Robotics, Vision and Control, Responsible: Professor Jing Zhou
The research theme of robotics and vision is based on multi-disciplinary competencies in robotics, control, vision, sensors, machine learning, and dynamics systems. Our research covers the advancement of theory, algorithm design, simulation, and experimental evaluation. It focuses on robotic systems that effectively combine state-of-the-art physical-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.
The research areas include robotics, multi-robot collaboration, human-robot interaction, advanced control, sensing and vision, machine learning, autonomous systems, etc. Researchers from the group lead and participate in a number of groundbreaking national and international research projects.
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, construction, agriculture, and service.
Machine Design, Responsible: Associate professor Mohammad Poursina
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.
Intelligent Monitoring, Responsible: Professor Van Khang Huynh
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.
Biomechatronics and Collaborative Robotics, Responsible: Professor Filippo Sanfilippo
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.
Collaborative Robotics is a branch of Biomechatronics that explores the interaction and collaboration between humans and robots. This includes the design of robotic systems that can work alongside humans in shared environments, as well as the study of human-robot interaction and the development of new control strategies to enable effective collaboration. The evolution of the field may be seen as a gradual approach to human-robot interaction, human-robot cooperation, and human-robot teaming. Human-robot interaction is the study of how humans and robots interact, as well as how to develop robots that can adapt to human behavior. Human-robot cooperation expands on this by creating new approaches and technologies that allow robots to collaborate with people in shared environments. The field of human-robot teaming goes one step further, by studying how to create teams of humans and robots that can work together effectively and efficiently to achieve common goals.
This research group focuses on the intersection of Biomechatronics and Collaborative Robotics. The objective is to develop advanced technologies that enhance human capabilities and enable more efficient and effective collaboration between humans and robots. Haptics, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (ER) technologies are integrated to create immersive and interactive experiences for human-robot collaboration to enhance human capabilities and improve the efficiency of tasks.
SFI/Agder Offshore Energy Technology, Responsible: Professor Geir Grasmo.