UAV route planning for active disease classification
Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vivaldini, Kelen C. T. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Autonomous robots - Springer US, 1994, 43(2018), 5 vom: 28. Juli, Seite 1137-1153 |
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| Übergeordnetes Werk: |
volume:43 ; year:2018 ; number:5 ; day:28 ; month:07 ; pages:1137-1153 |
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| DOI / URN: |
10.1007/s10514-018-9790-x |
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| 520 | |a Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. | ||
| 650 | 4 | |a Route planning | |
| 650 | 4 | |a UAV | |
| 650 | 4 | |a Bayesian optimization | |
| 650 | 4 | |a Rapidly-exploring random trees | |
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| 700 | 1 | |a Souza, Jefferson R. |4 aut | |
| 700 | 1 | |a Oliveira, Matheus D. |4 aut | |
| 700 | 1 | |a Ramos, Fabio T. |4 aut | |
| 700 | 1 | |a Wolf, Denis F. |4 aut | |
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10.1007/s10514-018-9790-x doi (DE-627)OLC2052753060 (DE-He213)s10514-018-9790-x-p DE-627 ger DE-627 rakwb eng 620 VZ Vivaldini, Kelen C. T. verfasserin (orcid)0000-0002-8802-196X aut UAV route planning for active disease classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. Route planning UAV Bayesian optimization Rapidly-exploring random trees Martinelli, Thiago H. aut Guizilini, Vitor C. aut Souza, Jefferson R. aut Oliveira, Matheus D. aut Ramos, Fabio T. aut Wolf, Denis F. aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 28. Juli, Seite 1137-1153 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:28 month:07 pages:1137-1153 https://doi.org/10.1007/s10514-018-9790-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 43 2018 5 28 07 1137-1153 |
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10.1007/s10514-018-9790-x doi (DE-627)OLC2052753060 (DE-He213)s10514-018-9790-x-p DE-627 ger DE-627 rakwb eng 620 VZ Vivaldini, Kelen C. T. verfasserin (orcid)0000-0002-8802-196X aut UAV route planning for active disease classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. Route planning UAV Bayesian optimization Rapidly-exploring random trees Martinelli, Thiago H. aut Guizilini, Vitor C. aut Souza, Jefferson R. aut Oliveira, Matheus D. aut Ramos, Fabio T. aut Wolf, Denis F. aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 28. Juli, Seite 1137-1153 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:28 month:07 pages:1137-1153 https://doi.org/10.1007/s10514-018-9790-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 43 2018 5 28 07 1137-1153 |
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10.1007/s10514-018-9790-x doi (DE-627)OLC2052753060 (DE-He213)s10514-018-9790-x-p DE-627 ger DE-627 rakwb eng 620 VZ Vivaldini, Kelen C. T. verfasserin (orcid)0000-0002-8802-196X aut UAV route planning for active disease classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. Route planning UAV Bayesian optimization Rapidly-exploring random trees Martinelli, Thiago H. aut Guizilini, Vitor C. aut Souza, Jefferson R. aut Oliveira, Matheus D. aut Ramos, Fabio T. aut Wolf, Denis F. aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 28. Juli, Seite 1137-1153 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:28 month:07 pages:1137-1153 https://doi.org/10.1007/s10514-018-9790-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 43 2018 5 28 07 1137-1153 |
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uav route planning for active disease classification |
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UAV route planning for active disease classification |
| abstract |
Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Eucalyptus represents one of the main sources of raw material in Brazil, and each year substantial losses estimated at $400 million occur due to diseases. The active monitoring of eucalyptus crops can help getting accurate information about contaminated areas, in order to improve response time. Unmanned aerial vehicles (UAVs) provide low-cost data acquisition and fast scanning of large areas, however the success of the data acquisition process depends on an efficient planning of the flight route, particularly due to traditionally small autonomy times. This paper proposes a single framework for efficient visual data acquisition using UAVs that combines perception, environment representation and route planning. A probabilistic model of the surveyed environment, containing diseased eucalyptus, soil and healthy trees, is incrementally built using images acquired by the vehicle, in combination with GPS and inertial information for positioning. This incomplete map is then used in the estimation of the next point to be explored according to a certain objective function, aiming to maximize the amount of information collected within a certain traveled distance. Experimental results show that the proposed approach compares favorably to other traditionally used route planning methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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UAV route planning for active disease classification |
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Martinelli, Thiago H. Guizilini, Vitor C. Souza, Jefferson R. Oliveira, Matheus D. Ramos, Fabio T. Wolf, Denis F. |
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Martinelli, Thiago H. Guizilini, Vitor C. Souza, Jefferson R. Oliveira, Matheus D. Ramos, Fabio T. Wolf, Denis F. |
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2024-07-03T16:24:24.643Z |
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