Part A: Course Overview

Course Title: Programming Autonomous Robots

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2781

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2021

COSC2781

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 1 2022,
Sem 1 2023,
Sem 1 2024,
Sem 1 2025

COSC3011

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet1 2024,
Viet1 2025

Course Coordinator: Dr Timothy Wiley

Course Coordinator Phone: +61 3 9925 5202

Course Coordinator Email: timothy.wiley@rmit.edu.au

Course Coordinator Location: 14.11.13

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses 

Successful completion of the following course/s: 

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course. 

For information go to RMIT Course Requisites webpage. 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer. Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning. Please follow the link for further information on how to apply for credit for prior study or experience.


Course Description

Software for robots face unique challenges, especially semi or fully automated robotic systems. This software must handle the limited computation power of robots along with the uncertainty and noise produced by their sensors and actuators. Robotic software must integrate across algorithms at multiple levels of abstraction, from the low-level information of the sensor’s, to high-level reasoning. This course focuses on the design and development of the software modules and architectures for autonomous robotic systems, including reactive actuator control, localisation, mapping, vision and audio processing, and task planning. You will complete practical work both in simulation and on real-world robot platforms.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes 

This course contributes to the program learning outcomes for the following program(s):  

MC271 – Master of Artificial Intelligence:

PLO 1: Enabling Knowledge

  • Demonstrate mastery of a body of knowledge that includes recent developments in Artificial Intelligence, Computer Science and information technology;
  • Understand and use appropriate and relevant, fundamental and applied AI knowledge, methodologies and modern computational tools;
  • Recognise and use research principles and methods applicable to Artificial Intelligence.

PLO 2: Critical Analysis

  • Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
  • Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;
  • Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of AI problems.

PLO 3: Problem Solving

  • Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
  • Apply an understanding of the balance between the complexity / accuracy of the Artificial techniques used and the timeliness of the delivery of the solution.

PLO 4: Communication

  • Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

PLO 5: Team Work

  • Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.


Upon successful completion of this course you should be able to:

  1. Discuss and Critically Analyse and a variety of software architectures and algorithms for solving typical problems in the context of autonomous robot systems; Discuss and Critically Analyse the strengths and limitations of these architectures and algorithms.
  2. Discuss and Critically Analyse the challenges of designing and developing software for a variety of robot systems of different complexities, including noise, uncertainty, and computational power.
  3. Research, Discuss, and Use new and novel algorithms for solving problems with autonomous robot systems.
  4. Use pre-existing robot software to solve common problems on simulated and real-world robots; Develop and Implemented new algorithms and software for solving problems on simulated and real-world robots; Integrate this software in the ROS framework.
  5. Develop skills for further self-directed learning in the general context of software, algorithms, and architectures for autonomous robot systems; Adapt experience and knowledge to and from other computer sciences contexts such as artificial intelligence, machine learning, and software design.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course. 


Overview of Learning Resources

You will make extensive use of computer laboratories, and relevant robot hardware and software provided by the School. You will work in the Artificial Intelligence Innovation Lab and be required to adhere to relevant health and safety requirements. You will be able to access course information and learning materials through MyRMIT and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Group assessment
Weighting: 25%
This task supports CLOs: 1, 2, 4

Assessment Task 2: Major project and Group assessment
Weighting: 50%

This task supports CLOs: 1, 2, 3, 4, 5

Assessment Task 3: Research Presentation
Weighting: 25%
This task supports CLOs: 1, 2, 3, 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.