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UAV route planning for active disease classification

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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.

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Notes

  1. In previous works (Souza et al. 2015), several different covariance functions were considered, and the Rational Quadratic produced better classification results.

  2. We employ line segments as the template for trajectory calculations, however Eq. 9 can be equally applied to any sort of curve, such as splines (Egerstedt and Martin 2001).

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Acknowledgements

The authors acknowledge by the CNPq Foundation (process 400699/2016-8), CAPES and FAPESP for the financial support. This research project was also supported by funding from the Faculty of Engineering & Information Technologies, The University of Sydney, under the Faculty Research Cluster Program. Lastly, Federal University of Uberlândia. Funding was provided by CNPq - Brazil (Grant Nos. 400.395/2014-2).

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Authors and Affiliations

  1. Department of Computer Science, Federal University of São Carlos (UFSCar), São Carlos, SP, Brazil

    Kelen C. T. Vivaldini

  2. Mobile Robotics Lab, Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, SP, Brazil

    Thiago H. Martinelli, Matheus D. Oliveira & Denis F. Wolf

  3. Australian Centre for Field Robotics, School of Information Technologies, University of Sydney, Sydney, Australia

    Vitor C. Guizilini & Fabio T. Ramos

  4. Department of Computer Science and Information Systems, Faculty of Computing (FACOM), Federal University of Uberlândia (UFU), Uberlândia, MG, Brazil

    Jefferson R. Souza

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  1. Kelen C. T. Vivaldini
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  3. Vitor C. Guizilini
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Correspondence to Kelen C. T. Vivaldini.

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Vivaldini, K.C.T., Martinelli, T.H., Guizilini, V.C. et al. UAV route planning for active disease classification. Auton Robot 43, 1137–1153 (2019). https://doi.org/10.1007/s10514-018-9790-x

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