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, 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) 

14.10.  Introduction & Organization  01introduction  e01basics  
21.10.  Kinematics  02kinematics  e02geometry  
28.10.  Kinematics (cont'd)  e03kinematics  
4.11.  Dynamics  03dynamics  e04kinematics2  
11.11.  Dynamics (cont'd)  e05dynamics  
18.11.  Path Planning  04pathPlanning  e06dynamics  
25.11.  Path Optimization  05pathOptimization  e07pathFinding  
2.12.  Probabilities  06probabilities  cancelled  
9.12.  cancelled  
16.12.  Mobile Robotics  07mobileRobotics  e08probabilities  
6.1.  (holiday)  no exercises on Jan 7th  
13.1.  Mobile Robotics (cont'd)  e09particleAndKalmanFilter  
20.1.  Reinforcement Learning (brief overview)  10RL  
27.1.  Control Theory  08controlTheory  e10RL  
3.2.  Control Theory (cont'd)  e11riccati
main.problem.cpp 
Extra Exercise for those that need extra points: exxkalmanSLAM  
10.2.  Summary  14Roboticsscript 
 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)
 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/RobotModelingControlMarkSpong/dp/0471649902/ref=sr_1_fkmr0_3?ie=UTF8&qid=1286959147&sr=83fkmr0
 Springer Handbook of Robotics (partially online at Google books)