Unsupervised learning for surveillance planning with team of aerial vehicles

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Unsupervised learning for surveillance planning with team of aerial vehicles

Original language description

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.

Czech name

—

Czech description

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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)

Project

<a href="/en/project/GA16-24206S" target="_blank" >GA16-24206S: Efficient Information Gathering with Dubins Vehicles in Persistent Monitoring and Surveillance Missions</a><br>

Continuities

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

Publication year

2017

Confidentiality

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

Article name in the collection

Proceedings of the International Joint Conference on Neural Networks

ISBN

978-1-5090-6181-5

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

4340-4347

Publisher name

IEEE Xplore

Place of publication

—

Event location

Anchorage

Event date

May 14, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426968704078