An extended moment-based trajectory accuracy reliability analysis method of robot manipulators with random and interval uncertainties
提出一种结合随机和区间变量的轨迹精度可靠性分析方法，通过区间分析、特征分解和自适应稀疏网格求积，高效评估混合不确定性对机器人操作器运动性能的影响，并用六自由度机器人实例验证了其准确性和效率。
This paper proposes a novel trajectory accuracy reliability analysis method with random and interval variables to evaluate the impact of mixed uncertainties on the motion performance of robot manipulators. To effectively and accurately solve the hybrid reliability model, the interval analysis is first conducted on the positional error model established by differential kinematics, and a maximum positional error searching algorithm is developed based on geometric transformation and cell enumeration. Then, the trajectory accuracy reliability model is reconstructed by the eigen-decomposition technique, which further incorporates the adaptive weight vector-based anisotropic sparse-grid quadrature approach to derive the statistical moments of maximum positional error. Afterward, by matching with the cumulants of the original reliability model, a novel approximation method is proposed based on the Weibull distribution and minimized matching error model to complete the trajectory accuracy reliability analysis. The practicality and advantages of the proposed method are demonstrated by two illustrative examples of 6-degrees-of-freedom robot manipulators. Comparative results affirm that the proposed method outperforms existing state-of-the-art algorithms in terms of accuracy and efficiency for trajectory accuracy reliability analysis encompassing random and interval uncertainties. Overall, the outcomes of this paper contribute significantly to the design and analysis of safety and reliability in moving machinery.