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Special Topic
This course covers the fundamentals and state-of-the-art of robotics and intelligent systems and will prepare students for more advanced robotics courses. By developing software applications for robots, which are usually concurrent, real-time and distributed systems, students will learn to follow state-of-the art software architectural practices for complex systems. Through collaborative design and development projects, students will gain skills required to work in large scale engineering and/or software projects.
After successful completion of this course, students will be able to:demonstrate the knowledge and understanding of the components used for mobile robot designdemonstrate the knowledge and understanding of the theories, techniques, mathematical models and methods involved in mobile robot motion plan and program mobile robot actions by applying a range of artificial intelligence techniques and control paradigmsdescribe and analyse the general purpose component and middleware technologies that support many of the fundamental architectural patterns used in robots and other similar complex systemsdemonstrate the knowledge and understanding of Human-Robot Interaction models used for robot designwork in project teams to develop software to meet changing requirements for a complex application
Approval by the Head of Department
Students must attend one activity from each section.
Prof. Pradeep Abeygunawardhana
Review of selected papers and presentation (15%): You will be assigned some papers to read, and will need to write a short review (one paragraph) and email it to the lecturer at least one hour before the lecture. Your review may contain questions about the paper, or a description of the most important point of the paper. You will also participate in class discussion on these papers. You will present one paper to the class.No assignments will be accepted after the drop dead date (i.e. a week after the assignment is due). The penalty for the late submission of an assignment will be an absolute deduction of 15% of the maximum possible mark. In order to pass a course you must meet two requirements:a) The university has adopted a common scale for converting marks to grades. According to this scale, an average mark of 50% is sufficient to pass the course (i.e. to achieve a C-), with an average mark of 55% a C grade is achieved and so forth. We apply this conversion scale to the average marks students achieve over all assessment items.b) You must achieve an average mark of at least 45% on invigilated assessment items.Marks are sometimes scaled to achieve consistency between courses from year to year.
Notices about this course will be posted to the course forum in the Learn system (learn.canterbury.ac.nz). Enrolled students will also be members of a class called "CSSE Notices", where general notices will be posted that apply to all classes (such as information about building access or job opportunities).There are several important documents available online about departmental regulations, policies and guidelines at the following site. We expect all students to be familiar with these. https://www.canterbury.ac.nz/study/academic-study/engineering/schools-and-departments-engineering-forestry-product-design/computer-science-and-software-engineering-department/additional-resources/resources-for-students/policies
1 Introduction to Robotics2 Locomotion Concepts / Degrees of Freedom / Leged vs Wheel Robots’ locomotion3 Mobile Robot Kinematics • Representing Position and Orientation • Mapping between the World Frame and the Robot Frame • Orthogonal Rotation Matrix • Kinematic Model of a Differential Drive Robot4 Localization • Localization and Map Building • Challenges of Localization • Sensor Noise • Sensor Aliasing • Odometry and Dead Reckoning • Deterministic and Non-Deterministic Errors • Model Based Navigation vs Behavior Based Navigation • Belief Representation • Representation of the Environment • Types of Map Representations5 Sensors and Actuators in Robotics • Classification of Sensors • Basic Sensor Response Ratings • Sensor Performance • Types of Sensors6 Planning and Control • Methods of Controlling Robots • Artificial Intelligence Techniques in Robotics • Hierarchical Paradigm • Reactive Paradigm • Subsumption ArchitectureSemester break 7 Perception • Features • Environmental Modelling • Representing Uncertainty • Feature Extraction Based on Range Data: Line Extraction • Range Histogram Features • Vision Based Feature Extraction8 Fuzzy Control • Introduction to Fuzzy Logic and Fuzzy Sets • Fuzzy Linguistic Variables • Membership Functions • Fuzzy Control9 Human-Robot Interaction • Cognitive HRI • Human Models of Interaction • Mental Models of Robots • Mental Models in Robot Design • Social Cognition • Minimal and Human Like Cues • Embodied Social Cues • Models for Human-Robot Interaction • Social Robotics10 Software Architectures in Robotics • Software Engineering in Robotics • Component based Software Development • Distributed Middleware • Robot Operating System (ROS)11 Program a Mobile Robot to Perform a given Complex Task, which requires the application of Control Paradigms and Techniques Learned in this Course12 Presentations, Course review
Domestic fee $1,247.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Computer Science and Software Engineering .