LQR-Trees with Sampling Based Exploration of the State Space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00580655" target="_blank" >RIV/67985807:_____/23:00580655 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/23:00374131
Result on the web
<a href="https://doi.org/10.1109/IROS55552.2023.10341767" target="_blank" >https://doi.org/10.1109/IROS55552.2023.10341767</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IROS55552.2023.10341767" target="_blank" >10.1109/IROS55552.2023.10341767</a>
Alternative languages
Result language
angličtina
Original language name
LQR-Trees with Sampling Based Exploration of the State Space
Original language description
This paper introduces an extension of the LQR-tree algorithm, which is a feedback-motion-planning algorithm for stabilizing a system of ordinary differential equations from a bounded set of initial conditions to a goal. The constructed policies are represented by a tree of exemplary system trajectories, so called demonstrations, and linear-quadratic regulator (LQR) feedback controllers. Consequently, the crucial component of any LQR-tree algorithm is a demonstrator that provides suitable demonstrations. In previous work, such a demonstrator was given by a local trajectory optimizer. However, these require appropriate initial guesses of solutions to provide valid results, which was pointed out, but largely unresolved in previous implementations. In this paper, we augment the LQR-tree algorithm with a randomized motion-planning procedure to discover new valid demonstration candidates to initialize the demonstrator in parts of state space not yet covered by the LQR-tree. In comparison to the previous versions of the LQR-tree algorithm, the resulting exploring LQR-tree algorithm reliably synthesizes feedback control laws for a far more general set of problems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA21-09458S" target="_blank" >GA21-09458S: Quasi-Decision Procedures for First-Order Theories of Real Functions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ISBN
978-1-6654-9190-7
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
4777-4782
Publisher name
IEEE
Place of publication
Detroit
Event location
Detroit
Event date
Oct 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001133658803093