Coverage Optimization in the Cooperative Surveillance Task using Multiple Micro Aerial Vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336539" target="_blank" >RIV/68407700:21230/19:00336539 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8914330" target="_blank" >https://ieeexplore.ieee.org/document/8914330</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC.2019.8914330" target="_blank" >10.1109/SMC.2019.8914330</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Coverage Optimization in the Cooperative Surveillance Task using Multiple Micro Aerial Vehicles

Popis výsledku v původním jazyce

In the task of cooperative surveillance using Micro Aerial Vehicles (MAVs), MAVs cooperatively observe a given set of Areas of Interest (AoI). The missions are usually prepared in a decoupled manner: first, the sensing locations are found, followed by computations of the trajectories assuming GPS-based localization. The precision of GPS may, however, be insufficient to keep the MAVs in compact groups, which may lead to mutual collisions. To avoid the collisions between MAVs, a camera-based on-board localization has to be used. This however requires to maintain positions of the team members in the given range to enable reliable on-board localization (each MAV has to be visible from other ones). The task of the mission planning is to find an appropriate distribution of MAVs above AoIs together with feasible trajectories from a depot to reach these locations. The on-board localization constraints and MAV motion constraints have to be satisfied during the entire mission. We propose a modification of RRT (Rapidly Exploring Random Tree) for this mission planning. The algorithm first explores the state space to find suitable sensing locations together with feasible trajectories towards them. Then, the sensing locations are optimized using Particle Swarm optimization (PSO). The proposed method has been verified in numerous simulations and outdoor experiments. The achieved results exhibit significantly better performance in terms of lower computational power and complexity of solved scenarios than the state-of-the-art solutions.

Název v anglickém jazyce

Coverage Optimization in the Cooperative Surveillance Task using Multiple Micro Aerial Vehicles

Popis výsledku anglicky

In the task of cooperative surveillance using Micro Aerial Vehicles (MAVs), MAVs cooperatively observe a given set of Areas of Interest (AoI). The missions are usually prepared in a decoupled manner: first, the sensing locations are found, followed by computations of the trajectories assuming GPS-based localization. The precision of GPS may, however, be insufficient to keep the MAVs in compact groups, which may lead to mutual collisions. To avoid the collisions between MAVs, a camera-based on-board localization has to be used. This however requires to maintain positions of the team members in the given range to enable reliable on-board localization (each MAV has to be visible from other ones). The task of the mission planning is to find an appropriate distribution of MAVs above AoIs together with feasible trajectories from a depot to reach these locations. The on-board localization constraints and MAV motion constraints have to be satisfied during the entire mission. We propose a modification of RRT (Rapidly Exploring Random Tree) for this mission planning. The algorithm first explores the state space to find suitable sensing locations together with feasible trajectories towards them. Then, the sensing locations are optimized using Particle Swarm optimization (PSO). The proposed method has been verified in numerous simulations and outdoor experiments. The achieved results exhibit significantly better performance in terms of lower computational power and complexity of solved scenarios than the state-of-the-art solutions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

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

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

ISBN

978-1-7281-4569-3

ISSN

2577-1655

e-ISSN

—

Počet stran výsledku

8

Strana od-do

4373-4380

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Bari

Datum konání akce

6. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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