EECI 2012: Computer Session: TuLiP: Difference between revisions
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==  Lecture Materials ==  | ==  Lecture Materials ==  | ||
* Lecture slides: [http://  | * Lecture slides: [http://dl.dropbox.com/u/29005314/C2_tulip-17May12.pdf TuLiP] (Exercises are at the end of the slides.)  | ||
* MATLAB plotting: [http://  | * MATLAB plotting: [http://dl.dropbox.com/u/29005314/plotRobotSim.m plotRobotSim.m], [http://dl.dropbox.com/u/29005314/plotCarSim.m plotCarSim.m]  | ||
* [http://dl.dropbox.com/u/29005314/tulip_examples.zip Example TuLiP files] (zip file):   | |||
*   | ** 6 cell robot, discrete state space: [http://dl.dropbox.com/u/29005314/robot_discrete_simple.py robot_discrete_simple.py]  | ||
** 6 cell robot, with dynamics: [http://dl.dropbox.com/u/29005314/robot_simple.py robot_simple.py], [http://dl.dropbox.com/u/29005314/robot_simple2.py robot_simple2.py] (alternative formulation)  | |||
== Further Reading ==  | == Further Reading ==  | ||
Revision as of 04:49, 10 May 2012
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This lecture provides an overview of TuLiP, a Python-based software toolbox for the synthesis of embedded control software that is provably correct with respect to a GR[1] specifications. TuLiP combines routines for (1) finite state abstraction of control systems, (2) digital design synthesis from GR[1] specifications, and (3) receding horizon planning. The underlying digital design synthesis routine treats the environment as adversary; hence, the resulting controller is guaranteed to be correct for any admissible environment profile. TuLiP applies the receding horizon framework, allowing the synthesis problem to be broken into a set of smaller problems, and consequently alleviating the computational complexity of the synthesis procedure, while preserving the correctness guarantee.
A brief overview of TuLiP will be followed by hands-on exercises using the toolbox.
Lecture Materials
- Lecture slides: TuLiP (Exercises are at the end of the slides.)
 - MATLAB plotting: plotRobotSim.m, plotCarSim.m
 - Example TuLiP files (zip file):
- 6 cell robot, discrete state space: robot_discrete_simple.py
 - 6 cell robot, with dynamics: robot_simple.py, robot_simple2.py (alternative formulation)
 
 
Further Reading
TuLiP: A Software Toolbox for Receding Horizon Temporal Logic Planning, T. Wongpiromsarn, U. Topcu, N. Ozay, H. Xu and R. M. Murray, Hybrid Systems: Computation and Control, 2011.
Additional Information
JTLV Project Home Site JTLV provides the framework for the underlying digital design synthesis routine used in TuLiP.