Autonomous Assistive Technologies

Garant předmětu:

Libor Přeučil

Lecturer:

Libor Přeučil

Tutor:

Miroslav Kulich

Supervisor:

Department of Natural Sciences

Synopsis:

The course deals with the elementary structure of robots, especially mobile ones, and their potential use in autonomous assistive and rehabilitation tasks. The course covers the principles of solving typical tasks that enable the realization and control of autonomous robot behavior. Sensor data acquisition and processing techniques will be introduced to address the generic task of autonomous robot navigation. The course addresses machine perception techniques of realistic environments to create internal world models, robot positioning, use of simultaneous localization and mapping techniques, and uncertainty handling. Robot trajectory planning techniques will also be demonstrated.

Requirements:

Form of verification of study results: oral examination.

As a standard, the course is taught in contact form and the course has lectures and exercises. In case the number of students is less than 5, the teaching can take place in the form of guided self-study with regular consultations. In this case, in addition to the examination, the student is required to produce a written study on the assigned topic.

For combined study:

Teaching takes the form of guided self-study with regular consultations. In addition to the examination, the student is required to prepare a written study on a given topic.

Syllabus of lectures:

Topics presented include:

1. Introduction, robotics in assistive technology and rehabilitation, control system architectures

2. Sensors for mobile robotics: camera, rangefinder, odometry and introduction to data preprocessing

3. Data fusion, knowledge representation and principles of building environmental models and their properties

4. Reliability grids, geometric maps, topological maps, symbolic models

5. Environmental models based on embodied environmental properties, robust image flags

6. Robot kinematics and control principles

7. Configuration space and planning

8. Trajectory planning - Potential field, Dijsktra, D*, BUG

9. Planning under uncertainty, probabilistic planning methods

10. Reduction of computational complexity of planning, hierarchical procedures, trajectory control

11. Taxonomy of the localization problem. Continuous localization methods.

12. Simultaneous localization and mapping of unknown environment

13. Robot navigation in human-oriented environment, global robot localization in known environment

14. Human-robot interaction issues, forms and constraints

Syllabus of tutorials:

Exercises will take the form of practical projects in which students will test the knowledge acquired in lectures.

Study Objective:

Study materials:

Thrun S., Burgard W., and Fox D.: Probabilistic Robotics. MIT Press, Cambridge, MA, 2005. ISBN-13: 978-0262201629

Kelly A.: Mobile Robotics: Mathematics, Models, and Methods. Cambridge University Press, 2013. ISBN-13: 978-1107031159

Recommended:

Borenstein, J., Everett, B., and Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd., Wellesley, MA, 1996. ISBN 1-56881-058-X.

Dudek, G., Jenkin, M.: Computation Principles of Mobile Robotics, Cambridge University Press, 2000. ISBN 0521560217.

Jaulin L. Mobile Robotics, 2nd edition. Wiley, 2019

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: