Enhanced awareness for mobile cobots in manufacturing assembly
Enhanced awareness for mobile cobots in manufacturing assembly
批准号:
566182-2021
负责人:
StOnge, David
金额:
$3.44万
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31
中文摘要
在工业可用的大量机器人架构中,最近出现了新的范例。更具体地说,协作机器人近年来经历了令人印象深刻的增长,并已渗透到许多新市场,特别是中小企业之间,这些中小企业以前不愿意安装机器人和自动化设备,不仅是因为财务风险,而且首先是人类人员的健康和安全。协作机器人(通常缩写为cobots)通过提供最具吸引力的特性来缓解这一主要问题,即能够在不危及人类的情况下与人类近距离工作,并且不需要复杂而昂贵的安全设备。Kinova是加拿大为数不多的机器人手臂制造商之一,也是世界上为数不多的专注于以人为本的设计应用的制造商之一。对于制造业来说,他们的产品仍然是新的,他们的产品已经为工业的协作机器人革命而制造。该项目旨在开发软件、抓取设备和算法,Kinova将与他们的产品一起提供,以促进他们在市场上的整合。然而,我们并不坚持工业机械手的常见固定设置,而是旨在通过专注于移动操作,使Kinova在市场上具有真正的优势。我们的场景是几个移动机械手在保证工人安全的情况下合作为制造和装配站提供饲料。对于Kinova和加拿大机器人行业来说,能够达到并展示高水平的安全性是至关重要的。该项目将为移动协作机器人在制造业部署的测试和验证铺平道路。除了验证之外,这项研究还将解决在制造业部署移动协作机器人的基本挑战。我们的机械工程方法旨在为装配工作站带来计量级精度,即使是在移动基座上的机械手。在我们的方法中,我们将利用双臂适应抓取装置,基于模型的几何抓取学习和以操作员为中心的表上校准。那么,当移动协作机器人自主离开工作站时,它可能会成为工人在充满压力和危险的环境中的负担。我们的目标是同时进行轮式机器人和人类机载检测的可解释运动,为周围工人的运动提供适应性可解释轨迹的新潜力。
英文摘要
Among the vast range of robotic architectures available to industry, new paradigms have recently emerged. More specifically, collaborative robotics has experienced an impressive growth in recent years and has penetrated many new markets, especially between SMEs, which were previously reluctant to install robotic and automated devices due to the risk, not only financial but first and foremost with respect to the health and safety of human personnel. Collaborative robots (commonly abbreviated cobots) alleviate this major concern by offering the most attractive feature of being able to work in close proximity with a human being without endangering the latter and not requiring complex and costly safety equipment. Kinova is one of the few Canadian manufacturers of robotic arms, and one of the few worldwide that focus their design on human-centered applications. Still new to the manufacturing sector, their products are already made for the collaborative robotic revolution of the industry. This project aims at developing software, grasping devices and algorithms that Kinova will be providing alongside their products to facilitate their integration in that market. However, we do not stick to the common fixed setup of industrial manipulators, but rather aim at giving Kinova a real edge on the market by focusing on mobile manipulation. Our scenario is several mobile manipulators cooperating to feed manufacturing and assembly stations while maintaining the workers' safety. For Kinova and for the Canadian robotic industry, it is of the utmost importance to be able to reach and demonstrate high levels of safety. This project will pave the way to testing and validation of mobile cobots deployment in the manufacturing sector. More than validation, this research will address fundamental challenges to the deployment of mobile cobots in the manufacturing sector. Our mechanical engineering approach aims at bringing metrology-level accuracy to the assembly work station even with manipulators on mobile bases. Among our methods we will leverage dual arm adapted grasping device, model-based geometric grasping learning and operator-centric on-table calibration. Then, when the mobile cobot leaves the workstation autonomously, it may become a liability to the workers in a stressful environment with numerous dangers. We aim at doing explainable motion of wheeled robots and human on-board detection simultaneously, granting the novel potential for adaptive explainable trajectories to surrounding workers movements.
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