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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

2020

Confidentiality

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

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

24

Issue of the periodical within the volume

32

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

18193-18211

UT code for WoS article

000595603200026

EID of the result in the Scopus database

2-s2.0-85065728110