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.

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.