Om senteret

Assisted Living, Recycling (batteries), Flexible Production, Construction, and Offshore Mechatronics.

AA 1 Assisted Living, Responsible: Assistant professor Morten Ottestad 
The number of elders grows in an increasing rate, and as there are not enough nurses or doctors to handle this growing population, smart systems must be available to let elders live at home without requiring human intervention for basic tasks. Smart house with a voice-enabled assistant, friendly robots with voice, cameras, and distance sensors and smart machines for routine tasks will be a growing area in the future. A master specialization in mechatronics will be developed in collaboration with the center for I4Health. Two labs will be established: 1) A bioinstrumentation lab where we are acquiring, processing, and analysing biomechanical and physiological measurements to develop diagnostic systems and human interaction design. 2) An extended body capability lab for granting enhanced force and mobility to a human body, for instance active prosthesis, semiautonomous wheelchair. 

AA 2 Recycling (batteries), Responsible: Associate professor Martin Choux 
The primary goal is to reduce cumulative environmental damages (land clearing, mitigation of CO² emissions, nature (conservation) from the current linear economy system by recycling waste when reducing and reusing are not possible. In the case of scarce metals like cobalt and lithium present in batteries, recycling can extend the life span of the stocks, mitigate dependence on imported materials, help to retain the value of recovered materials within the Norwegian economy and create employment in the recycling sector. While a lot of research has been published since 2009 about chemical processes for recycling lithium batteries, automatic discharging, disassembly, and sorting processes have received very little attention. These steps are nonetheless essential for effective and economically achievable repair, reuse or recycling. Standing on the shoulders of the five TRCM research themes, AA 2 Recycling (Batteries) will push the technology forward in automatic discharging, robotic disassembly and sorting of batteries. 

Resirkulering av batterier.

AA 3 Flexible Production, Responsible: Assistant professor Kristian Muri Knausgård 
The industry in Agder comprises a few world-leading companies and many SMEs who are suppliers and independent manufacturers of goods and commodities. Trends in manufacturing industry go towards flexible production of small series or even production of individually produced units. This development is challenging to production equipment, usually developed for standardized production of large series. Automation for flexible production requires the development of more intelligent robots, including vision, flexible management systems, collaborative control of cooperating units, failure monitoring, and correction systems. 

AA 4 Construction, Responsible: Professor Rein Terje Thorstensen 
Construction is the largest industrial sector in Agder by employment, equal to the aggregated sum of all other industrial sectors. This industry is known for a low degree in digitalization and robotization, and Norway is further lagging towards the international competition. International research and application trends include digital twins, robotized assembly, autonomic vehicles, 3D printing of massive structures, structural monitoring and use of Artificial Intelligence. The potential for technology transfer from the drilling industry is huge, also in areas like environmental monitoring and cleaning technology for runoff water. Pilotveien Finsland - artikkel og film

AA 5 Offshore Mechatronics, Responsible: Professor Geir Grasmo

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 Industrial IT, Responsible: Associate professor Muhammad Faisal Aftab 
Research at Industrial IT group is aimed at developing innovative and smart industrial automation solutions for fault diagnosis and performance monitoring.  The objective is to enhance the robustness and autonomy of the industrial control system via harnessing technologies like machine learning, industrial internet of things (IIOT), and cloud computing, as envisioned by Industry 4.0. Two PhD positions are associated with this group to achieve the stated objectives.

RT 3 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 4 Robotics & Vision, Responsible: Professor Jing Zhou 
Robotics is a transformative technology with potential applications across a range of industries, including manufacturing, agriculture, construction, and service. Developing robots that understand what they see is the key for emerging applications in robotics and automation. Vision is a complex process requiring rapid and continuous feedback for control, especially when the robot is performing complex interactive tasks in unstructured environments. Control of a robotic system interacting with a changing environment is challenging. We will develop robots that can perceive their environment: that sense, understand and learn in order to improve performance over time. 

RT 5 Collaborative Robots (CoBots), Responsible: Professor Filippo Sanfilippo.  
Outline of the research. A cobot or co-robot (from collaborative robot) is a robot intended to physically interact with humans in a shared workspace. With the advent of the fourth industrial revolution (4IR), cobots are advancing from being simple stand-alone manipulators passing tools or parts to human collaborators to becoming autonomous co-workers. However, there is a gap between the desire for humans and robots to work closely together and share control of operations, and how robustly we can measure and predict human motions and intentions in physical human-robot interaction (pHRI) operations. 

Our research aims at fostering the development of a novel framework architecture to enable humans and machines for working hand in hand within full-inclusive environments by achieving both human-robot and robot-robot collaboration in a robust, safe and secure manner. This framework architecture shall be designed to securely integrate proactive intelligent detection approaches with predictive maintenance methods for potentially dangerous events by complementing the workspace with a combination of novel sensing technologies and multi-sensor highly compliant robots. The proposed framework shall enable researchers to develop control algorithms for cobots more safely, rapidly, and efficiently.

One of the most relevant application areas is the employment of cobots for assistive health. In this perspective, our aim is to design, prototype and verify an ecosystem of assistive technology for personalised tele-rehabilitation and physiotherapy. The objective of this  research is twofold: first to develop a Biomechatronics Lab as a prototyping incubator for novel assistive technology, based on the integration of haptics, virtual reality (VR)/augmented reality (AR) technology,  sensor-fusion and gamification; second, to deploy a sustainable and simplified selection of the same technology at home for personalised training and tele-rehabilitation purposes to keep patients confident to live independently.