Subject description
Programming Autonomous Systems introduces students to the foundation of intelligent autonomous agents combined with a number of challenging hands-on applications. The subject will start with an introduction to the field of mobile robots. At its core the subject will address the problems of localisation, planning and control, perception, robot motion … For more content click the Read More button below.
Enrolment rules
Pre-Requisite
Tutorial enrolment
Students can enrol online via the Tutorial Enrolment link in SOLS
Delivery
To view information specific to your campus, click on Select availability in the top right of screen and choose from the campus, delivery mode and session options.
Teaching staff
Subject coordinators
Engagement hours
Contact Hours:2 hr lecture, 3 hr practical & 3 hr workshop
Learning outcomes
On successful completion of this subject, students will be able to:
1.
Demonstrate robust practical experience with both software and hardware details of one robot architecture.
2.
Evaluate alternative architectures and decision making systems utilised with autonomous systems.
3.
Demonstrate experiential understanding of the practicalities of programming physical robots in contrast to purely simulated software systems.
4.
Demonstrate an extensive understanding of robot autonomy as a complete system, as well as its component entities.
5.
Apply leadership and independent self-directed practice within a collaborative team and be capable of demonstrating entrepreneurship & innovation, with respect to programming autonomous mobile robots.
Assessment details
Laboratory Participation Quizzes
Theory Quizzes
Laboratory Demonstration 1
Laboratory Demonstration 2
Laboratory Demonstration 3
Final Exam
Work integrated learning
Embedded WIL:This subject contains elements of "Embedded WIL". Students in this subject will experience activities that relate to or simulate professional practice as part of their learning.
Textbook information
There is no set text for this course. However, the following books are recommended:
TurtleBot3 Online Manual, Robotis
ROS Robot Programming, YoonSeok Pyo, HanCheol Cho, RyuWoon Jung, TaeHoon Lim
Artificial Intelligence: A Modern Approach, S. Russell and P. Norvig
Probabilistic Robotics, S. Thrun, W. Burgard and D. Fox
Automated Planning: Theory and Practice, M. Ghallab, D. Nau and P. Traverso
Reinforcement Learning: An Introduction, R. Sutton and A. Barto
Machine Learning, T. Mitchell