ZigBee与OPC技术在工业管控系统中的应用

本文介绍了ZigBee的技术特点,及ZigBee与OPC技术结合在工业管控系统中的应用方法,为工业管控系统提供了一种开放式、自治愈的无线解决方案。     

C-V模型与工业CT相结合的几何测量方法

针对工业生产中复杂封闭内腔几何尺寸很难测量的问题,提出一种基于C-V模型的工业CT几何测量方法.首先利用C-V模型提取目标边缘坐标点的有序链码,然后利用Green定理和欧氏距离公式计算目标区域的面积和周长.在提取目标边缘坐标点的步骤中,把边缘灰度先验知识融入C-V模型以提高测量精度;在计算目标区域的面积和周长的过程中,合理地选择目标边缘有序坐标点和插值有序坐标点,再一次提高几何测量精度.实验结果表明,与2DFacet模型测量方法相比,文中方法测量精度约提高1个数量级;并已将该方法应用于实际中.     

对日IT软件产业结构优化的研究

软件服务外包作为一个新型的软件产业方兴未艾,这种贸易方式加强了国际间的合作与区域性产业的互补。作为中国的近邻日本,这种互补方式的优势显得尤为突出。但是,由于中国外包企业的竞争力较弱、技术力量不够雄厚、企业规模尚未形成等弱点存在,大大制约着中国软件服务外包业的进一步发展。特别是随着诸多新兴接包国家和地区的崛起,使得中国所面临的竞争进一步加大,从前的低成本优势将得到削弱。这些都严重地制约着中国软件服务外包行业的健康发展。有序地调整以低端为主的产业结构,提高自主创新能力,加大服务外包在国家IT（Information Technology）产业中的比重,从而增强核心竞争力。     

基于三维实体模型的钢结构工业厂房CAD/CAM软件后处理系统

为提高钢结构工业厂房设计的准确性、精确度和工作效率,为某钢结构工业厂房CAD/CAM软件研制出基于三维实体模型的后处理系统.该系统基于面向对象的编程技术,抽象出描述实际结构零件几何信息、结构特征和设计条件的智能型实体对象.将对象以及对象之间的层次关系和逻辑关系存储在AutoCAD图形数据库中,形成三维实体模型,用于直观、准确和完整地表现具有复杂空间关系和细部构造的真实结构;实现数据库对象的建立、编辑、查询、显示和数据整理等操作.该系统结合具体结构形式的特点,实现用于施工图设计和深化设计的全部关键功能.     

用水竞争力指数评价方法及其应用研究

水资源短缺加剧了区域、行业间水资源竞争的程度,影响了水资源的演变格局,也对水资源供需管理提出了新的要求。为了定量化描述用水竞争现象,辨析了用水竞争力概念、内涵及其驱动因素,引入了用水竞争力指数（WCI）,并采用驱动增长率、刚性需水量、用水效益和用水紧缺程度四类指标构建了用水竞争力评价模型,以京津冀地区为例,定量分析得到京津冀地区2001—2015年农业、工业和生活分行业多年平均用水竞争力指数分别为0.09、0.17和0.56,其中生活用水竞争力指数最大并长期保持增长趋势,成为影响京津冀地区用水竞争力的主要行业,其次是工业用水竞争力指数,但呈波动性变化特征,农业用水竞争力指数最小但呈持续增加的趋势。     

Robots positioning control revisited

Positioning control systems for robot arms are presented within a general framework in view of unification and classification. Treating coupling torques from other links as an external disturbance reduces the design of the control system to that of SISO servomechanisms driving each of the robot axes individually. A short survey of many control strategies proposed in the literature presents them as different methods aiming at attenuation of this disturbance. The use of disturbance observers is shown to provide a nice and simple way for that purpose. This could be applied to the improvement of actual industrial robots at the cost of slight software modifications.     

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.     

Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility

Key areas of robot agility include methods that increase capability and flexibility of industrial robots and facilitate robot re-tasking. Manual guidance can achieve robot agility effectively, provided that a safe and smooth interaction is guaranteed when the user exerts an external force on the end effector. We approach this by designing an adaptive admittance law that can adjust its parameters to modify the robot compliance in critical areas of the workspace, such as near and on configuration singularities, joint limits, and workspace limits, for a smooth and safe operation. Experimental validation was done with two tests: a constraint activation test and a 3D shape tracing task. In the first one, we validate the proper response to constraints and in the second one, we compare the proposed approach with different admittance parameter tuning strategies using a drawing task where the user is asked to guide the robot to trace a 3D profile with an accuracy or speed directive and evaluate performance considering path length error and execution time as metrics, and a questionnaire for user perception. Results show that appropriate response to individual and simultaneous activation of the aforementioned constraints for a safe and intuitive manual guidance interaction is achieved and that the proposed parameter tuning strategy has better performance in terms of accuracy, execution time, and subjective evaluation of users.     

Bicriteria scheduling of a material handling robot in an m-machine cell to minimize the energy consumption of the robot and the cycle time

This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.