Unsupervised learning for surveillance planning with team of aerial vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315450" target="_blank" >RIV/68407700:21230/17:00315450 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7966405/" target="_blank" >http://ieeexplore.ieee.org/document/7966405/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2017.7966405" target="_blank" >10.1109/IJCNN.2017.7966405</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised learning for surveillance planning with team of aerial vehicles

Popis výsledku v původním jazyce

In this paper, we extent an existing self-organizing map (SOM)-based approach for the Dubins traveling salesman problem (DTSP) to solve its multi-vehicle variant generalized for visiting target regions called k-DTSP with Neighborhoods (k-DTSPN). The Dubins TSP is a variant of the combinatorial TSP for curvature-constrained vehicles. The problem is to determine a cost efficient path to visit a given set of continuous regions while the path allows to satisfy kinematic constraints of non-holonomic vehicles. The k-DTSPN is a generalization to determine k such paths, one for each vehicle. Although the k-DTSPN has been addressed by evolutionary methods, the proposed approach is able to provide solutions very quickly in units of seconds on conventional computationally resources which makes the proposed SOM-based approach suitable for on-line planning. The studied problem is motivated by surveillance task in which it is required to quickly provide information about the given set of target locations. Therefore, real computational requirements are crucial properties of the desired k-DTSPN solver. The proposed method meets this requirement and feasibility of the found solutions are demonstrated not only in computer simulations but also with a practical deployment on real aerial vehicles.

Název v anglickém jazyce

Unsupervised learning for surveillance planning with team of aerial vehicles

Popis výsledku anglicky

In this paper, we extent an existing self-organizing map (SOM)-based approach for the Dubins traveling salesman problem (DTSP) to solve its multi-vehicle variant generalized for visiting target regions called k-DTSP with Neighborhoods (k-DTSPN). The Dubins TSP is a variant of the combinatorial TSP for curvature-constrained vehicles. The problem is to determine a cost efficient path to visit a given set of continuous regions while the path allows to satisfy kinematic constraints of non-holonomic vehicles. The k-DTSPN is a generalization to determine k such paths, one for each vehicle. Although the k-DTSPN has been addressed by evolutionary methods, the proposed approach is able to provide solutions very quickly in units of seconds on conventional computationally resources which makes the proposed SOM-based approach suitable for on-line planning. The studied problem is motivated by surveillance task in which it is required to quickly provide information about the given set of target locations. Therefore, real computational requirements are crucial properties of the desired k-DTSPN solver. The proposed method meets this requirement and feasibility of the found solutions are demonstrated not only in computer simulations but also with a practical deployment on real aerial vehicles.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-24206S" target="_blank" >GA16-24206S: Metody informatického plánování cest pro neholonomní mobilní roboty v úlohách monitorování a dohledu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the International Joint Conference on Neural Networks

ISBN

978-1-5090-6181-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

4340-4347

Název nakladatele

IEEE Xplore

Místo vydání

—

Místo konání akce

Anchorage

Datum konání akce

14. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426968704078