MTRN4010 Advanced Autonomous Systems Coursework Brief 2025

Course Aims 

The course is aimed at learning basic and advanced techniques that are necessary for sensing and control of autonomous mechatronic systems. Contents covered in this course include Bayesian state estimation / Sensor data fusion, and certain relevant techniques (Dynamic Programming, Optimization, PSO). Half of the course is lecture-based. In the other half, students implement and apply those techniques. In addition to the theory discussed in the lectures, the course is also focused on practical implementation matters, involving implementing processing modules, which are tested with real measurements from sensors, additionally being tested in simulation, in diverse scenarios.

Course Learning Outcomes

• CLO1: Analyse the theoretical underpinnings of Bayesian Estimation and demonstrate advanced proficiency in implementing the Kalman Filter to address diverse engineering challenges.
• CLO2: Apply critical analysis to optimization techniques, demonstrating the ability to strategically employ them in solving complex engineering problems.
• CLO3: Evaluate and integrate Sensor Data Fusion methodologies for vehicle localization and other engineering challenges, demonstrating a mastery of their application. 
• CLO4: Design and implement software solutions for solving complex engineering problems, critically assess the performance of applied approaches and gain experience with typical sensors utilized in Field Robotics and Autonomous Systems.