Robotics Course, WS 13/14, U Stuttgart
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, NewtonEuler, 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, LinearQuadratic optimal control, Riccati equations (differential, algebraic, discretetime), 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, graphbased SLAM)
 This is the central website of the lecture. Link to slides, exercise sheets, announcements, etc will all be posted here.
 See the 01introduction slides for further information.
date  topics  slides  exercises (due on 'date'+1) 
15.10.  Introduction & Organization  01introduction  read this 
22.10.  Kinematics  02kinematics  e01geometry 
29.10.  Kinematics (cont.)  e02kinematics  
5.11.  Path Planning  03pathPlanning  e03pegInAHole 
12.11.  Path Optimization Dynamics 
04pathOptimization
05dynamics 
e04pathFinding 
19.11.  Dynamics  05dynamics  e05dynamics 
26.11.  Mobile Robotics  07mobileRobotics
06probabilities 
e06dynamics 
3.12.  Mobile Robotics (cont.)  07mobileRobotics  e07particleFilter 
10.12.  Control Theory  08controlTheory  e08kalmanSLAM 
10.12.  Practical: The `racer'  09racer  e09cartPole 
7.1.  Practical: The `racer' (cont.)  e10racer
e10riccati 

14.1.  Control Theory (cont.)  08controlTheory  e11SysId
../data/01imu.dat ../data/02imu.dat ../data/01times.dat ../data/02times.dat racer.h racer.cpp 
21.1.  Reinforcement Learning in Robotics  10RL  e12stability 
28.1.  cancelled in favor of exercise work (exercises take place)  e13policySearch
CMA.tgz 

4.2.  Summary and Exam Preparation  13Roboticsscript
I will give a summary over everything you learned, and answer questions about the exam. 
cancelled Instead, please have a look on the full script (see '13Roboticsscript') and prepare questions for the lecture, if you have any 
 VideoLecture by Oussama Khatib: http://academicearth.org/courses/introductiontorobotics http://www.virtualprofessors.com/introductiontoroboticsstanfordcs223akhatib (focus on kinematics, dynamics, control)
 Oliver Brock's lecture http://courses.robotics.tuberlin.de/mediawiki/index.php/Robotics:_Schedule_WT09
 Stefan Schaal's lecture Introduction to Robotics: http://wwwclmc.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, nonholonomic constraints)
 Latombe's `motion planning' lecture: http://robotics.stanford.edu/~latombe/cs326/2007/schedule.htm (useful: sampling based path finding; nonholonomic (controlbased) 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.unifreiburg.de/teaching/ss10/robotics/
also the `robotics 2' lecture: http://ais.informatik.unifreiburg.de/teaching/ws10/robotics2/ (useful: Bayesian filter, SLAM)
 Handbook of Robotics (partially online at Google books) http://tiny.cc/u6tzl
 Robotics: modelling, planning and control) By Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani http://tiny.cc/b3faq
 LaValle's Planning Algorithms http://planning.cs.uiuc.edu/
 Robot Modeling and Control http://www.amazon.de/RobotModelingControlMarkSpong/dp/0471649902/ref=sr_1_fkmr0_3?ie=UTF8&qid=1286959147&sr=83fkmr0