Safe Adaptive Social Cyber Physical Systems

安全自适应社交网络物理系统

• 批准号: RTI-2020-00733
• 负责人: Givigi, Sidney
• 金额: $ 10.93万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693822
• 关键词: Safe; Adaptive; Social; Cyber; Physical

Social collaborative robots, known as cobots, are a new emerging technology. Cobots work with human workers as a team in complex tasks, increasing the productivity of the activity when compared to only manual labour or robotic systems. The deployment of such systems is largely dependent on software development techniques that guarantee the safety of operation. One such technique is known as Model-based Software Development (MBSD). However, with the rise of machine learning and increased adaptability of robotic systems, software safety is now more complex to achieve.******This proposal deals with the application of several techniques that guarantee the safety of cobot systems while maintaining their adaptability. Among these techniques, we use the interpretability of models using discrete event systems and model construction constraints, robotic reasoning under uncertainty, estimation of human cognitive load in cooperative tasks using biological signals, and real-time guarantees through the use of MBSD. ******The equipment requested consists of two collaborative robots and an electroencephalogram (EEG) combined with functional near-infrared spectroscopy (fNIRS) headset. The equipment will be used in a lab space with a camera measurement system already available at Queen's University. The camera system will be used to track the motion of the robot and human participants. The collaborative robots will be programmed to act independently and collaborate with humans in assembly tasks. Since human motion is not completely deterministic, robots will have to deal with uncertainty while efficiently solving complex tasks.******The equipment will be used by four research programs at Queen's University: three at the School of Computing and one at the Department of Electrical and Computer Engineering and will involve initially over 20 highly qualified people. By 2021, close to 30 HQP will be directly involved in the research with the equipment. Emphasis will be given to the recruitment of a diverse student population that will benefit from the diverse research environment already available at the labs. With the increased need of competitive advantages required in the current globalized marketplace, our training programs are well suited to help fill the need of Canadian businesses and research institutes in machine learning and robotics applied to improving the outcome of industrial tasks.

社会协作机器人，被称为cobots，是一种新兴的技术。协作机器人与人类工人作为一个团队在复杂的任务中工作，与手工劳动或机器人系统相比，提高了活动的生产力。这种系统的部署在很大程度上取决于保证操作安全的软件开发技术。一种这样的技术被称为基于模型的软件开发（MBSD）。然而，随着机器学习的兴起和机器人系统适应性的提高，软件安全现在变得更加复杂。该提案涉及几种技术的应用，这些技术保证了协作机器人系统的安全性，同时保持了它们的适应性。在这些技术中，我们使用离散事件系统和模型构造约束，机器人推理不确定性下的模型的可解释性，在合作任务中使用生物信号的人类认知负荷的估计，并通过使用MBSD的实时保证。** 所要求的设备包括两个协作机器人和一个脑电图仪和一个功能性近红外光谱仪耳机。该设备将在实验室空间中使用，该实验室空间已经配备了皇后大学的相机测量系统。摄像机系统将用于跟踪机器人和人类参与者的运动。协作机器人将被编程为独立行动，并在装配任务中与人类合作。由于人类的运动不是完全确定的，机器人将不得不处理不确定性，同时有效地解决复杂的任务。这些设备将用于女王大学的四个研究项目：三个在计算机学院，一个在电气和计算机工程系，最初将涉及20多名高素质的人员。到2021年，近30名HQP将直接参与设备的研究。重点将放在招募多样化的学生群体，这将受益于实验室现有的多样化研究环境。随着当前全球化市场对竞争优势的需求不断增加，我们的培训计划非常适合帮助满足加拿大企业和研究机构在机器学习和机器人技术方面的需求，以改善工业任务的成果。