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.
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.
The prerequisites of the class are essentially mathematical. They are described in a PDF document available to students on Aulaweb.
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.
Ricevimento: Teacher is not in Genoa. Please contact him per email.
GIORGIO CANNATA (President)
GAETAN GARCIA
ENRICO SIMETTI (President Substitute)
https://corsi.unige.it/10635/p/studenti-orario
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.
Final exam 100%, but as stated above, the exam may contain questions to evaluate the understanding of the labs.
Teacher’s email address: gaetan.garcia@ec-nantes.fr