Units

---

Sample subject outline - Semester 1 2013

Note: Subject outlines often change before the semester begins. Below is a sample outline.

Rationale

Increasingly human operators expect that modern computer controlled machinery relieves them from routine control actions, operating decisions, failure diagnostics and maintenance operations. Computational Intelligence methods are an important element in the achievement of this goal and professionals in the area of Mechatronics require a good working knowledge in this area.

Aims

This is a specialisation unit in the area of Mechatronics that introduces methods from the field of computational intelligence and relates them to applications on real time control and embedded systems. The methods are: Behaviours Based Robotics, Fuzzy Control, Probabilistic Robotics, and Machine Learning Learning.

The unit is also intended to teach the specific design and programming skills that will enable you to solve problems using computational intelligence methods in real-time embedded systems. It is assumed that you already have knowledge of programming.

Objectives

On successful completion of this unit, you should be able to:

Demonstrate understanding of the meaning of intelligence in the context of autonomous systems including control software for real time and embedded systems (GC1, GC2).

Demonstrate knowledge of fundamental concepts of behaviour-based systems, fuzzy control, probabilistic robotics, and machine learning programming (GC1, GC2).

Demonstrate knowledge of the kind of functionality that can be achieved with each of the techniques mentioned in the previous item (GC1, GC2).

Demonstrate understanding of the strengths and weaknesses of these methods in specific examples where these techniques have been implemented (GC1, GC2, GC3).

Show improvement in your skills for generic knowledge acquisition, problem analysis, problem solving and critical thinking (GC2).

Identify problems where Computational Intelligence (CI) methods could be applied advantageously (GC1, CG2) and write program modules that implement CI methods for real time control and embedded systems.

Key: Graduate Capabilities
GC1 - Knowledge and Skills
GC2 - Critical and Creative Thinking
GC3 - Communication
GC4 - Lifelong Learning
GC5 - Independence and Collaboration
GC6 - Social and Ethical Responsibility
GC7 - Leadership and Change

Content

This unit introduces the four methods from the field of computational intelligence with special emphasis on applications in real time control and embedded systems. The four methods are: Rule based systems, Fuzzy control, Neural Networks, and Reinforcement Learning. It also introduces programming of these methods for implementation of real-time control and embedded systems.

Approaches to Teaching and Learning

Lectures and practicals aim at achieving objectives 1 to 5. Lectures will provide the knowledge base required for this unit. The practicals will give you the opportunity to apply the theory described in lectures to solving small-scale illustrative problems using real robots (Lego Mindstorms NXT kits)..

You will also be expected to solve a somewhat larger problem in the context of an assignment project, and thereby address objectives 6 to 8.

Lectures will be held in a MELT for live demonstrations of software and visualization techniques. Theoretical lecture content will be made available in weekly releases of lecture notes downloadable from QUT's Blackboard web site. You will also have practicals each week in a computer laboratory. Teaching staff will answer questions arising from the practicals sessions. The lecturer will also answer your questions via e-mail and/or consultation.

Assessment

The assessment of this unit has two components, a formative component consisting of one assignment that you can do in teams.

The normative component consists of the mid-semester exam and the final exam.
The mid-semester exam serves to familiarise you with the style of the exam questions used in this unit and also provides you with early feedback on your level of understanding of the material in the unit.Feedback will be available on your progress.

Assessment name: Project (applied)
Description: The aim of this team assignment is to create and implement a complex robotic behaviour. For example, the aim could be to program robot to play soccer.
Relates to objectives: 1 to 4.
Weight: 40%
Internal or external: Internal
Group or individual: Group with Individual Component
Due date: Start Week 11

Assessment name: Examination (Theory)
Description: Summative Examination
Relates to objectives: 1 to 4.
Weight: 20%
Internal or external: Internal
Group or individual: Individual
Due date: Week 8

Assessment name: Examination (Theory)
Description: Summative Examination
Relates to objectives: 1 to 6.
Weight: 40%
Internal or external: Internal
Group or individual: Individual
Due date: Exam Period

Academic Honesty

QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.

A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.

Resource materials

Lecture Notes: To be released on the Blackboard
Practicals Materials: To be released on the Blackboard

References:
1. Stuart J. Russel and Peter Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall (2010)
2. Bart Kosko, Fuzzy Engineering. Prentice Hall (1996)
3. Simon Haykin, Neural Networks: A Comprehensive foundation. Prentice Hall (1998)
4. Martin T Hagan, Howard B. Demuth and Mark Beale, Neural Network Design. PWS Publishing Co, (1996)

No extraordinary costs or charges are associated with the requirements of this unit.

Risk assessment statement

There are no out of the ordinary risks associated with this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.

Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.

Last modified: 22-Feb-2013