Safe Adaptive Social Cyber Physical Systems

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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.