Artificial Intelligence and Robotics

GIUSEPPE ORIOLO

General objectives

The course presents the basic methods for achieving mobility and autonomy in robots.

Specific objectives

Knowledge and understanding:
Students will learn (1) basic methods for the modeling, analysis and control of mobile robots, both wheeled and legged, and (2) fundamental algorithms for autonomous motion planning.

Apply knowledge and understanding:
Students will be able to analyze and design architectures, algorithms and modules for planning, control and localization of autonomous mobile robots.

Critical and judgment skills:
Students will be able to choose the most suitable functional control architecture for a specific robotic system and to analyze its complexity as well as possible weaknesses.

Communication skills:
The course activities will allow students to be able to communicate/share the main problems concerning autonomous mobile robots, as well as the possible design choices for the control of such systems.

Learning ability:
The course development aim at giving the student a mindset oriented to the development of modules for the autonomous mobility of robots.

Students will be able to analyze and design architectures, algorithms and modules for planning, control and localization of autonomous mobile robots.

http://www.diag.uniroma1.it/~oriolo/amr/

No prerequisites

Introduction: applications, problems, architectures. Configuration Space. Wheeled Mobile Robots: mechanics of mobile robots, kinematic models of mobile robots, path/trajectory planning, control: trajectory tracking and regulation. Perception: sensors for mobile robots. Localization: odometric localization, Kalman Filter localization, landmark-based localization and SLAM. Motion Planning: retraction and cell decomposition, probabilistic planning, artificial potential fields. Humanoid Robots: introduction, dynamic modeling, gait generation and control. Case studies.

Slides and studying material on the course website.
B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics: Modelling, Planning and Control, Springer, 2009.

Classroom lectures

Attendance is recommended but not mandatory.

(1) Midterm test + final project, or (2) final exam

See previous tests on http://www.diag.uniroma1.it/~oriolo/amr/

• seehttps://www.diag.uniroma1.it/~oriolo/amr/