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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20204 - Robotics and automatic control

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

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

ISBN

978-1-7281-4569-3

ISSN

2577-1655

e-ISSN

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Number of pages

8

Pages from-to

4373-4380

Publisher name

IEEE

Place of publication

Piscataway

Event location

Bari

Event date

Oct 6, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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