【标题】Universal Robots opens UK's newest robotic hub in Yorkshire
【参考中译】环球机器人公司在约克郡开设英国最新的机器人中心
【类型】 期刊
【作者】 Justin Burns
【摘要】 Universal Robots ( www.tinyurl.com/4knenxum ) has opened its first UK hub in Sheffield which has the principal aim of helping UK manufacturers unlock the benefits of collaborative robotics and automation. Leveraging Yorkshire's strong manufacturing links, the new robotics hub will house offices and a showroom where the latest technology and applications for cobots (collaborative robots) will be demonstrated.
【参考中译】 Universal Robots（www.tinyurl.com/4knenxum）在谢菲尔德开设了其第一个英国中心，其主要目的是帮助英国制造商释放协作机器人和自动化的好处。利用约克郡强大的制造业联系，新的机器人中心将设有办公室和展示厅，展示协作机器人（协作机器人）的最新技术和应用。
【来源】 Machinery 2023, vol.181
【入库时间】 2024/2/22
【标题】German robotics rising: A look at ten of the hottest new players in German robotics and automation
【参考中译】德国机器人技术的崛起：看看德国机器人和自动化领域十大最热门的新玩家
【作者】 Kevin Jones
【摘要】 Germany is an undisputed leader in engineering and design. What has been true for decades is true today, as the world becomes increasingly automated and more and more of us find ourselves working side-by-side with automated assistants. Germany is proving to be fertile ground for new ideas in robotics and automation, and German engineering and manufacturing are proving as capable as always at bringing new ideas to market. On this and the next two pages, we present, in no particular order, a few of this year's hottest German robotics startups. Robots that play well with others. Cobots are the specialty of NEURA Robotics, a company founded in 2019 in Metzingen, near Stuttgart, with the goal of "revolutionizing the world of robotics." The company's mission is to expand the skill set of collaborative robots with cognitive capabilities so that robots can work with humans in existing environments without the need to invest in complex and costly safety systems. NEURA Robotics' machines, in brief, are engineered to play well with others.
【参考中译】 德国在工程和设计方面是无可争辩的领导者。几十年来一直是正确的，随着世界变得越来越自动化，越来越多的我们发现自己与自动化助手并肩工作，今天也是如此。事实证明，德国是机器人和自动化领域新想法的沃土，德国的工程和制造业一如既往地有能力将新想法推向市场。在这篇文章和接下来的两页中，我们将不分先后地介绍几家今年最受欢迎的德国机器人初创公司--S。CoBots是Neura Robotics的专长，该公司于2019年在斯图加特附近的梅津根成立，目标是“彻底改变机器人世界”。S的使命是扩大具有认知能力的协作机器人的技能集，使机器人能够在现有环境中与人类合作，而不需要投资于复杂而昂贵的安全系统。简而言之，NeuRA Robotics的机器设计就是为了与他人很好地合作。
【来源】 Electrical Apparatus 2023, vol.76, no.10
【标题】Machine Learning Methods to Improve the Accuracy of Industrial Robots
【参考中译】提高工业机器人精度的机器学习方法
【作者】 Lauren McGarry; Joseph Butterfield; Adrian Murphy; Colm Higgins
【摘要】 There has been an ongoing need to increase the application of industrial robots to complete high-accuracy aerospace manufacturing and assembly tasks. However, the success of this is dependent on the ability of robotic systems to meet the tolerance requirements of the sector. Machine learning (ML) robot error compensation models have the potential to address this challenge. Artificial neural networks (ANNs) have been successful in increasing the accuracy of industrial robots. However, they have not always brought robotic accuracy within typical aerospace tolerances. Methods that have not yet been investigated to further optimize the use ANNs used in ML robot error compensation methods are presented in this paper. The focus of ML compensation methods has dominantly surrounded ANNs; there have been little to no investigations into other types of ML algorithms for their suitability as robot error compensation models. The success of ANNs to date proves the capability of ML algorithms for this task, and therefore other ML algorithms should be investigated to determine their capability to potentially improve industrial robot accuracy. This paper takes a novel approach by investigating the Support Vector Regression (SVR) ML algorithm to compensate for robot error. The ML models in this research were trained using measurement data captured using a laser tracker and collaborative robot. The ANN model reduced the mean error by 46.4%, 94.8%, and 95.8%, in the x, y, and z-axis, respectively. The SVR model reduced the mean error by 42.4%, 95.9%, and 98.4%, in the x, y, and z-axis, respectively, demonstrating its ability to be implemented as a robotic error compensation model. The success of both the ANN and SVR algorithms enforces the need for further research into other ML algorithms as robot error compensation models, and there is also still potential to further optimize the algorithms used.
【参考中译】 一直需要增加工业机器人的应用，以完成高精度的航空航天制造和组装任务。然而，这项工作的成功取决于机器人系统满足该部门公差要求的能力。机器学习(ML)机器人误差补偿模型有可能解决这一挑战。人工神经网络(ANN)已经成功地提高了工业机器人的精度。然而，它们并不总是将机器人的精度带到典型的航空航天公差范围内。提出了在最大似然机器人误差补偿方法中进一步优化神经网络使用的方法。最大似然补偿方法的焦点主要围绕神经网络；对于其他类型的最大似然算法作为机器人误差补偿模型的适用性，很少或根本没有研究。到目前为止，人工神经网络的成功证明了ML算法对这一任务的能力，因此应该研究其他ML算法，以确定它们的能力，以潜在地提高工业机器人的精度。通过研究支持向量回归(SVR)最大似然算法来补偿机器人的误差，提出了一种新的方法。本研究中的ML模型是使用激光跟踪器和协作机器人捕获的测量数据进行训练的。神经网络模型在x、y和z轴上的平均误差分别降低了46.4%、94.8%和95.8%。SVR模型在x、y和z轴上的平均误差分别降低了42.4%、95.9%和98.4%，证明了其作为机器人误差补偿模型的能力。人工神经网络和支持向量机算法的成功加强了对其他最大似然算法作为机器人误差补偿模型的进一步研究的需要，而且还存在进一步优化所用算法的潜力。
【来源】 SAE International Journal of Advances and Current Practices in Mobility 2023, vol.5, no.5