Research groups: Machine Design, Industrial IT, Intelligent Monitoring, Robot & Vision, and Collaborative Robots.
RT 1 Machine Design
Responsible: Associate Professor Morten Kjeld Ebbesen
The classic theory of statics and dynamics together with multibody dynamics are used to model rigid and flexible dynamic systems. This also includes the hydraulic and/or electric actuators and the entire drive train. With these models, the operation of the system is simulated and realistic loads on the mechanical parts are obtained. On this background, the structural integrity of the entire system and down to the individual machine elements like shafts, bearings, and gear wheels can be checked, and the expected service life can be estimated with fatigue theory. The approach sketched here can be applied in the design process to evaluate different designs before resources are spent on building them for example when prototyping is too expensive. Research is carried out to improve the different steps like the accuracy of the modelling or the computational procedure for life estimation.
RT 2 Intelligent Monitoring
Responsible: Professor Van Khang Huynhab
Condition monitoring is of key importance to avoid unexpected system breakdowns and unplanned shutdowns, including costly production loss and human safety risks. This research team focuses on developing physics- and AI based methods to monitor the health status of subcomponents in mechatronic systems and energy systems, and to estimate their remaining useful lifetime using limited historical failure data for asset management. Towards intelligent electric vehicles and smart grids, the team aims to develop control and monitoring tools to enhance security, reliability, and performance of cyber and physical components under faults or cyber-attacks.
RT 3 Robotics & Vision
Responsible: Professor Jing Zhou
The research theme of robotics and vision is based on strong core disciplinary competencies in robotics, control, vision, sensors, machine learning, artificial intelligence, and dynamics systems. Our research covers the advancement of theory, algorithm design, simulation, and experimental evaluation. Our research focuses on robotic systems that effectively combine state-of-the-art model-based mechanisms, as a priori information, while allowing adaptation and learning at every level of the robot’s control system – sensing, perception, navigation, manipulation, decision-making, planning, and human-robot interaction. Our vision is to facilitate a higher degree of autonomy by developing robots to cope with unanticipated changes in the process and the environment, perform complex interactive tasks, and include high-level cognitive functions for effective collaboration with other robots and humans. The technology has potential applications across various industries, including manufacturing, battery, agriculture, construction, and service.
RT 4 Biomechatronics and Collaborative Robotics
Responsible: Assistant Professor Morten Ottestad
Biomechatronics is a multidisciplinary field that combines principles of biology and mechatronics (electrical, electronics, and mechanical engineering). It also encompasses the fields of robotics and neuroscience. This discipline aims at designing and controlling advanced robotic systems that interact with the human body. This field includes a wide range of applications, including the design and control of wearable and implantable devices, prosthetics, exoskeletons, and assistive robots.