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, Newton-Euler, joint space control, reference trajectory following, optimal operational space control) - Planning and optimization
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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
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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
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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) |
15.10. | Introduction & Organization | 01-introduction | read this |
22.10. | Kinematics | 02-kinematics | e01-geometry |
29.10. | Kinematics (cont.) | e02-kinematics | |
5.11. | Path Planning | 03-pathPlanning | e03-pegInAHole |
12.11. | Path Optimization Dynamics |
04-pathOptimization
05-dynamics |
e04-pathFinding |
19.11. | Dynamics | 05-dynamics | e05-dynamics |
26.11. | Mobile Robotics | 07-mobileRobotics
06-probabilities |
e06-dynamics |
3.12. | Mobile Robotics (cont.) | 07-mobileRobotics | e07-particleFilter |
10.12. | Control Theory | 08-controlTheory | e08-kalmanSLAM |
10.12. | Practical: The `racer' | 09-racer | e09-cartPole |
7.1. | Practical: The `racer' (cont.) | e10-racer
e10-riccati |
|
14.1. | Control Theory (cont.) | 08-controlTheory | e11-SysId
../data/01-imu.dat ../data/02-imu.dat ../data/01-times.dat ../data/02-times.dat racer.h racer.cpp |
21.1. | Reinforcement Learning in Robotics | 10-RL | e12-stability |
28.1. | cancelled in favor of exercise work (exercises take place) | e13-policySearch
CMA.tgz |
|
4.2. | Summary and Exam Preparation | 13-Robotics-script
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 '13-Robotics-script') and prepare questions for the lecture, if you have any |
- 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)
- 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/Robot-Modeling-Control-Mark-Spong/dp/0471649902/ref=sr_1_fkmr0_3?ie=UTF8&qid=1286959147&sr=8-3-fkmr0