对于机器人特别是并联机器人替代传统数控机床进行加工是当前的主流趋势,从而对机器人的定位精度提出了更高的要求,误差补偿及运动学标定可以有效提高并联机器人末端定位精度。以新型2-R(Ps)&P(Ps)三平动自由度并联机器人为研究对象...对于机器人特别是并联机器人替代传统数控机床进行加工是当前的主流趋势,从而对机器人的定位精度提出了更高的要求,误差补偿及运动学标定可以有效提高并联机器人末端定位精度。以新型2-R(Ps)&P(Ps)三平动自由度并联机器人为研究对象,提出了基于单目视觉的运动学标定方法,从而提高此类机器人末端定位精度。基于误差闭环矢量链法构建该机构的几何误差模型,得到影响动平台末端位姿的34项几何误差源,采用Sobol算法对其进行误差灵敏度分析,找出对末端误差影响较大的误差源。采用单目相机视觉标定的方法来获取末端位姿,该方法采用Eye in Hand的标定形式,通过视觉图像算法来获取标定板中靶点位置信息进行误差测量,再构建误差辨识方程,利用最小二乘法进行辨识,最后通过修正控制系统输入的方式完成误差补偿流程,进行运动学标定试验。通过该试验,标定前后误差值Δr′均值平均下降77.16%,最大值平均下降69.46%。标定试验结果表明,所提出的运动学标定方法具有一定的有效性,该标定方法适用于同类并联机器人误差标定。展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
文摘对于机器人特别是并联机器人替代传统数控机床进行加工是当前的主流趋势,从而对机器人的定位精度提出了更高的要求,误差补偿及运动学标定可以有效提高并联机器人末端定位精度。以新型2-R(Ps)&P(Ps)三平动自由度并联机器人为研究对象,提出了基于单目视觉的运动学标定方法,从而提高此类机器人末端定位精度。基于误差闭环矢量链法构建该机构的几何误差模型,得到影响动平台末端位姿的34项几何误差源,采用Sobol算法对其进行误差灵敏度分析,找出对末端误差影响较大的误差源。采用单目相机视觉标定的方法来获取末端位姿,该方法采用Eye in Hand的标定形式,通过视觉图像算法来获取标定板中靶点位置信息进行误差测量,再构建误差辨识方程,利用最小二乘法进行辨识,最后通过修正控制系统输入的方式完成误差补偿流程,进行运动学标定试验。通过该试验,标定前后误差值Δr′均值平均下降77.16%,最大值平均下降69.46%。标定试验结果表明,所提出的运动学标定方法具有一定的有效性,该标定方法适用于同类并联机器人误差标定。
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
