• Offered by School of Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Classification Advanced
  • Course subject Engineering
  • Areas of interest Engineering
  • Academic career PGRD
  • Course convener
    • Prof Hongdong Li
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2022
    See Future Offerings

This course provides an introduction to modern robotics. The focus is on robot kinematics, sensing techniques, localization, navigation, mapping and planning. Topics to be covered include robot spatial configuration, homogeneous coordinate transformation, mobile robot locomotion, mobile robot kinematics, robot motion control, sensors and perception, navigation and path planning, robot localization, simultaneous localization and mapping SLAM, robotic system architecture.


The applied component of the course includes experimental work with a programmable mobile robotic platform equipped with sensors. The project aims at integrating sensor measurements to build a representation of the environment and perform a robotic task.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Discuss the history, concepts and key components of robotics technologies.
  2. Describe and compare various robot sensors and their perception principles that enable a robot to analyse their environment, reason and take appropriate actions toward the given goal.
  3. Analyse and solve problems in spatial coordinate representation and spatial transformation, robot locomotion, kinematics, motion control, localization and mapping, navigation and path planning.
  4. Apply and demonstrate the learned knowledge and skills in practical robotics applications.
  5. Critically appraise current research literature and conduct experiments with state of the art robotic algorithms on a robotic platform.
  6. Effectively communicate engineering concepts and design decisions using a range of media.

Other Information

Professional Skills Mapping:

Mapping of Learning Outcomes to Assessment and Professional Competencies

Indicative Assessment

  1. Practical Labs (40) [LO 1,2,3,4,5,6]
  2. Final Project (30) [LO 1,2,3,4,5,6]
  3. Final Exam (30) [LO 1,2,3,4,5,6]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

3 x 1 hour lecture. 1 hour tutorial, 3 hours lab time, 2 hours independent study time - per week

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must be studying Master of Engineering or Master of Machine Learning and Computer Vision. Incompatible with ENGN4627.

Prescribed Texts

M. Spong, S. Hutchinson and M. Vidyasager, Robot Modelling and Control, Wiley, 2006.

Fees

Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
2
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2022 $4740
International fee paying students
Year Fee
2022 $6000
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
5326 25 Jul 2022 01 Aug 2022 31 Aug 2022 28 Oct 2022 In Person N/A

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