A/Prof Adrian Wills
This course deepens students’ knowledge in Bayesian estimation. The course covers several data fusion techniques, including the topic of data association, advanced system identification, filtering versus smoothing, sensor modelling and calibration, robot mapping and map types, robot localisation, and simultaneous localisation and mapping (SLAM). I was fortunate to present my research and expertise in SLAM, further aiding in the students’ understanding in the importance of Bayesian estimation and probability theory.
This course prepares the engineer with advanced skills in the area of real-time optimisation for embedded systems. The course covers mathematical programming problem descriptions, necessary and sufficient conditions of optimality, duality, algorithm design and real-time considerations including early termination and warm-starting. The course covers special cases of linear programming, quadratic programming, convex programming and general smooth non-linear programming, slack variables and soft constraints as they arise in engineering problems. It also covers non-linear least squares problems with application to parameter estimation in dynamic systems, and integer programming and mixed-integer programming and their application to multi-agent task planning. The course has a particular emphasis on embedded systems applications where accuracy can be traded for speed.
This course teaches the fundamentals of modelling and simulating mechatronic systems. Systems analysed in this course have interacting mechanical translation, mechanical rotation, electrical, and fluid components.
MCHA3900 - Mechatronic System Design II
This is a project based course, where students deepen their knowledge on the mechatronic system design process. This course primarily focuses on modelling, control and estimation for both robot manipulators and vehicles operating in 3D space.