Unsupervised Learning-Based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380506" target="_blank" >RIV/68407700:21230/24:00380506 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-67159-3_2" target="_blank" >https://doi.org/10.1007/978-3-031-67159-3_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-67159-3_2" target="_blank" >10.1007/978-3-031-67159-3_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Learning-Based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time

Popis výsledku v původním jazyce

In remote data collection from sampling stations, a vehicle must be within sufficient distance from a particular station for a predefined minimal time to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensing site and ensure the vehicle is within a reliable communication distance from the station for a sufficient period. We propose to formulate the planning problem as a variant of the Close Enough Dubins Traveling Salesman Problem with Time Constraints (CEDTSP-TC) that is heuristically solved by unsupervised learning of the Growing Self-Organizing Array (GSOA) modified to address the constrained minimal data retrieving time. The proposed method is compared with a baseline based on a sampling-based decoupled approach, and the results support the feasibility of both proposed solvers in random instances.

Název v anglickém jazyce

Unsupervised Learning-Based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time

Popis výsledku anglicky

In remote data collection from sampling stations, a vehicle must be within sufficient distance from a particular station for a predefined minimal time to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensing site and ensure the vehicle is within a reliable communication distance from the station for a sufficient period. We propose to formulate the planning problem as a variant of the Close Enough Dubins Traveling Salesman Problem with Time Constraints (CEDTSP-TC) that is heuristically solved by unsupervised learning of the Growing Self-Organizing Array (GSOA) modified to address the constrained minimal data retrieving time. The proposed method is compared with a baseline based on a sampling-based decoupled approach, and the results support the feasibility of both proposed solvers in random instances.

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/GA22-05762S" target="_blank" >GA22-05762S: Optimální řešení robotických směrovacích úloh</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond

ISBN

978-3-031-67158-6

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

11

Strana od-do

11-21

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Místo konání akce

Mittweida

Datum konání akce

10. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001322509700002