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对这一发现资助项目进行研究。这项研究的一个方面将集中在让机器人在处理各种物体方面更加灵活和熟练。这可以使他们以更高的效率执行任务。抓握是人类执行得最多的动作之一；然而，目前对机器人来说，以非常高效和灵活的方式像人类一样完成这一任务是一个挑战。由人类执行的抓取是基于物体的众多已知特征和关于物体的瞬时决策。这项拟议的研究将针对这一挑战，让机器人以类似人类的方式抓握，与2名MSCE、1名博士和4名本科生合作，推导出一种使用机器人灵活手指的智能抓取协议。 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都会导致产品整体不合格。塑料产品在日常生活中构成了大量的变化。之所以如此，是因为它们的制造可以以非常低的成本以高速度进行。虽然这有利于最终用户的多样性，但制造出可接受的产品(汽车、医疗、航空航天、家用电器、包装等)当涉及到塑料产品时，需要熟练的技术人员来识别产品质量分类的许多缺陷度量(视觉)。在零件尺寸方面，使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，尺寸质量是随时间变化的。更复杂的是，检查是稀疏的，在特定的时间进行一次，这可能无法捕捉到缺陷，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巡视机器人系统(MRIS)，该系统可以按需执行检查任务，并具有智能，建议其他机器在循环中进行调整。 让机器人更高效地处理各种商品，对加拿大的工业有重大好处。随着机器人在抓取方面变得更加智能，其社会影响也将实现高价值。拥有智能MIRS将有助于及早识别低质量零件，从而减少未来数以千计的废品。因此，可以显著减少回收对能源需求的影响，并减少对环境的负面影响。

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.