Robotics for AI

Faculteit Science and Engineering
Jaar 2019/20
Vakcode KIM.ROB03
Vaknaam Robotics for AI
Niveau(s) master
Voertaal Engels
Periode semester I b
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Robotics for AI
Leerdoelen At the end of the course, the student is able to:
- use Git, Python, ROS (Robot Operating System), and Gazebo in the practical implementation on a real domestic service robot.
- design a SLAM (Simultaneous Localization And Mapping) algorithm by using Monte Carlo methods for the autonomous
navigation of mobile robot.
- implement and experiment a SLAM algorithm on a real mobile robot.
- design a deep learning algorithm, based on the Google TensorFlow framework, for object recognition and autonomous grasping by means of a robotic arm.
- implement and experiment a deep learning algorithm on a real robotic arm.
- prepare a final robotic demonstration in which a mobile domestic service robot navigates in an unknown environment, approaches a table, recognizes objects, and grasps a specific one.
Omschrijving In this course, the students will deepen the theoretical knowledge on artificial intelligence concepts and will gain practical experience on a real domestic service robot.
Specifically, the students will: i) design and implement a SLAM (Simultaneous Localization And Mapping) algorithm for the autonomous navigation of a mobile robot; ii) design and implement a deep learning algorithm for object recognition and autonomous grasping by means of a robotic arm.
Uren per week
Onderwijsvorm Practisch werk (PRC)
(Students will work in the robot laboratory, partly independently and partly under supervision. Given the limited number of robots, students will work in groups of two or three. Mandatory attendance.)
Toetsvorm Opdracht (AST), Verslag (R)
(50% Report (divided over four assignments), 50% Assignments (two demos that will count for a total of 30% and a video that will count for 20%). Students will need to score at least a 5.5 average, and will have to get at least a 5.0 on each component. This course has mandatory presence.)
Vaksoort master
Coördinator prof. dr. R. Carloni
Docent(en) prof. dr. R. Carloni
Entreevoorwaarden Prior knowledge of Python and Git can be useful, but is not required.
Opmerkingen
Opgenomen in
Opleiding Jaar Periode Type
MSc Artificial Intelligence  (C - Elective Course Units) - semester I b keuze MAS
MSc Artificial Intelligence  (B - Mandatory Course Units Computational Intelligence and Robotics) - semester I b verplicht CI&R
MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester I b keuze
MSc Human-Machine Communication  (C - Elective Course Units) - semester I b keuze
MSc Mechanical Engineering: Smart Factories  (Electives ) 1 semester I b keuzegroep