Course content

How can robots see, recognise, respond to, and learn from their environment?

This course contains a selection of topics related to robot vision and artificial intelligence, for example:

• fundamental image analysis
• fundamental 3D modelling
• software and digital tools for computer/machine/robot vision
• object recognition and tracking
• optical flow estimation
• image manipulation
• probabilities, statistics, regression and classification
• fundamental machine learning
• artificial neural networks
• possibly other relevant topics

More information about the curriculum will be made available at the start of the semester.

Learning outcome

Competence

Upon completion of the course, the candidate can

• use digital and physical tools for implementing practical solutions within robot vision and artificial intelligens.
• discuss aspects of robot vision and artificial intelligent with respect to ethics and sustainability.
• present challenges, solution methods, and results in a professional and proximally scientific manner.

Knowledge and skills

Upon completion of the course, the candidate can

• explain and compare theory, functionality, strengths, and weaknesses of methods presented in the course.
• demonstrate use of methods presented in the course, both through digital tools, simulation, and physical implementation.

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.

Compulsory assignments

• Mandatory learning activities

Further on evaluation

The final grade is based on an overall evaluation of the portfolio, which consists of work that is carried out, documented and digitally submitted during the term. Such submissions may include some of the following:

• software
• assignments
• technical reports
• essays
• reflection notes
• video submissions, e.g. demonstration of work or tests of knowledge
• possibly other kinds of submissions.

Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio.

The re-sit exam is an oral exam in August.

Note that the course also has some compulsory activities that must be approved in order for the portfolio to be assessed.

More information will be provided at the start of the semester.

Specific conditions

Recommended previous knowledge

It is recommended that students have good knowledge in engineering mathematics, statistics, programming, and mechatronics/electronics and hardware, for example from having completed the following courses (or similar):

• IMAA1002 Matematikk for ingeniørfag 1
• IMAA2012 Matematikk for ingeniørfag 2
• ISTAA1002 Statistikk
• AIS1003 Objektorientert programmering for kyberfysiske systemer
• AIS1104 Automatisering og mekatronikk med prosjekt

Required previous knowledge

The course has no prerequisites. It is a requirement that students are enrolled in the study programme to which the course belongs.

Course materials

An updated course overview, including curriculum, is presented at the start of the semester and may also include English material.