This course introduces students to the broad
field of robotics predominately from an artificial intelligence perspective.
This course is the last course in the robot specialization. The course provides an introduction to robotics and starts by answering general questions such as what a robot is, their origins, types, and applications. It then introduces the key approaches to robot control and explores the pros and cons of each. The course finally covers the advanced topics of robot learning and bio-inspired robots in addition to current robot research topics at ITU. In addition to the theory, the course also has a significant practical, hands-on dimension that comprises introduction to relevant software tools (simulators and operating systems) and hands-on experiments with simulated and physical robots.
Robotics is interdisciplinary by nature and research topics range from fundamental modelling of robot physics over programming and design all the way to ethical and philosophical questions. Hence, students with highly diverse backgrounds are welcome on this course. However, the course as taught at ITU has its root in Computer Science and will be taught predominately from this perspective. Hence, it is required that the student has basic programming skills (e.g. obtained from an introduction to programming course). Although not required, it is also recommended and useful to have taken the sister course:"How to build almost anything" which largely covers how robots are built. The course also includes some machine learning hence knowledge in this area is also useful, but not mandatory.
Intended learning outcomes
After the course, the student should be able to:
- Describe what a robot is, their origin, types, and applications
- Describe, compare, and apply robot control stategies
- Apply robot software tools (simulation, operating systems)
- Apply bio-inspired solutions and machine learning appropriately in robotics
- Formulate and reflect on robot solutions to real-world problems
The learning activities will be tailored such that there is a suitable balance between obtaining knowledge, applying it, and reflecting on the knowledge. Hence, the learning activities changes between lectures, small exercises, guided introductions to tools and robots, and larger report to allow for deeper reflection of the learned material.
The students have to get two mandatory reports approved to attend the exam. The reports will be based on mini-projects made in small groups and will typically cover design, implementation, and experimentation with simulated or physical robots.
The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
The course literature is published in the course page in LearnIT.
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 15%
- Lectures: 15%
- Exercises: 15%
- Assignments: 15%
- Project work, supervision included: 35%
- Exam with preparation: 5%
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
The final project and the resulting exam report will be made in the context of a robot competition. The exam report will describe the group's robotic solution, document the developed robotic solution using experimetal data, and finally reflect on the solution based on the theoretical material of the course.
- Group size: 1- 4 students, preferably 3.
Individual exam : Individual student presentation followed by an individual dialogue. The student is examined while the rest of the group is outside the room.
Time and dateOrdinary Exam - submission Wed, 5 Jan 2022, 08:00 - 14:00
Ordinary Exam Wed, 19 Jan 2022, 09:00 - 21:00
Ordinary Exam Thu, 20 Jan 2022, 09:00 - 21:00