CTU FEE Moodle
Autonomous Robotics
B232 - Summer 23/24
Autonomous Robotics - B3M33ARO
| Credits | 7 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 3P+2L |
Annotation
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.
(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.
Study targets
No data.
Course outlines
https://cw.fel.cvut.cz/wiki/courses/aro/lectures/start
Exercises outlines
https://cw.fel.cvut.cz/b212/courses/aro/tutorials/start
Literature
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
Planning and Control, Springer 2009
2. Fahimi, F.: Autonomous Robots: Modeling, Path Planning, and Control, Springer 2009
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.