Outline

Overview

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.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	MTRX5700
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	Yes

Teaching staff

Coordinator	Guodong Shi, guodong.shi@sydney.edu.au
Lecturer(s)	Ian Manchester, ian.manchester@sydney.edu.au
	Mitch Bryson, mitch.bryson@sydney.edu.au
	Tejaswi Sundara Digumarti, tejaswi.digumarti@sydney.edu.au
	Donald Dansereau, donald.dansereau@sydney.edu.au
	Viorela Ila, viorela.ila@sydney.edu.au

Type	Description	Weight	Due	Length
Participation	Student Lectures	10%	Multiple weeks	N/A.
Outcomes assessed:
Assignment	Problems and Practices	30%	Multiple weeks	N/A.
Outcomes assessed:
Presentation	Research Project Pitch	10%	Week 07	N/A.
Outcomes assessed:
Presentation	Research Project Final Presentation	20%	Week 13	N/A.
Outcomes assessed:
Assignment	Research Project Report A 2/3-page professional research proposal.	30%	Week 13	N/A.
Outcomes assessed:
= group assignment ?

Assessment summary

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

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.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

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

Disclaimer

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

MTRX8700: Experimental Robotics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect