Measurements are the key to knowledge. This also applies to intelligent vehicles, machines and production systems. But it doesn't stop there. In order to improve the performance level of these systems under all conditions, these measurements should also be used to robustly adapt their control.

Concrete research objectives

We contribute to the development of technology for intelligent monitoring and advanced control systems and for supporting the decision-making process. We zoom in on the following research domains:

Sensing & monitoring

  • Glass fibre sensors: robust alternative for measuring, among others, pressure and temperature. 
  • Self-calibrating accelerometers for inertial navigation
  • Predictive condition monitoring, focusing on bearings, gears, motors and electromechanical powertrains.
  • Indoor and outdoor localisation systems: AGVs, mobile robot platforms and drones. These typically make use of a variety of technologies. Our research focuses on finding the most efficient solution for improved accuracy, a higher update rate and a better cost efficiency.
  • Vision-based monitoring (1D, 2D, 3D; visible and infrared), design of optical systems equipped with freeform lenses and image processing algorithms. 
  • Self-learning sensors, which automatically adjust to their environment so as to generate more reliable data, configure themselves and perform self-diagnostic tests upon the occurrence of errors.

Control & decision making    

  • Optimal control thanks to new, robust operating algorithms that are easier to adjust and operate in real time so as to permanently ensure the highest possible quality.
    • Context aware control: detection of actual operating context
    • Dynamic path planning: for mobile systems to avoid obstacles
    • Optimal trajectory generation: planning trajectory taking constraints into account
  • Learning control, enabling the system to adjust to variable production processes and anticipate the demand for lot size 1 production.
    • Safe learning: guarantees that constraints are respected during the learning process
    • Simple learning: increases robustness through use of simple, parametrized models
    • Shared learning: from the system's own experience or from that of similar systems in the fleet
  • Supporting tools in the decision-making process that help to filter out relevant data (using artificial intelligence).

For whom?

  • Manufacturing companies looking for a strategy to obtain a zero-defect, zero-machine downtime production.
  • Manufacturing companies that need technology for developing flexible, automated production lines.
  • Machine builders that wish to incorporate condition and process monitoring.
  • Software companies that develop tools for designing and testing mechatronic systems.