Autonomous Robotics
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BE3M33ARO1 | Z,ZK | 6 | 2P+2L | English |
- Relations:
- During a review of study plans, the course B3M33ARO1 can be substituted for the course BE3M33ARO1.
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course B3M33ARO1 (mutually exclusive courses).
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course BE3M33ARO (mutually exclusive courses).
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course B3M33ARO (mutually exclusive courses).
- During a review of study plans, the course BE3M33ARO can be substituted for the course BE3M33ARO1.
- During a review of study plans, the course B3M33ARO can be substituted for the course BE3M33ARO1.
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course B3M33ARO1 (mutually exclusive courses).
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course BE3M33ARO (mutually exclusive courses).
- It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course B3M33ARO (mutually exclusive courses).
- Course guarantor:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Cybernetics
- Synopsis:
-
The Autonomous robotics course will explain the principles needed to develop algorithms for intelligent mobile robots such as algorithms for:
(1) Mapping and localization (SLAM) sensors calibration (lidar or camera).
(2) Planning the path in the existing map or planning the exploration in a partially unknown map and performing the plan in the world.
IMPORTANT: It is assumed that students of this course have a working knowledge of optimization (Gauss-Newton method, Levenberg Marquardt method, full Newton method), mathematical analysis (gradient, Jacobian, Hessian), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms.
This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.
- Requirements:
-
It is assumed that students of this course have a working knowledge of optimization (Gauss-Newton method, Levenberg Marquardt method, full Newton method), mathematical analysis (gradient, Jacobian, Hessian, multidimensional Taylor polynomial), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms.
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
- Study materials:
-
1. Siciliano, Bruno and Sciavicco, Lorenzo and Villani, Luigi and Oriolo, Giuseppe: Robotics, Modelling,
Planning and Control, Springer 2009
2. Fahimi, F.: Autonomous Robots: Modeling, Path Planning, and Control, Springer 2009
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/aro
- No time-table has been prepared for this course
- The course is a part of the following study plans:
-
- Aerospace Engineering - study branch Avionics (compulsory elective course)
- Cybernetics and Robotics (compulsory course in the program)