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. Course 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 ?	(AMME3500 OR AMME9501 OR AMME8501) AND (MTRX3700 OR MTRX3760)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Knowledge of statics and dynamics, rotation matrices, programming and some electronic and mechanical design experience is assumed.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Viorela Ila, viorela.ila@sydney.edu.au
Lecturer(s)	Tejaswi Sundara Digumarti, tejaswi.digumarti@sydney.edu.au
	Viorela Ila, viorela.ila@sydney.edu.au
	Stefan Williams, stefan.williams@sydney.edu.au
Tutor(s)	Stephany Berrio Perez, stephany.berrioperez@sydney.edu.au
Tutor(s)	Jing Cheng, jing.cheng@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam The exam will include essay type questions related to course material.	30%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Assessment will include in-class demonstrations and a report.	5%	Week 03	n/a
Outcomes assessed:
Assignment	Assignment 2 Assessment will include in-class demonstrations and a report.	10%	Week 06	n/a
Outcomes assessed:
Assignment	Assignment 3 Assessment will include in-class demonstrations and a report.	15%	Week 09	n/a
Outcomes assessed:
Assignment	Major project Assessment will include a presentation to peers (50%) and report (50%).	40%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type D final exam ?

Assessment summary

Assignments: Tutorials 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	Work of exceptional standard. Work demonstrates initiative and ingenuity in research, pointed and critical analysis of material, thoroughness of design, and innovative interpretation of evidence. Demonstrates a comprehensive understanding of the unit material and its relevance in a wider context.
Distinction	75 - 84	Work of superior standard. Work demonstrates initiative in research and reading, complex understanding and original analysis of subject matter and its context, both empirical and theoretical; shows critical understanding of the principles and values underlying the unit of study. In particular, students who aim for a Distinction and higher will have to accomplish the requirements of a Credit and should be able to • Generalise algorithms to more complicated systems not dealt with explicitly in class. • Demonstration of independent research and in-depth understanding of material beyond the scope of the lecture material. • Synthesise and analyse various components of a robotic system using a systems engineering approach in order to develop and demonstrate a working system.
Credit	65 - 74	Competent work. Evidence of extensive reading and initiative in research, sound grasp of subject matter and appreciation of key issues and context. Engages critically and creatively with the question and attempts an analytical evaluation of material. Goes beyond solving of simple problems to seeing how material in different parts of the unit of study relate to each other by solving problems drawing on concepts and ideas from other parts of the unit of study. In particular, students who aim for a Credit will have to accomplish the requirements of a Pass and should be able to: • Relate between the various components of the course and understand their interaction in terms of integration of robotic systems. • Manipulate kinematic and dynamic equations related to manipulator and mobile robotic systems. • Implement and demonstrate advanced robotic concepts as described in theoretical works studied as part of the course
Pass	50 - 64	Work of acceptable standard. Work meets basic requirements in terms of reading and research and demonstrates a reasonable understanding of subject matter. Able to solve relatively simple problems involving direct application of particular components of the unit of study. In particular, students who aim for a Pass should be able to: • Identify the various components of a robotic system. • Use simple equations for problem solving in robotic systems.
Fail	0 - 49	Work not of acceptable standard. Work may fail for any or all of the following reasons: unacceptable level of paraphrasing; irrelevance of content; presentation, grammar or structure so sloppy it cannot be understood; submitted very late without extension; not meeting the University’s values with regards to academic honesty.

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	Introduction to robotics	Lecture (2 hr)
Week 02	Robot kinematics and dynamics	Lecture (2 hr)
Week 02	Kinematics and dynamics	Tutorial (3 hr)
Week 03	Sensors, measurements and perception	Lecture (2 hr)
Week 03	Kinematics and dynamics	Tutorial (3 hr)
Week 04	Robot vision and vision processing	Lecture (2 hr)
Week 04	Perception	Tutorial (3 hr)
Week 05	Robot learning	Lecture (2 hr)
Week 05	Perception	Tutorial (3 hr)
Week 06	Localisation and navigation	Lecture (2 hr)
Week 06	Perception demonstration	Tutorial (3 hr)
Week 07	Estimation and data fusion	Lecture (2 hr)
Week 07	Robot navigation	Tutorial (3 hr)
Week 08	Estimation and SLAM	Lecture (2 hr)
Week 08	Robot navigation	Tutorial (3 hr)
Week 09	Robot navigation	Tutorial (2 hr)
Week 09	Robot navigation - Demonstration	Tutorial (3 hr)
Week 10	Major project - Pitch	Tutorial (2 hr)
Week 10	Major project	Tutorial (3 hr)
Week 11	Obstacle avoidance and path planning	Lecture (2 hr)
Week 11	Major project	Tutorial (3 hr)
Week 12	Robotic architectures	Lecture (2 hr)
Week 12	Major project	Tutorial (3 hr)
Week 13	Case study	Tutorial (2 hr)
Week 13	Major project	Tutorial (3 hr)
Weekly	Individual study of material related to lectures, tutorials and assignments.	Independent study (6 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 their 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 the students a broad exposure to robotic systems both in 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.

Required readings

There is no prescribed text for this course. Recommended reading and references will be provided in relation to assignments. You may also wish to consult the following texts to help you in your understanding of the material related to this course.

Manipulator Kinematics and Dynamics

John J. Craig, Introduction to Robotics: Mechanics and Control, 3rd Edition, Prentice-Hall, 2003
Lorenzo Sciavicco, Bruno Siciliano, Modeling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), Springer 2000
Mark W. Spong, M. Vidyasagar, Robot Dynamics and Control, Wiley, 1989

Computer Vision

Ballard and Brown, Computer Vision, Prentice Hall, 1982
David A. Forsyth and Jean Ponce, Computer Vision -- A Modern Approach, Prentice Hall, 2002
Machine Learning
Tom Mitchell, Machine Learning, McGraw-Hill, 1997
Stuart J. Russell and Peter Norvig, Artificial Intelligence, A Modern Approach, 2nd Edition, Prentice Hall, 2002

Mobile Robotics

Sebastian Thrun, Dieter Fox and Wolfram Burgard, Probabilistic Robotics, The MIT Press, 2005
Greg Dudek and Michael Jenkin, Computational Principles of Mobile Robotics, Cambridge University Press, 2000
Roland Siegwart and Illah R. Nourbakhsh, Roland Siegwart, Illah R Nourbakhsh, Davide Scaramuzza, Introduction to Autonomous Mobile Robots, The MIT Press, 2011

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.

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 examining techniques for processing this data
LO4. implement navigation, sensing and control algorithms on a practical robotic system
LO5. understand conventions used in robot kinematics and dynamics
LO6. express ideas both orally and written on technical material
LO7. develop the capacity to think creatively and independently about new design problems
LO8. undertake independent research and analysis and to think 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

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.2. Information literacy and the ability to manage information and documentation. 3.3. Creativity and innovation. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 3.3. Creativity and innovation. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements. 4.5. An ability to undertake problem solving, design and project work within a broad contextual framework accommodating social, cultural, ethical, legal, political, economic and environmental responsibilities as well as within the principles of sustainable development and health and safety imperatives. 5.7. Proficiency in appropriate laboratory procedures; the use of test rigs, instrumentation and test equipment. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.2. Information literacy and the ability to manage information and documentation. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.3. Creativity and innovation. 3.7. A capacity for lifelong learning and professional development and appropriate professional attitudes. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.6. Skills in the design and conduct of experiments and measurements. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 3.2. Information literacy and the ability to manage information and documentation. 3.7. A capacity for lifelong learning and professional development and appropriate professional attitudes. 5.1. An appreciation of the scientific method, the need for rigour and a sound theoretical basis. 5.6. Skills in the design and conduct of experiments and measurements. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.

Engineers Australia Curriculum Performance Indicators -

Competency code	Taught, Practiced or Assessed	Competency standard
1.1		Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice.
1.2		Tackling technically challenging problems from first principles.
2.2		Application of enabling skills and knowledge to problem solution in these technical domains.
2.3		Meaningful engagement with current technical and professional practices and issues in the designated field.
2.4		Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity.
3.1		An ability to communicate with the engineering team and the community at large.
3.2		Information literacy and the ability to manage information and documentation.
3.3		Creativity and innovation.
4.1		Advanced level skills in the structured solution of complex and often ill defined problems.
4.2		Ability to use a systems approach to complex problems, and to design and operational performance.
4.4		Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements.
5.1		An appreciation of the scientific method, the need for rigour and a sound theoretical basis.
5.2		A commitment to safe and sustainable practices.
5.3		Skills in the selection and characterisation of engineering systems, devices, components and materials.
5.4		Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;.
5.5		Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
5.6		Skills in the design and conduct of experiments and measurements.
5.7		Proficiency in appropriate laboratory procedures; the use of test rigs, instrumentation and test equipment.
5.8		Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
5.9		Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.

Responding to student feedback

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

Overall, feedback has been very positive concerning this Unit of Study. We have shifted some content related to Robotic Learning to earlier in the course to provide students with an earlier introduction to this material to inform later assignments.

Additional information

Work, health and safety

All students must be inducted to work in the Mechatronics Lab prior to undertaking work during tutorial sessions.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

MTRX5700: 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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect