Robotics Course WS 14/15 U Stuttgart
Please register to the Email list
See my general teaching page for previous versions of this lecture.

The lecture will give an introduction to robotics in four chapters:
- Scope
-
- Kinematics & Dynamics
-
goal: orchestrate joint movements for
desired movement in task spaces
(Kinematic map, Jacobian, optimality principle of inverse kinematics, singularities, configuration/operational/null space, multiple simultaneous tasks, special task variables, trajectory interpolation, motion profiles; 1D point mass, damping \& oscillation, PID, general dynamic systems, Newton-Euler, joint space control, reference trajectory following, optimal operational space control) - Planning and optimization
-
goal: planning around obstacles, optimizing trajectories
(Path finding vs.\ trajectory optimization, local vs.\ global, Dijkstra, Probabilistic Roadmaps, Rapidly Exploring Random Trees, differential constraints, metrics; trajectory optimization, general cost function, task variables, transition costs, gradient methods, 2nd order methods, Dynamic Programming) - Control Theory
-
theory on designing optimal controllers
(Topics in control theory, optimal control, HJB equation, infinite horizon case, Linear-Quadratic optimal control, Riccati equations (differential, algebraic, discrete-time), controllability, stability, eigenvalue analysis, Lyapunov function) - Mobile robots
-
goal: localize and map yourself; walk
(State estimation, Bayes filter, odometry, particle filter, Kalman filter, Bayes smoothing, SLAM, joint Bayes filter, EKF SLAM, particle SLAM, graph-based SLAM)
- This is the central website of the lecture. Link to slides, exercise sheets, announcements, etc will all be posted here.
- See the 01-introduction slides for further information.
date | topics | slides | exercises (due on 'date'+1) |
|
14.10. | Introduction & Organization | 01-introduction | e01-basics | |
21.10. | Kinematics | 02-kinematics | e02-geometry | |
28.10. | Kinematics (cont'd) | e03-kinematics | ||
4.11. | Dynamics | 03-dynamics | e04-kinematics2 | |
11.11. | Dynamics (cont'd) | e05-dynamics | ||
18.11. | Path Planning | 04-pathPlanning | e06-dynamics | |
25.11. | Path Optimization | 05-pathOptimization | e07-pathFinding | |
2.12. | Probabilities | 06-probabilities | cancelled | |
9.12. | cancelled | |||
16.12. | Mobile Robotics | 07-mobileRobotics | e08-probabilities | |
6.1. | (holiday) | no exercises on Jan 7th | ||
13.1. | Mobile Robotics (cont'd) | e09-particleAndKalmanFilter | ||
20.1. | Reinforcement Learning (brief overview) | 10-RL | ||
27.1. | Control Theory | 08-controlTheory | e10-RL | |
3.2. | Control Theory (cont'd) | e11-riccati
main.problem.cpp |
Extra Exercise for those that need extra points: exx-kalmanSLAM | |
10.2. | Summary | 14-Robotics-script |
- VideoLecture by Oussama Khatib: http://academicearth.org/courses/introduction-to-robotics http://www.virtualprofessors.com/introduction-to-robotics-stanford-cs223a-khatib (focus on kinematics, dynamics, control)
- Oliver Brock's lecture http://courses.robotics.tu-berlin.de/mediawiki/index.php/Robotics:_Schedule_WT09
- Stefan Schaal's lecture Introduction to Robotics: http://www-clmc.usc.edu/Teaching/TeachingIntroductionToRoboticsSyllabus (focus on control, useful: Basic Linear Control Theory (analytic solution to simple dynamic model $\to$ PID), chapter on dynamics)
- Chris Atkeson's `Kinematics, Dynamic Systems, and Control' http://www.cs.cmu.edu/~cga/kdc/ (uses Schaal's slides and LaValle's book, useful: slides on 3d kinematics http://www.cs.cmu.edu/~cga/kdc/ewhitman1.pptx )
- CMU lecture `introduction to robotics' http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/16311/www/current/syllabus.html (useful: PID control, simple BUGs algorithms for motion planning, non-holonomic constraints)
- Latombe's `motion planning' lecture: http://robotics.stanford.edu/~latombe/cs326/2007/schedule.htm (useful: sampling based path finding; non-holonomic (control-based) planners)
- Robert Stengel's lectures on `Optimal Control and Estimation' http://www.princeton.edu/~stengel/MAE546Lectures.html
- Drew Bagnell's lecture on `Adaptive Control and Reinforcement Learning' http://robotwhisperer.org/acrls11/
-
Freiburg's `mobile robotics' lecture:
http://ais.informatik.uni-freiburg.de/teaching/ss10/robotics/
also the `robotics 2' lecture: http://ais.informatik.uni-freiburg.de/teaching/ws10/robotics2/ (useful: Bayesian filter, SLAM)
- PDFs of several books: http://www.kramirez.net/Robotica/Material/Libros/
- For a good overview: Robotics: modelling, planning and control. Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani http://www.kramirez.net/Robotica/Material/Libros/Robotics%20-%20Modelling
- For planning methods: LaValle's Planning Algorithms http://planning.cs.uiuc.edu/
- For SLAM: Probabilistic Robotics, Trhun, Burgard, Fox.
- Classical: Robot Modeling and Control http://www.amazon.de/Robot-Modeling-Control-Mark-Spong/dp/0471649902/ref=sr_1_fkmr0_3?ie=UTF8&qid=1286959147&sr=8-3-fkmr0
- Springer Handbook of Robotics (partially online at Google books)