Course Title: Autonomous Systems

Part A: Course Overview

Course Title: Autonomous Systems

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


Bundoora Campus


115H Aerospace, Mechanical & Manufacturing Engineering


Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016


Bundoora Campus


172H School of Engineering


Sem 2 2017

Course Coordinator: A/Prof. Reza Hoseinnezhad

Course Coordinator Phone: +61 3 9925 6135

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge: It is assumed that students have successfully completed the courses Automatic Control Systems (MANU1174), as well as Introduction to Mechatronics (MANU2205), or equivalent to those two courses, before attempting this course.

Course Description

This course introduces you to various concepts and components of autonomous systems in an autonomous mobile robotics context. The main concepts covered include locomotion, vehicle kinematics, autonomous navigation and intelligent path planning and perception. System components include various types of sensors and actuators and state-of-the-art technologies.

This is a project-based learning course. The project is a mobile robotics competition. In collaboration with your team members, you will devise strategies and create behaviours to be exhibited by an autonomous mobile robot. The robot equipped with the autonomous behaviours that you have designed and created, is expected to win a challenging competition.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level.
This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.(;ID=eyj5c0mo77631)

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes:

  • Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline
  • Fluent application of engineering techniques, tools and resources
  • Application of systematic engineering synthesis and design processes
  • Effective oral and written communication in professional and lay domains.
  • Creative, innovative and pro-active demeanour
  • Effective team membership and team

Course Learning Outcomes (CLOs)

On completion of this course you should be able to:

CLO 1.      Clarify problem definition based on a client’s needs for a system to achieve a level of autonomy, and devise a layout of locomotion and sensing requirements of the system that would be able to meet the client’s needs in an autonomous manner;

CLO 2.      Design a system with sensing, actuating and embedded processing components, required to run the defined task autonomously;

CLO 3.      Program the embedded processor of your designed system to gain machine intelligence in terms of perception and actuation; and 

CLO 4.      Work effectively as part of a team to devise and implement experimental benchmarks to examine the performance of your designed system and the extent of its autonomy, stability and robustness. 

Overview of Learning Activities

In the lectures, you will learn the fundamentals of how to devise autonomous behaviours in an intelligent machine. In the tutorials, you will learn how to create programs in MATLAB and Simulink to implement the autonomous behaviours. In the lab sessions, you will put theory into practice.

All the lectures and other resources you need to complete the project, will be made available online in the Blackboard. This course will significantly enhance your employability by enriching your technical skills in the context of machine intelligence, and your teamwork, project management and communication skills.  

Overview of Learning Resources

You will be able to access course information and learning materials through the Learning Hub and may be provided with additional materials in class. Lists of relevant reference books and digitalised materials at RMIT libraries will be available as well. You will also use equipment and software packages in the laboratory for the project work. During the course, students will be directed to many websites to enhance your knowledge and understanding of difficult concepts.

Overview of Assessment

 X This course has no hurdle requirements.

☐ All hurdle requirements for this course are indicated clearly in the assessment regime that follows, against the relevant assessment task(s) and all have been approved by the College Deputy Pro Vice-Chancellor (Learning & Teaching).

Assessment tasks

Assessment Task 1: Multiple-choice online quizzes
Weighting 10%
This assessment task supports CLOs 2 & 3

Assessment Task 2: Programming test
Weighting 15%
This assessment task supports CLO 3

Assessment Task 3: Laboratory tests
Weighting 25%
This assessment task supports CLOs 1 & 2 & 3

Assessment Task 4: Final exam
Weighting 50%
This assessment supports CLOs 1 & 2 & 3