Artificial IntelligenceCopyright: © IGMR
The topics of artificial intelligence, machine learning and digital transformation can be found in all of IGMR's research fields.
Artificial intelligence for Type- and Dimension SynthesisCopyright: © IGMR
At IGMR, research is also being conducted into the interaction of Artificial Intelligence with Methods of Classical Mechanism Technology. The main focus is on Intelligent Structure and Dimension Synthesis of planar Mechanism. For example, an approach to this was presented in the Dissertation "Artificial intelligence for type- and dimensional synthesis of planar guidance and transfer mechanisms" by Dr. Mario Müller. According to this approach, the correct Mechanism Structure for a Motion Task can be generated from a Database by an Expert System. The Mechanism Dimensions of the selected Structure can then be Synthesized using nonlinear Optimization Algorithms to suit the given Motion Task.
Machine Learning in Vibration TechnologyCopyright: © IGMR
The goal of our Research is to develop new, highly automatable Methods for the Construction of Process and Condition Monitoring Systems, by combining classical methods of Vibration Analysis with Machine Learning approaches. In addition, the automation of vibration analysis created in this way opens up new possibilities for efficiently evaluating large amounts of data and thus for better understanding complex processes.
RoboticCopyright: © IGMR
Task Planning and Fleet Management
AI-based task planning allows multi-robot systems and collaborative teams to dynamically allocate, schedule, and execute tasks, and adaptively respond to unforeseen events.
Sensor Data Evaluation and Computer Vision
Based on machine learning, an artificial agent can be given an understanding of its environment through sensor data evaluation. This allows an artificial agent to learn its position and interaction possibilities in the environment. In addition, sensor data analysis allows predictive maintenance of machines and devices. This is also referred to as predictive maintenance. By using machine learning, it is possible to monitor the process and the value chain.
Mobile Autonomous Systems
AI enables mobile autonomous systems to move autonomously in their environment and to respond dynamically to environmental events and changing requirements. With appropriate intelligence, mobile robots can learn processes, recognize intentions, and make independent decisions about purposes, actions and paths to travel.