Cooperative path planning for multiple MAVs operating in unknown environments
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345631" target="_blank" >RIV/68407700:21230/20:00345631 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/ICUAS48674.2020.9213896" target="_blank" >https://doi.org/10.1109/ICUAS48674.2020.9213896</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUAS48674.2020.9213896" target="_blank" >10.1109/ICUAS48674.2020.9213896</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Cooperative path planning for multiple MAVs operating in unknown environments
Popis výsledku v původním jazyce
In recent years, Micro Aerial Vehicles (MAVs) have become widely available and are successfully used in many real scenarios. While the early applications like surveillance mostly utilized single MAVs or a group of multiple, yet non-cooperative MAVs, recent research is more focused on a group of cooperating MAVs. A typical example is the payload transport task, where multiple MAVs carry a single object. This problem has been studied mainly from the control theory point of view, providing robust control to cooperating MAVs using the dynamics of the whole system. Real applications, however, require operating in unknown environments with obstacles, which needs motion planning. In this paper, we propose a novel motion planning method for multiple MAVs operating in unknown environments. The proposed work is based on the Sensor-based Random Trees method (SRT), which was originally intended for exploration of unknown environments. We extend the method for online path planning of multi MAVs. In the proposed method, each MAV makes a motion plan and exchanges key waypoints with other MAVs to ensure that their mutual positions satisfy the mission constraints. The performance of the method is demonstrated in various simulation experiments.
Název v anglickém jazyce
Cooperative path planning for multiple MAVs operating in unknown environments
Popis výsledku anglicky
In recent years, Micro Aerial Vehicles (MAVs) have become widely available and are successfully used in many real scenarios. While the early applications like surveillance mostly utilized single MAVs or a group of multiple, yet non-cooperative MAVs, recent research is more focused on a group of cooperating MAVs. A typical example is the payload transport task, where multiple MAVs carry a single object. This problem has been studied mainly from the control theory point of view, providing robust control to cooperating MAVs using the dynamics of the whole system. Real applications, however, require operating in unknown environments with obstacles, which needs motion planning. In this paper, we propose a novel motion planning method for multiple MAVs operating in unknown environments. The proposed work is based on the Sensor-based Random Trees method (SRT), which was originally intended for exploration of unknown environments. We extend the method for online path planning of multi MAVs. In the proposed method, each MAV makes a motion plan and exchanges key waypoints with other MAVs to ensure that their mutual positions satisfy the mission constraints. The performance of the method is demonstrated in various simulation experiments.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20204 - Robotics and automatic control
Návaznosti výsledku
Projekt
<a href="/cs/project/GJ19-22555Y" target="_blank" >GJ19-22555Y: Vzorkovací techniky plánování pohybu a akcí s využitím aproximačních řešení</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
International Conference on Unmanned Aircraft Systems (ICUAS)
ISBN
978-1-7281-4278-4
ISSN
2373-6720
e-ISSN
2575-7296
Počet stran výsledku
7
Strana od-do
661-667
Název nakladatele
IEEE
Místo vydání
Vancouver
Místo konání akce
Athens
Datum konání akce
1. 9. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000612041300089