First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
AUTOMATION ENGINEERING
Course unit
ROBOTICS, VISION AND CONTROL
INP4063809, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
AUTOMATION ENGINEERING
IN0527, Degree course structure A.Y. 2008/09, A.Y. 2018/19
N0
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination ROBOTICS, VISION AND CONTROL
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0527-000ZZ-2017-INP4063809-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge RUGGERO CARLI ING-INF/04

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP4063809 ROBOTICS, VISION AND CONTROL RUGGERO CARLI IN2371
INP4063809 ROBOTICS, VISION AND CONTROL RUGGERO CARLI IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/04 Automatics 9.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
5 A.A. 2018/2019 01/10/2018 15/03/2020 CARLI RUGGERO (Presidente)
SCHENATO LUCA (Membro Effettivo)
BEGHI ALESSANDRO (Supplente)
CENEDESE ANGELO (Supplente)
PILLONETTO GIANLUIGI (Supplente)
PINZONI STEFANO (Supplente)
TICOZZI FRANCESCO (Supplente)
VALCHER MARIA ELENA (Supplente)
VITTURI STEFANO (Supplente)
ZAMPIERI SANDRO (Supplente)
ZORZI MATTIA (Supplente)
4 A.A. 2017/2018 01/10/2017 15/03/2019 CARLI RUGGERO (Presidente)
SCHENATO LUCA (Membro Effettivo)
BEGHI ALESSANDRO (Supplente)
BISIACCO MAURO (Supplente)
CENEDESE ANGELO (Supplente)
CHIUSO ALESSANDRO (Supplente)
FERRANTE AUGUSTO (Supplente)
PILLONETTO GIANLUIGI (Supplente)
PINZONI STEFANO (Supplente)
SUSTO GIAN ANTONIO (Supplente)
TICOZZI FRANCESCO (Supplente)
VALCHER MARIA ELENA (Supplente)
VITTURI STEFANO (Supplente)
ZAMPIERI SANDRO (Supplente)
ZORZI MATTIA (Supplente)

Syllabus
Prerequisites: The course requires basic knowledge in Calculus and Linear Algebra, Automatic Control, Estimation and Filtering, and Computer Programming in MatLab.
Target skills and knowledge: In its various articulations the course is expected to provide the following knowledge and skills:

ROBOTIC ARM AND MOBILE ROBOTS
1. To derive kinematic and dynamic models for robotic arms and mobile robots like unicycle, bicycle and quadrotors.
2. To design control algorithms for tracking trajectories (robotic arms), and for localization and navigation (mobile robots).

COMPUTER VISION
1. To know and be able to use the main algorithms used in image formation, in image processing and features extraction.
2. To combine control strategies with computer vision algorithms.
Examination methods: The verification of knowledge and skills' expected is carried out with a written test that includes:
a) open questions on the topics seen during the lessons;
b) resolutions of numerical exercises.
In addition, students can optionally take an oral exam to improve the grade obtained in the written test. The oral exam is based on the presentation of a research article assigned by the teacher.
Assessment criteria: The evaluation criteria with which the verification of knowledge and expected skills will be carried out are:
1. Completeness of the acquired knowledge
2. Analytical skills in the resolution of exercises
3. Property in the technical terminology used
4. Exposure quality both written and oral (the latter in case the student also takes the oral exam)
Course unit contents: In its various articulations the course plans to cover the following contents:
REPRESENTATIONS OF POSE
Translations; rotation matrices; Euler angles; quaternions
ROBOTIC MANIPULATORS
Description of the structure of a robotic arm; revolute and prismatic joints; direct kinematics; Denavit-Hartenberg Convention; inverse kinematics; differential kinematics; geometric jacobian; redundancy analysis; inverse differential kinematics; analytical jacobian; static; derivation of the model of a robotic arm using the Lagrangian approach and the Newton-Euler approach; development of control algorithms for trajectories tracking
MOBILE ROBOTS
Holonomic and non-holonomic constraints; derivation of models of unicycles, bicycles and quadricopters; control algorithms for localization and navigation
COMPUTATIONAL VISION
Spectral representation of light and color; imaging formation; image analysis (monodic, dyadic and spatial operations); image feature extraction (region features, line features, point features); elements of computer vision using multiple images (feature correspondence, geometry of multiple views, stereo vision)
ROBOTICS, VISION AND CONTROL
Vision serving algorithms (in particular Position-based Visual Servoing and Image-Based Visual Servoing)
Planned learning activities and teaching methods: Theoretical frontal lessons, accompanied by practical examples, numerical exercises and exercises in Matlab. The student must accompany the theoretical study with an adequate number of numerical and non-numerical exercises. Homework will be assigned gradually, as the various topics will be explained during the course.
Additional notes about suggested reading: Lecture notes provided by the teacher.
Textbooks (and optional supplementary readings)
  • Peter Corke, Robotics, Vision and Control. --: Springer, 2011. Cerca nel catalogo
  • Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo, Robotics. Modelling, Planning and Control. --: Springer, 2009. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Case study
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education Gender Equality Decent Work and Economic Growth Industry, Innovation and Infrastructure Reduced Inequalities