Course - Autonomous Systems - AIS2205
Autonomous Systems
New from the academic year 2026/2027
About
About the course
Course content
The course gives an introduction to autonomous systems from an automation and cybernetics perspective. A selection of the following topics will be included:
- Introduction to autonomous systems
- Applications, including autonomous ships and unmanned robotics platforms (UXVs)
- State estimation for autonomous vehicles, including GNSS-aided inertial navigation
- Situational awareness in unstructured environments
- Path planning and collision avoidance algorithms
- Control systems for autonomous vehicles, including cascaded PID and model predictive control (MPC)
- Software frameworks for autonomous systems, including ArduPilot, PX4, ROS2
- Modeling and simulation of autonomous systems, system identification and digital twins
- Artificial intelligence (AI) for autonomy
- Operations, regulations and assurance of autonomous systems
- Possibly other relevant topics
More details on the curriculum will be provided at the start of the semester.
Learning outcome
Knowledge
- The candidate can explain and compare theory, principles, applications, strengths and weaknesses of methods presented in the course
Skills
- The candidate can demonstrate the use of methods presented in the course, both through digital tools and simulation
General competence
- The candidate can use digital tools for implementation of autonomous systems
- The candidate can explain the value of autonomous systems for sustainable processes, services, or systems
- The candidate can present problems and relevant solution methods in a professional and scientific manner
- The candidate can discuss ethical challenges of autonomy
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
- Approval of selected exercises
Further on evaluation
The final grade is based on an overall evaluation of the portfolio, which consists of a number of works delivered through the semester. The portfolio contains assignments that are carried out, digitally documented and submitted during the term. 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. Compulsory assignments: A selection of learning exercises must be approved. The re-sit exam is an oral exam the following spring.
Specific conditions
Admission to a programme of study is required:
Automation and Intelligent Systems - Engineering (BIAIS)
Civil Engineering - Engineering (BIBYGG)
Computer Science - Engineering (BIDATA)
Mechatronics and Product Design - Engineering (BIMEPRO)
Naval Architecture - Engineering (699SD)
Renewable Energy - Engineering (BIFOREN)
Recommended previous knowledge
- AIS2002 Reguleringsteknikk
- AIS2102 Dynamiske systemer
- AIS2105 Mekatronikk og robotikk
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. Permission to take the course can be allowed for engineering students at NTNU or from abroad (exchange students) at the discretion of the academic director of the study programme.
Course materials
An updated course overview, including curriculum, is presented at the start of the semester and will typically also include English material.
Subject areas
- Marine Cybernetics
- Engineering Cybernetics
- Engineering