定性的敏感性，从而实现运动精度和系统鲁棒性的可靠改进。