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, kinematic chains, inverse kinematics, Jacobian, singularities, holonomic constraints, Ackermann-drive, PID, Newton-Euler.
  • 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.


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

Zusätzliche Informationen

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.


Bitte auch die Ankündigung im Vorlesungsverzeichnis beachten!