Learning objectives
The course aims at providing to the students the theoretical principles and working knowledge for the design and development of autonomous and robotic systems.
Prerequisites
Adequate knowledge of architecture and programming of computer systems is recommended. Software design, dynamic systems, and control techniques notions are occasionally referenced.
Course unit content
Introduction to Robotics
- Classification of robotic systems
- Classification of sensors
- Control architectures: deliberative, reactive, and hybrid architectures
- Configuration space and motion planning
Mobile robotics
- Behavior-based robotics and mobile robot navigation
- Advanced range sensing
- Probabilistic methods for the estimation of robot and environment state
- Localization, Mapping and SLAM
Human-robot interaction, programming by demonstration, manipulation and grasping
- Robot teleoperation
- Human-Robot interaction
- Haptics
- Robot grasping
- Robot Learning
- Imitation and robot programming by demonstration
- Physics-based robot simulation
- Virtual reality and motion capture
- Humanoid robots
- Object recognition and environment modeling
- Visual servoing
Laboratory activities
Most course topics involve specific lab classes and demonstrations at the Dipartimento di Ingegneria dell'Informazione on the following topics:
- Frameworks for robotic applications
- Proximity sensors
- Development of robot behaviors
- Local representation of the environment
Full programme
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Bibliography
H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki and S. Thrun, Principles of Robot Motion, The MIT Press, 2005.
R. Siegwart, I.R. Nourbakhsh, "Autonomous Mobile Robots", MIT Press.
S. Thrun, W. Burgard, and D. Fox. Probabilistic Robotics. MIT Press, Cambridge, MA, 2005.
Teaching methods
The course is carried out through classroom lectures to illustrate the concepts, monographic workshops on specific topics and practicals in the laboratory.
Assessment methods and criteria
The final mark is the sum of the following tests:
- Competition: 6%
- Written class test : 47%
- Final project (with presentation): 47%
Other information
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