Examination arrangement

Course content

The course contains a selection of topics, with an emphasis on applications within robotics and intelligent systems:

• Industrial collaborative robots
• Mobile robots
• Robot programming, data structures and algorithms
• Modelling and control in state space, e.g., pole placement, observer, PID
• Optimization methods, e.g, gradient descent, Newton-Raphson's method
• Motion planning and inverse kinematics
• Artificial intelligence and intelligent systems, e.g., evolutionary algorithms, fuzzy systems, agent-based modelling
• Data analysis and machine learning
• IoT, sensors, actuators, and machine vision
• Virtual and physical prototyping
• Possibly other relevant topics

More details about the curriculum will provided during the start of semester.

Learning outcome

Knowledge and skills

Within the context of robotics and intelligent systems, the candidate can explain and use

• fundamental theory, methods, and components within automation and electrical engineering, including common mechatronic components, sensors, and actuators.
• programmable logic controls (PLCs) and industrial control systems, microcontrollers (e.g., Arduino), and microcomputers (e.g., Raspberry Pi) in a mechatronic system.
• industrial and collaborative robots, robot programming, and robot architectures.
• theory and methods within data communication, IoT, machine vision, and cyber-physical systems for collection, processing, storing, and sending of data.
• methods and principles within software engineering such as IDEs, version control, tools, libraries, testing, verification, and fault diagnosis.
• software and programming for data analysis, machine learning, and machine vision.
• methods, algorithms, and intelligent systems for automatic control of robots and mechatronic systems.
• methods and tools (e.g., Fusion 360, Matlab, Python) for design, simulation, and virtual prototyping.
• methods and tools for physical prototyping, for example additive (e.g., 3D printing) and subtractive methods (e.g., laser cutting), casting, and use of semi-finished products.
• relevant electronics and mechanical tools in a lab.

Competence

With respect to robotics and intelligent systems, the candidate can

• identify and analyse complex problems and challenges.
• explain, evaluate, compare, and use methods and tools for design, implementation, and testing of sustainable solutions.
• gather, critically evaluate, and take advantage of technical, innovation-based, and research-based information, as for other enabling technologies.
• identify potential for innovation and develop solution proposals.
• identify and explain societal challenges and potential solutions, as well as possible consequences and future outcome.
• reflect on norms for ethics and sustainability at an individual, societal, and global level, and their relevance.
• work individually and goal-oriented, take initiative and interact well in a team, and show leadership in project and development work.
• on a suitable level present to, and discuss with, a variety of relevant stakeholders.
• demonstrate ability in, and willingness for, lifelong learning.

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials, assignments and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory. The learning activities are adapted to the practical work taking place at any time, and may touch upon the concept of "delayed instruction." The course aims to have synergies with respect to the learning activities in the other courses that run in parallel, in particular AIS4001 Cybernetics and Robotics (autumn semester) and AIS4002 Intelligent Machines (spring semester).

During the study year, there might be events such as excursions to relevant industry, seminars and conferences, and guest lectures and coursework provided from companies representatives.

Compulsory assignments

• Compulsory 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
• 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 course.

Specific conditions

Recommended previous knowledge

• MMA4001 Fundamentals of Automation & Mechatronics Engineering
• AIS4001 Cybernetics & Robotics (before the second semester)

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.