Multi-Vehicle Close Enough Orienteering Problem with Bézier Curves for Multi-Rotor Aerial Vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332892" target="_blank" >RIV/68407700:21230/19:00332892 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8794339" target="_blank" >https://ieeexplore.ieee.org/document/8794339</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICRA.2019.8794339" target="_blank" >10.1109/ICRA.2019.8794339</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Vehicle Close Enough Orienteering Problem with Bézier Curves for Multi-Rotor Aerial Vehicles

Popis výsledku v původním jazyce

This paper introduces the Close Enough Orienteering Problem (CEOP) for planning missions with multi-rotor aerial vehicles considering their maximal velocity and acceleration limits. The addressed problem stands to select the most rewarding target locations and sequence to visit them in the given limited travel budget. The reward is collected within a non-zero range from a particular target location that allows saving the travel cost, and thus collect more rewards. Hence, we are searching for the fastest trajectories to collect the most valuable rewards such that the motion constraints are not violated, and the travel budget is satisfied. We leverage on existing trajectory parametrization based on Bézier curves recently deployed in surveillance planning using unsupervised learning, and we propose to employ the learning in a solution of the introduced multi-vehicle CEOP. Feasibility of the proposed approach is supported by empirical evaluation and experimental deployment using multi-rotor vehicles.

Název v anglickém jazyce

Multi-Vehicle Close Enough Orienteering Problem with Bézier Curves for Multi-Rotor Aerial Vehicles

Popis výsledku anglicky

This paper introduces the Close Enough Orienteering Problem (CEOP) for planning missions with multi-rotor aerial vehicles considering their maximal velocity and acceleration limits. The addressed problem stands to select the most rewarding target locations and sequence to visit them in the given limited travel budget. The reward is collected within a non-zero range from a particular target location that allows saving the travel cost, and thus collect more rewards. Hence, we are searching for the fastest trajectories to collect the most valuable rewards such that the motion constraints are not violated, and the travel budget is satisfied. We leverage on existing trajectory parametrization based on Bézier curves recently deployed in surveillance planning using unsupervised learning, and we propose to employ the learning in a solution of the introduced multi-vehicle CEOP. Feasibility of the proposed approach is supported by empirical evaluation and experimental deployment using multi-rotor 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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2019 International Conference on Robotics and Automation

ISBN

978-1-5386-6026-3

ISSN

1050-4729

e-ISSN

2577-087X

Počet stran výsledku

6

Strana od-do

3039-3044

Název nakladatele

IEEE Xplore

Místo vydání

—

Místo konání akce

Montreal

Datum konání akce

20. 5. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000494942302035