A learning force control strategy for robotic gripping and a mobile robot inspector system for intelligent factories

新不伦瑞克大学机械工程系的四名研究生和六名本科生将在这个发现基金项目中进行研究。研究的一个方面将集中在使机器人更加灵巧和熟练地处理各种物体上。这可以使他们以更高的效率执行任务。抓握是人类最常做的动作之一；然而，目前对机器人来说，以一种非常高效和灵巧的像人一样的方式做到这一点是一个挑战。人类的抓握是基于物体的众多已知特征和对物体的即时决策。拟议的研究将针对这一挑战，使机器人以类似人类的方式抓取，与2名msc， 1名博士和4名本科生合作，利用机器人灵活的手指得出智能抓取协议。由于许多部件可以组成一个产品，任何一个或多个不正确的质量标准将导致一个整体失败的产品。塑料制品在日常生活中构成了大量的变化。这是因为它们的制造可以以非常低的成本以很高的速度进行。虽然这有利于最终用户的多样性，但制造可接受的产品（汽车，医疗，航空航天，电器，包装等）需要熟练的人员来识别许多缺陷度量（视觉），用于塑料产品的产品质量分类。在零件尺寸方面，采用各种测量方法扫描零件的三维形状，以验证尺寸质量。然而，尺寸质量是时变的。更复杂的是，检查是稀疏地进行的，一次在特定的时间，这可能无法捕获缺陷，导致更多的不合格产品。拟议的研究也将针对这一挑战，与1名博士和2名本科生合作，创建一个移动检查机器人系统（MRIS），该系统可以按需执行检查任务，并智能地建议其他机器在循环中进行调整。让机器人更有效地处理各种各样的货物，对加拿大的工业有很大的好处。随着机器人在抓握方面变得更加智能，其社会影响也将实现高价值。拥有一个智能的MIRS将有助于及早识别低质量的零件，从而减少未来成千上万的次品。因此，回收对能源需求的影响可以大大减少，同时也减少了对环境的负面影响。

Four graduate and six undergraduate students in the Department of Mechanical Engineering at the University of New Brunswick will conduct research on this Discovery Grant project. One aspect of the research will focus on making robots more dexterous and skilled at handling varied objects. This can result in them perform tasks at higher levels of efficiency. Gripping is one of human's most-performed actions; however it is currently a challenge for robots to do this in a human-like way that is very efficient and dexterous. Gripping performed by humans is based on numerous known characteristics of objects and instantaneous decision making about the objects. The proposed research will target this challenge of making robots grip in a human-like way, working with 2 MScEs, 1 PhD and 4 undergraduates to derive an intelligent gripping protocol using flexible fingers on a robot. As many components can form a product, any one or more that is not the correct quality standard will result in an overall failed product. Plastic products constitute a vast number of variations in every day life. This is so since their manufacture can be conducted at high rates at a very low cost. While this is good to the end-user for diversity, making an acceptable product (automobile, medical, aerospace, appliances, packaging etc.) requires skilled personnel to identify the many defect metrics (visually) for product quality classifications when it come to plastic products. In terms of part dimensions, various measuring methods are used to scan the 3-D shape of parts for verifying dimensional quality. However, dimensional quality is time-varying. To compound this, inspection is conducted sparsely, once at a specific time, which may not capture the defect, resulting in many more failed products. The proposed research will also target this challenge, working with 1 PhD and 2 undergraduates to create a mobile inspector robot system (MRIS) that can perform inspection tasks in an on-demand manner with intelligence to advise other machines to make adjustments in the loop. There is significant benefit to Canadian industries in making robots more efficient at handling varied goods. The societal impact will also realize high value as robots become more intelligent at gripping. Having an intelligent MIRS will help identify low quality parts early so as to mitigate thousands of future rejects. Therefore, the impact on energy requirements for recycling can be significantly reduced, as well as reducing negative effects on the environment.