Basic description of the topic:
Complex mechatronic systems increasingly rely on the measurements of sensors and sensor systems. These are usually asynchronous, with different noise, and sometimes with drift, and can measure certain properties of the system. The function of the filter is to provide an optimal estimate of the state of the system based on a model that describes these properties well, for example, the movement state in the case of the localisation task.
In recent decades, the international community has carried out extremely extensive research on the subject. The diploma work identifies, understands and compares the main directions of these methods based on the movement data of the PlatypOUs mobile robot platform developed at the university offline and comparing the efficiency of the procedures with the currently used program packages.
Knowledge required for the task:
- C++ and MATLAB (or Python) programming knowledge
- Git version tracking system
- Control control theory knowledge (state space model, simulation, filtering)
- Knowledge of stochastic calculations (expected value, standard deviation)
Detailed tasks:
- Learning the basics of linear/extended/unscented Kalman filter methods;
- Processing the related literature, the selection of the methods to be compared;
- Development of localisation model(s);
- Getting to know the platform, recording test data;
- Implementation, testing and optimisation of screening procedures based on test data;
- Concluding, comparing it with the currently used method;
- Best practice implementation as a ROS component;
- 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.