1 x 120-minute lecture weekly
1 x 60-minute tutorial weekly
1 x 120-minute laboratory weekly
1 ENGR3711 - Control Systems
2 ENGR2721 - Microprocessors
3 Admission into GDPEE-Graduate Diploma in Engineering (Electronics)
3a Admission into HBIT-Bachelor of Information Technology (Honours)
3b Admission into HBSC-Bachelor of Science (Honours)
3c Admission into MEE-Master of Engineering (Electronics)
3d Admission into GDPEB-Graduate Diploma in Engineering (Biomedical)
3e Admission into MEB-Master of Engineering (Biomedical)
4 1 of ENGR2772, ENGR8772
Must Satisfy: ((1 and 2) or ((3 or 3a or 3b or 3c or 3d or 3e)) or (4))
Assumed knowledge
Students should have a good notion of probability and statistics
Examination, Log book, Quiz, Report(s)
Topic description

How noisy sensor measurements can be interpreted in time? How can autonomous systems (in the virtual or in the real world) use their noisy measurements to learn the best course of actions (policy) to take? This topic teaches how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Robust estimation techniques are employed to produce values that tend to be closer to the true values of the measurements and their associated calculated values and are an essential part of the development of advanced navigation systems and principles of machine learning (mainly reinforcement learning and deep reinforcement learning) will be applied to optimize the agent's behaviour in its environment. In other words, the syllabus includes probabilistic algorithms, Markov decision processes, Bayesian filtering and reinforcement learning.

Educational aims

This topic aims to introduce students to the fundamental principles of theory and practice of estimation and machine learning applied on the interpretation of noisy sensor data from any source. The performance of the sensors, including precision, accuracy, repeatability, sensitivity, linearity and dynamic performance, are analysed. Calibration and estimation techniques are also examined. Reinforcement learning and Deep Reinforcement Learning will be introduced as a means to take the students to the state-of-the-art of modern intelligent systems and sensor interpretation.

Expected learning outcomes
On completion of this topic you will be expected to be able to:

  1. Understand the principles used by the various instrumentation sensors to determine attitude, position, acceleration, velocity, heading
  2. Analyse the factors affecting measurement performance with respect to navigation sensors
  3. Criticise various design solutions for estimation and machine learning algorithms to be applied in distinct situations
  4. Create experimental frameworks for the various situations investigated
  5. Evaluate the results and outcomes of experiments