Unsupervised learning-based solution of the Close Enough Dubins Orienteering Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00344488" target="_blank" >RIV/68407700:21230/20:00344488 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s00521-019-04222-9" target="_blank" >https://doi.org/10.1007/s00521-019-04222-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-019-04222-9" target="_blank" >10.1007/s00521-019-04222-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised learning-based solution of the Close Enough Dubins Orienteering Problem

Popis výsledku v původním jazyce

This paper reports on the application of novel unsupervised learning-based method called the Growing Self-Organizing Array (GSOA) to data collection planning with curvature-constrained paths that is motivated by surveillance missions with aerial vehicles. The planning problem is formulated as the Close Enough Dubins Orienteering Problem which combines combinatorial optimization with continuous optimization to determine the most rewarding data collection path that does not exceed the given travel budget and satisfies the motion constraints of the vehicle. The combinatorial optimization consists of selecting a subset of the most rewarding data to be collected and the schedule of data collection. On the other hand, the continuous optimization stands to determine the most suitable waypoint locations from which selected data can be collected together with the determination of the headings at the waypoints for the used Dubins vehicle model. The existing purely combinatorial approaches need to discretize the possible waypoint locations and headings into some finite sets, and the solution is computationally very demanding because the problem size is quickly increased. On the contrary, the employed GSOA performs online sampling of the waypoints and headings during the adaptation of the growing structure that represents the requested curvature-constrained data collection path. Regarding the presented results, the proposed approach provides solutions to orienteering problems with competitive quality, but it is significantly less computationally demanding.

Název v anglickém jazyce

Unsupervised learning-based solution of the Close Enough Dubins Orienteering Problem

Popis výsledku anglicky

This paper reports on the application of novel unsupervised learning-based method called the Growing Self-Organizing Array (GSOA) to data collection planning with curvature-constrained paths that is motivated by surveillance missions with aerial vehicles. The planning problem is formulated as the Close Enough Dubins Orienteering Problem which combines combinatorial optimization with continuous optimization to determine the most rewarding data collection path that does not exceed the given travel budget and satisfies the motion constraints of the vehicle. The combinatorial optimization consists of selecting a subset of the most rewarding data to be collected and the schedule of data collection. On the other hand, the continuous optimization stands to determine the most suitable waypoint locations from which selected data can be collected together with the determination of the headings at the waypoints for the used Dubins vehicle model. The existing purely combinatorial approaches need to discretize the possible waypoint locations and headings into some finite sets, and the solution is computationally very demanding because the problem size is quickly increased. On the contrary, the employed GSOA performs online sampling of the waypoints and headings during the adaptation of the growing structure that represents the requested curvature-constrained data collection path. Regarding the presented results, the proposed approach provides solutions to orienteering problems with competitive quality, but it is significantly less computationally demanding.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2020

Kód důvěrnosti údajů

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

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

24

Číslo periodika v rámci svazku

32

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

18193-18211

Kód UT WoS článku

000595603200026

EID výsledku v databázi Scopus

2-s2.0-85065728110