Quadcopter Simulation Model for Research of Monitoring Tasks

Quadcopter Simulation Model for Research of Monitoring Tasks

The article describe a simulation of the drone designed to monitor a large area. Three scenarios are considered: detection of the fire source in the forest, detection of intruders in the forbidden territory and detection o their cars. Open source software is used for the simulation: robotic simulato...

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Main Authors: Artemii Zenkin, Ivan Berman, Kanstantsin Pachkouski, Igor Pantiukhin, Vyacheslav Rzhevskiy
Format: Article
Language:English
Published: FRUCT 2020-04-01
Series:Proceedings of the XXth Conference of Open Innovations Association FRUCT
Subjects:
unmanned aerial vehicles
wildfire monitoring
landfill monitoring
fire detection
human detection
car detection
Online Access:https://www.fruct.org/publications/fruct26/files/Zen.pdf
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spelling doaj-a74b6e7dde254046be29631521657e572020-11-25T03:53:56ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372020-04-0126144945710.23919/FRUCT48808.2020.9087391Quadcopter Simulation Model for Research of Monitoring TasksArtemii Zenkin0Ivan Berman1Kanstantsin Pachkouski2Igor Pantiukhin3Vyacheslav Rzhevskiy4ITMO University, RussiaITMO University, RussiaITMO University, RussiaITMO University, RussiaITMO University, RussiaThe article describe a simulation of the drone designed to monitor a large area. Three scenarios are considered: detection of the fire source in the forest, detection of intruders in the forbidden territory and detection o their cars. Open source software is used for the simulation: robotic simulator Gazebo, framework for robotic applications Robot Operating System, communication protocol for small unmanned vehicles MAVLink and autopilot system PX4. Iris+ quadcopter is chosen as a prototype for the simulation, its mathematical model is presented as well as the vision model based on the PX4FLOW monocular camera and optical flow. Algorithms for detecting objects of interest are described. As a result, the successful tests of simulation are presented, in which the classification and localization accuracy is tested.https://www.fruct.org/publications/fruct26/files/Zen.pdfunmanned aerial vehicleswildfire monitoringlandfill monitoringfire detectionhuman detectioncar detection
collection DOAJ
language English
format Article
sources DOAJ
author Artemii Zenkin
Ivan Berman
Kanstantsin Pachkouski
Igor Pantiukhin
Vyacheslav Rzhevskiy
spellingShingle Artemii Zenkin
Ivan Berman
Kanstantsin Pachkouski
Igor Pantiukhin
Vyacheslav Rzhevskiy
Quadcopter Simulation Model for Research of Monitoring Tasks
Proceedings of the XXth Conference of Open Innovations Association FRUCT
unmanned aerial vehicles
wildfire monitoring
landfill monitoring
fire detection
human detection
car detection
author_facet Artemii Zenkin
Ivan Berman
Kanstantsin Pachkouski
Igor Pantiukhin
Vyacheslav Rzhevskiy
author_sort Artemii Zenkin
title Quadcopter Simulation Model for Research of Monitoring Tasks
title_short Quadcopter Simulation Model for Research of Monitoring Tasks
title_full Quadcopter Simulation Model for Research of Monitoring Tasks
title_fullStr Quadcopter Simulation Model for Research of Monitoring Tasks
title_full_unstemmed Quadcopter Simulation Model for Research of Monitoring Tasks
title_sort quadcopter simulation model for research of monitoring tasks
publisher FRUCT
series Proceedings of the XXth Conference of Open Innovations Association FRUCT
issn 2305-7254
2343-0737
publishDate 2020-04-01
description The article describe a simulation of the drone designed to monitor a large area. Three scenarios are considered: detection of the fire source in the forest, detection of intruders in the forbidden territory and detection o their cars. Open source software is used for the simulation: robotic simulator Gazebo, framework for robotic applications Robot Operating System, communication protocol for small unmanned vehicles MAVLink and autopilot system PX4. Iris+ quadcopter is chosen as a prototype for the simulation, its mathematical model is presented as well as the vision model based on the PX4FLOW monocular camera and optical flow. Algorithms for detecting objects of interest are described. As a result, the successful tests of simulation are presented, in which the classification and localization accuracy is tested.
topic unmanned aerial vehicles
wildfire monitoring
landfill monitoring
fire detection
human detection
car detection
url https://www.fruct.org/publications/fruct26/files/Zen.pdf
work_keys_str_mv AT artemiizenkin quadcoptersimulationmodelforresearchofmonitoringtasks
AT ivanberman quadcoptersimulationmodelforresearchofmonitoringtasks
AT kanstantsinpachkouski quadcoptersimulationmodelforresearchofmonitoringtasks
AT igorpantiukhin quadcoptersimulationmodelforresearchofmonitoringtasks
AT vyacheslavrzhevskiy quadcoptersimulationmodelforresearchofmonitoringtasks
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