Refinement of manipulator accuracy for camera-based glass slide pick-up via modell-based and soft computing calibration

Basic description of the topic:

Industrial robot arms have extremely good repetition accuracy, but their absolute accuracy, which gives you the accuracy with which a given displacement/rotation can be achieved. This depends on the robot model used for the calculation, which is calibrated by the manufacturers and attempts to track deviations from the nominal model and give a mean value for inaccuracies due to elastic deformations and drive chain uncertainties.

This usually causes 5-10x greater inaccuracy than the robot’s repetition accuracy. This can be improved by improving the robot model. Since the early days of robotics, methods have been developed for this, both for the geometric description of the robot and for the additional uncertainty. Without claiming to be exhaustive, one of them is that the model is not designed for the entire workspace, but only for a narrow zone where precise operations are required. Fuzzy interpolation between these zones is also an exciting issue. Also, adding a soft computing layer to the traditional geometric model to describe additional properties can be a tool for calibration.

The question arose during the handling of biological samples, where the situation is complicated by the fact that, in this case, the glass plates are identified based on the installed camera. The goal is to develop the robot model and the calibration process for more accurate grasping.

Knowledge required for the task:

  • C++ and MATLAB (or Python) programming knowledge
  • Git version control system
  • Basic knowledge of robot modeling (Denavit-Hartenberg parameters)
  • Optimization methods

Detailed tasks:

  1.  Getting to know the parameters of the Denavit-Hartenberg model and related newer methods (e.g. Hayati), and the calibration methods based on them;
  2. Processing of the related literature;
  3. Developing a development plan;
  4. Data collection with the robotic arm;
  5. Implementation, testing and optimization of the calibration procedure using the data;
  6. Drawing a conclusion, comparing it with the currently used procedure;
  7. Evaluation and publication of results.

Over the course of the project, the student will get involved with the various research project of the Antal Bejczy Center for Intelligent Robotics.