Lecture:
This course teaches students the basic concepts and techniques used in the field of mobile robotics. Within the lecture we will analyse navigation challenges and present "state of the art" solutions. The presented techniques are universally usable but the course will focus on wheel robots for driving. Among other topics, we will discuss:
- Sensors models (Laser Sensor)
- Motion models (Differential Drive),
- Vehicle localization (particle-filter and kalman filter)
- SLAM
- Path planning
Exercise:
Stage, a 2D Simulation (http://wiki.ros.org/stage), and ROS (http://wiki.ros.org), are used to provide the students with a simulated robotic hardware with laser range measurements.
Students have to implement first wanderer behaviour to get familiar with the environment, followed by a step- by- step implementation of a Monte Carlo particle filter and an Extended Kalman filter for self-localization. The final exercise is an "Open Challenge". Students can implement enhancements to their code and/or can try their program on a real robot.
Work environment:
Linux (Ubuntu 16.04), ROS, C++
A virutal box image with the working environment will be provided but a "root" installation is recommended.
There is the possibility to discuss problems with the simulation environment for the exercises after the first two lectures. Get everything up and running as soon as possible. The description how to install the environment is on the related TUWEL-course.
Probabilistic Robotics (2005)
Sebastian Thrun, Wolfram Burgard, Dieter Fox
LVA: Introduction to Mobile Robotics (2014)
http://ais.informatik.uni-freiburg.de/teaching/ss14/robotics/
Wolfram Burgard, Maren Bennewitz, Gian Diego Tipaldi, Luciano Spinello
Introduction to Autonomous Mobile Robots (2011)
Roland Siegwart, Illah Reza Nourbakhsh, Davide Scaramuzza
Principles of Robot Motion: Theory, Algorithms, and Implementations (2005)
Howie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun
