Content

This class will give an introduction to robotics. It will be structured into the following parts:

• Generating motion and and dynamic control: This chapter will cover coordinate frames, non-holonomic constraints, Ackermann-drive (in analogy to street cars), PID.
• Planning: Planning around obstacles, path finding, Dijkstra, A*, configuration space obstacles, RRTs, lattice planners, gradient methods, potential fields, splines.
• Localization and mapping: state estimation problem, Bayesian filter, Odometry, Particle & Kalman filter, Extended and Unscented Kalman-Filter, simultaneous localization and mapping (SLAM).
• Vision and perception: SIFT, HOG-features, Deformable parts models, hough transform, lane detection, 3d-point clouds, RANSAC .

After these lectures, students will be able to design basic algorithms for motion, control and state estimation for robotics.

The lecture will be in German, accompanying materials in English.

Literatur:

John J Craig: Introduction to Robotics: Mechanics and Control; Steven LaValle: Planning Algorithms; Sebastian Thrun, Wolfram Burgard, Dieter Fox: Probabilistic Robotics

 

Students interested in robotics with application to autonomous vehicles. Voraussetzungen: As a prerequisite, student should have basic knowledge of maths, in particular linear algebra and a bit of optimization. Students will work with a real model car in the robotics lab.