ROVIS Robotics Summer School 2017

Organisation

Period: 17-24 July 2017
Target group: Students, PhD students, Professors
Summer School Coordinator : Gigel Macesanu
Lecturers: Claudiu Pozna (CV), Sorin Grigorescu (CV), Liviu Marina (CV), Tiberiu Cocias (CV), Gigel Macesanu (CV), Bogdan Trasnea (CV)
Language: English

Course aim

The main objective of the course is to give a general understanding of main concepts related to robot modeling and control, deep learning, robot vision and robotic operating systems. During this summer school, all students will start with the basics of manipulation models and mobile robots. Then, an introduction on robots operating system will be presented, followed by general topics about Machine Learning and Computer Vision theory. At the end of the course an overview of available algorithms for objects detection and recognition will be presented.

Content

Lecture Module Introduction Advanced Course Materials
1 Modeling and Control of Robotic Systems (Claudiu Pozna) Manipulators Models (8 hours):
  • Mathematical descriptions (models) of manipulators.
  • Starting with a Mathematical refresher we increase step by step the accuracy of knowledge.
  • Manipulator models: geometric, kinematic and dynamic model.
  • Programing and simulations (in Matlab).
Mobile Robots Models (10 hours):
  • Mathematical description of an autonomous car.
  • Trajectory definition; Comparator; PID controller; Car model; Sensors and filters.
  • Programing and simulations (in Matlab).
Control of manipulators (8 hours):
  • Control Design refresher: control methods.
  • DC motor control; Control based on Jacobian; Dynamic control.
  • Programing and simulations (in Matlab).
Manipulators Models Mobile Robots
2 Robot Operating System (ROS) Introduction to ROS (2 hours):
  • What is ROS
  • How to configure it
  • How to use it
Robots simulation (2 hours):
  • 3D visualization tool for ROS
  • Robots simulation using Gazebo
Modules development using ROS (4 hours):
  • Creating a ROS package by hand
  • Managing System dependencies
  • Running ROS across multiple machines
  • Libraries with ROS Interfaces
  • External ROS Resources
Introduction to ROS & Robots visualisation and simulation
3 Machine Learning in Robotics Linear Regression (4 hours):
  • Model representation
  • Cost function
  • Gradient descenet
Logistic Regression (4 hours):
  • Hypothesis representation
  • Cost function
  • Gradient descenet
Neural Networks (4 hours):
  • Network representation and feedforward processing
  • Backpropagation method for training
Convolutional Neural Networks (4 hours):
  • Architecture
  • Convolution
  • Pooling
4 Robot Vision Introduction to computer vision (4 hours):
  • Image formation and filtering
  • Image representation and noise
  • Spatial filtering
Object recognition (4 hours):
  • Template matching
  • Region segmentation
  • Edge detection
Optics and 3D Reconstruction (4 hours):
  • Ideal camera model
  • Camera calibration
  • Stereo Vision
  • Correspondence matching
  • Point clouds processing
Object tracking (4 hours):
  • Optical flow
  • Dynamic models for object tracking