Course Title: Advanced Robotics

Part A: Course Overview

Course Title: Advanced Robotics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


Bundoora Campus


115H Aerospace, Mechanical & Manufacturing Engineering

Distance / Correspondence or Face-to-Face

Sem 2 2008


Bundoora Campus


115H Aerospace, Mechanical & Manufacturing Engineering


Sem 1 2006,
Sem 2 2007,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016


Bundoora Campus


172H School of Engineering


Sem 2 2017,
Sem 2 2018,
Sem 2 2019


RMIT University Vietnam


172H School of Engineering


Viet2 2019

Course Coordinator: Dr Chow Yin Lai

Course Coordinator Phone: +61 3 9925 4416

Course Coordinator Email:

Course Coordinator Location: 057.03.018

Course Coordinator Availability: by appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge: It is assumed that students have successfully completed MANU1174 Automatic Control Systems, or an equivalent, before attempting this course. 

Course Description

This course develops your capabilities in Advanced Robotics. You will build on prior knowledge of Automatic Control systems and examine the design of robotic systems. Topics which will be covered include, (but are not limited to):

Coordinate frame transformations, kinematic analysis of robot design, formulation of matrices to develop robot arm transforms, extraction of analytic joint angle solution equations from both forward and inverse kinematic matrices; planning trajectories in joint space to accomplish a task in global space, robotic vision, and offline programming. 

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
  • In-depth understanding of specialist bodies of knowledge within the engineering discipline.
  • Application of established engineering methods to complex engineering solving
  • Fluent application of engineering techniques, tools and resources.
  • Application of systematic engineering synthesis and design processes


Course Learning Outcomes (CLOs)

On completion of this course you should be able to:

  1. Design multi-jointed serially linked manipulators:
  2. Identify intermediate arm matrices describing individual links:
  3. Determine the joint angle equations to attain a global position and attitude of the end effector:
  4. Determine how to identify velocity profiles of individual joints to achieve a desired global spatial trajectory.
  5. Relate driving currents and torques needed to control this trajectory for electrically-driven robots.
  6. Develop creative and innovative solutions to an advanced robotics problem and anticipate the financial and social consequences of any intended action. 
  7. Simulate the motion of articulated objects using high level mathematical skills. 
  8. Understand how robots identify objects and determine the position of the objects from camera images. 

Overview of Learning Activities

Learning activities can include lectures, lecture-tutorials or lectorials, quizzes, major and minor assignments and a final exam.

It is vital that you keep up-to-date with all learning activities. Details of assignments will be posted via LMS (Canvas) or via email. There will also be milestones for your project which must be achieved by certain dates. Tutorial activities have been designed to enable you to achieve these milestones within the appropriate time frame.

This course is designed to use your present knowledge of robotics and elevate it to in-depth technical knowledge. Assessments have been designed to for you to apply established engineering methods to highly complex engineering solving.

The first and third assessments will test your written communication skills.

Overview of Learning Resources

Course-related schedules and resources will be provided to students via on the course LMS (Canvas) or email. This can include supplementary course notes, tutorial material, lecture material, problem sheets and solutions.

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 item:
Robot Cell Design Assignment
Weighting of final grade: 20%
Related course learning outcomes: 1, 6
Description: You will be required to design a robotic workcell to accomplish a given automation task. This will include providing some conceptual designs, performing some comparisons, choosing the final design, and doing some cost calculations.


Assessment item: Offline Programming Assignment
Weighting of final grade: 20%
Related course learning outcomes: 7
Description: You will program a robot to carry out a given automation task using an offline programming software. You will then take a video of the robot motion as well as generate the robotic code.


Assessment item: Robot Vision Assignment
Weighting of final grade: 20%
Related course learning outcomes: 8
Description: You will be required to write codes and algorithms in MATLAB to accomplish some robotic vision tasks, such as  identifying the position of an object, or recognize the features of an object.


Assessment item: Exam
Weighting of final grade: 40%
Related course learning outcomes: 1, 2, 3, 4, 5. 8
Description: The final semester exam will test your ability to analyse an articulated robot, make the appropriate modelling as well as your ability to interpret robot models.