AIMS AND CONTENT

LEARNING OUTCOMES

The class first develops the kinematic modeling and motorization of mobile robots, illustrated by the full study of the differential drive robot. Then localization based on the Extended Kalman Filter is addressed, is illustrated by a lab which uses real data and presents a tuning methodology. Observability issues are also addressed, with practical examples. Planning methods applicable to mobile robots are studied, in particular potential field methods and the Rapidly exploring Random Tree. Control then focuses on direct applications to mobile robots: static and dynamic feedback control and Lyapunov based control, illustrated on the case of the differential drive robot.

AIMS AND LEARNING OUTCOMES

The students will be able to tackle real problems in mobile robotics. In kinematic modeling and in localization, their experience will involve practical knowledge and, in localization, hands on experience with real data.

PREREQUISITES

The prerequisites of the class are essentially mathematical. They are described in a PDF document available to students on Aulaweb.

TEACHING METHODS

The class is delivered in part online. Approximately half of the class is taught in presence over a two week period. This part is mainly devoted to exercises and labs.

A list of exercises is provided to the students, who can work on the exercises that they find the more challenging, under the guidance of the teacher.

SYLLABUS/CONTENT

• Modeling of wheeled Robots: Constraint equations, Classification of robots using degrees of mobility and steerability, Posture kinematic model, Configuration kinematic model, Motorisation of wheels.
• Localization: Relative localization using odometry, Absolute localization, Localization sensors, Localization using extended Kalman filtering.
• Control: Controllability and stabilization, static and dynamic feedback linearization, nonlinear control based on Lyapunov functions.
• Practical Work: The students will study various control laws in simulation. They will also implement a Kalman filter-based localization algorithm using data recorded with a real robot.

RECOMMENDED READING/BIBLIOGRAPHY

• “Theory of robot control”, Carlos Canudas de Wit, Bruno Siciliano, Georges Bastin, Springer Science & Business Media, 2012 - 392 pages.
• PDF documents provided by the teacher.

TEACHERS AND EXAM BOARD

Exam Board

GIORGIO CANNATA (President)

GAETAN GARCIA

ENRICO SIMETTI (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/10635/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam is based on exercises that remain fairly close to class examples and homework examples. The exam typically is a mix of questions that must be solved by calculation, and questions that must not. The exam may also contain questions that are meant to evaluate the knowledge gained from the labs.

ASSESSMENT METHODS

Final exam 100%, but as stated above, the exam may contain questions to evaluate the understanding of the labs.

FURTHER INFORMATION

Teacher’s email address: gaetan.garcia@ec-nantes.fr