TEL310 Probabilistic Robotics

The course consists of lectures and exercises, problem-based learning from industry partners, and computer exercises, with the use of programs in the analysis and design of probabilistic algorithms. The course is complemented with laboratory work (lab exercises), a set of practical implementation instructions in simulation using Matlab/Simulink software and Python language, and fieldwork (if any) on real mobile robots using the ROS framework.

Lectures and Exercises: concepts are presented and problems analyzed.

Computer Exercises: presented concepts are practiced through tutorials and exercises.

Lab Exercises: presented concepts are tested on simulated and real robots.

• The teacher is available for consultation in the lecturing period and otherwise available by e-mail or appointment.
• Assistant teachers will guide and support students during exercises, practice classes and labs.
• Course room on Canvas.
• Licenses for the The Construct Platform will be provided at the start of the course.

• Thrun, S., Burgard, W. & Fox, D., "Probabilistic Robotics," The MIT Press, 2005.
• Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G. A., Burgard, W., Kavraki, L. E. & Thrun, S., "Principles of Robot Motion: Theory, Algorithms, and Implementations", The MIT Press, 2005.
• Video Classes: Youtube (Cyrill Stachniss), MATLAB Tech Talks.

• INF120 Programming and Data Processing, ECTS 10.
• TEL240 Control and Engineering and Automation, ECTS 10.
• TEL200 Introduction to Robotics, ECTS 10.
• TEL211 Robot Programming, ECTS 10.

K2 - specified use of AI:

In this course, students are encouraged to leverage Artificial Intelligence (AI) tools to enhance their coding skills. However, there are specific guidelines regarding the appropriate use of AI assistance.

Permitted Use of AI Tools:

• Debugging Assistance: AI may be used to identify and correct errors in code that the student has written.
• Code Optimization: Students can use AI tools to improve the efficiency and readability of their own code.
• Learning and Concept Clarification: AI-generated explanations of programming concepts, algorithms, and coding best practices are allowed.
• Documentation and Style Guidance: AI may be utilized to refine documentation, comments, and adherence to coding standards.
• Restructuring Support: Students can use AI to suggest improvements for restructuring existing code while ensuring it remains their original work.

Prohibited Use of AI Tools:

• Code Generation: AI must not be used to produce entire solutions or significant portions of a coding assignment.
• Automated Completion of Assignments: Students should not rely on AI tools to complete problems, projects, or coursework on their behalf.
• Plagiarism or Direct Copying: Any AI-generated content that is copied and submitted without meaningful modifications is not allowed.
• Bypassing Learning Objectives: The intent of this course is to develop programming skills; using AI in a way that circumvents the learning process is strictly prohibited.

Academic Integrity and Accountability:

• Disclosure: If AI tools are used for debugging or improving code, students must indicate how and where they utilized AI assistance in their submission.
• Originality: All submitted code must be the student’s own work, with AI acting as a support tool rather than a replacement for personal effort.
• Consequences of Misuse: Any violation of this policy may result in penalties, including reduced grades, assignment re-submission requirements, or disciplinary action according to the university’s academic integrity policies.

By adhering to these guidelines, students can take advantage of AI-driven learning while ensuring they develop strong programming skills independently. If there are any questions regarding the use of AI, students should consult those responsible for the course for clarification.

Descriptions of AI-category codes.