EECI 2020: Computer Session: TuLiP: Difference between revisions
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== Lecture Materials == | == Lecture Materials == | ||
* Lecture slides: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/C2_tulip-11Mar2020.pdf TuLiP] | * Lecture slides: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/C2_tulip-11Mar2020.pdf TuLiP] | ||
* amination script: [ | * amination script: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/animate.py animate.py] | ||
* [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/tulip_examples.zip Example TuLiP files] (zip file): | * [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/tulip_examples.zip Example TuLiP files] (zip file): | ||
** 6 cell robot, discrete state space: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_discrete.ipynb robot_simple_discrete.ipynb] | ** 6 cell robot, discrete state space: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_discrete.ipynb robot_simple_discrete.ipynb] | ||
Revision as of 10:07, 12 March 2020
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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
- amination script: animate.py
- Example TuLiP files (zip file):
- 6 cell robot, discrete state space: robot_simple_discrete.ipynb
- 6 cell robot, with dynamics: robot_simple_continuous.ipynb
- 3x3 exercise: exercise_3x3.ipynb
- Left turn exercise: exercise_leftturn.ipynb
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.
