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.
Bruno Siciliano, Oussama Khatib: Handbook of Robotics (partially online at Googlebooks)
Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani: Robotics: modelling, planning 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.