Skip to main content

During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

MTRX8700: Experimental Robotics

This unit aims to present a broad overview of the technologies associated with industrial and mobile robots. Major topics covered are sensing, mapping, navigation and control of mobile robots and kinematics and control of industrial robots. The subject consists of a series of lectures on robot fundamentals and case studies on practical robot systems. Material covered in lectures is illustrated through experimental laboratory assignments. The objective of the course is to provide students with the essential skills necessary to be able to develop robotic systems for practical applications. At the end of this unit students will: be familiar with sensor technologies relevant to robotic systems; understand conventions used in robot kinematics and dynamics; understand the dynamics of mobile robotic systems and how they are modeled; have implemented navigation, sensing and control algorithms on a practical robotic system; apply a systematic approach to the design process for robotic systems; understand the practical application of robotic systems in manufacturing, automobile systems and assembly systems; develop the capacity to think critically and independently about new design problems; undertake independent research and analysis and to think creatively about engineering problems. Unit content will include: history and philosophy of robotics; hardware components and subsystems; robot kinematics and dynamics; sensors, measurements and perception; robotic architectures, multiple robot systems; localization, navigation and obstacle avoidance, robot planning; robot learning; robot vision and vision processing.


Academic unit Aerospace, Mechanical and Mechatronic
Unit code MTRX8700
Unit name Experimental Robotics
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Assumed knowledge

A demonstrated programming ability, familiarity with concepts in sensing and control systems and a background in either CS, Mechatronics or Electrical/Electronic Engineering.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Guodong SHI,
Type Description Weight Due Length
Participation Student Lectures
10% Multiple weeks N/A.
Outcomes assessed: LO2 LO6 LO7
Assignment Problems and Practices
30% Multiple weeks N/A.
Outcomes assessed: LO2 LO3 LO4 LO5
Presentation Research Project Pitch
10% Week 07 N/A.
Outcomes assessed: LO1 LO2 LO5 LO6 LO7 LO8
Presentation Research Project Final Presentation
20% Week 13 N/A.
Outcomes assessed: LO1 LO2 LO6 LO7 LO8
Assignment group assignment Research Project Report
A 2/3-page professional research proposal.
30% Week 13 N/A.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
group assignment = group assignment ?
  • Assignments: Labs will be conducted once a week. The use of laboratory work will allow students to apply their newfound knowledge of robotic systems to a variety of practical systems. The introductory labs are designed to familiarise students with the material required to prepare for the major laboratory project.
  • Major project and report: Students will be asked to present a demonstration of their major project to other students and staff. This will encourage them to produce a system of sufficient quality that they can demonstrate it to their peers. This will also provide the students with an opportunity to share their experiences with their classmates.
  • Final Exam: The final exam will test students’ understanding of the course material.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 History and philosophy of robotics (2 hr)  
Week 02 Robot kinematics and dynamics (2 hr)  
Kinematics and dynamics (3 hr)  
Week 03 Sensors, measurements and perception (2 hr)  
Kinematics and dynamics (3 hr)  
Week 04 Robot vision and vision processing (2 hr)  
Sensing (3 hr)  
Week 05 Localisation and navigation (2 hr)  
Sensing (3 hr)  
Week 06 Sensing (2 hr)  
Robot navigation (3 hr)  
Week 07 Estimation and data fusion (2 hr)  
Robot navigation (3 hr)  
Week 08 Obstacle avoidance and path planning (2 hr)  
Week 09 Navigation demonstration (3 hr)  
Major project (3 hr)  
Week 10 Robotic architectures (2 hr)  
Major project (3 hr)  
Week 11 Robot learning (2 hr)  
Major project (3 hr)  
Week 12 Case study (2 hr)  
Major project (3 hr)  
Week 13 Major project (2 hr)  
Major project (3 hr)  

Attendance and class requirements

  • Laboratory: Material covered in lectures is illustrated through experimental laboratory assignments. By applying the techniques they have learned, students will be given the opportunity to contextualise their learning. Application of the concepts will encourage a deeper approach to learning. Labs will be conducted once a week in the Mechatronics Lab.
  • Lecture: The series of lectures will cover robot fundamentals and case studies examining practical robot systems. Experts in the field will be invited to present guest lectures to give students a broad exposure to robotic systems in both research and industrial contexts.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. apply a systematic approach to the design process for robotic systems
  • LO2. examine advanced topics in robotics including obstacle avoidance, path planning, robot architectures, multi-robot systems and learning as applied to robotic systems
  • LO3. demonstrate familiarity with sensor technologies relevant to robotic systems, specifically working with laser and vision data and understanding techniques for processing these data
  • LO4. implement navigation, sensing and control algorithms on a practical robotic system
  • LO5. understand conventions used in robot kinematics and dynamics
  • LO6. clearly express technical ideas in both oral and written form
  • LO7. develop the capacity to think independently and creatively about design problems
  • LO8. undertake independent research and analysis, thinking creatively about engineering problems.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
MTRX8700 is offered as a training course for PhD students interested in robotics research for the first time in 2018.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.