Abstract
Field machines play an important role in the management of agricultural environments. Increasing use of automated machines in precision agriculture has gained significant attention of farmers and industries to minimize human work load to perform tasks such as land preparation, seeding, fertilizing, plant health monitoring and harvesting. Path planning is considered as a fundamental step for agricultural machines equipped with autonomous navigation system. For mountain vineyards, path planning is a big challenge due to terrain morphology and unstructured vineyards.
This paper proposes a workflow to generate an automatic coverage path plan for unmanned ground vehicles (UGVs) using georeferenced imagery taken by an unmanned aerial vehicle (UAV). First, image acquisition is performed over a vineyard to generate an orthomosaic and a digital surface model, which are then used to identify the vine rows and inter-row terrain. This information is then used by the algorithm to generate a path plan for UGV.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)
Oksanen, T., Visala, A.: Coverage path planning algorithms for agricultural field machines. J. Field Robot. 26(8), 651–668 (2009)
Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 718–724 (2004)
Hameed, I.A., Bochtis, D., Sørensen, C.A.: An optimized field coverage planning approach for navigation of agricultural robots in fields involving obstacle areas. Int. J. Adv. Robot. Syst. 10(5), 231 (2013)
Da Costa, J.P., et al.: Delineation of vine parcels by segmentation of high resolution remote sensed images. Precis. Agric. 8(1–2), 95–110 (2007)
Comba, L., et al.: Vineyard detection from unmanned aerial systems images. Comput. Electron. Agric. 114, 78–87 (2015)
Comba, L., et al.: Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture. Comput. Electron. Agric. 155, 84–95 (2018)
SistemaPiemonte. Carta dei paesaggi agrari e forestali 1:250.000. http://www.sistemapiemonte.it/eXoRisorse/dwd/servizi/Agricoltura/ServiziGeografici/note.pdf
Cina, A., et al.: Network real time kinematic (NRTK) positioning description, architectures and performances. In: Satellite Positioning-Methods, Models and Applications. InTech (2015)
Vivalda, C., et al.: Forest wildfire risk mapping and the influence of the weather and geo-morphological input data, p. 171, June 2017. https://doi.org/10.1201/9781315210469-145
Aicardi, I., et al.: Recent trends in cultural heritage 3D survey: the photogrammetric computer vision approach. J. Cult. Herit. 32, 257–266 (2018)
Grilli, E., Menna, F., Remondino, F.: A review of point clouds segmentation and classification algorithms. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 42, 339 (2017)
Antoniak, C.E.: Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Stat. 2, 1152–1174 (1974)
Kallenberg, O.: Foundations of Modern Probability. Springer, New York (2006)
Wagstaff, K., et al.: Constrained k-means clustering with background knowledge. In: ICML, vol. 1, pp. 577–584 (2001)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)
Acknowledgement
This work has been developed with the contribution of the Politecnico di Torino Interdepartmental Centre for Service Robotics PIC4SeR, https://pic4ser.polito.it and Azienda Agricola Ciabot, https://www.aziendaagricolaciabot.com/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zoto, J., Musci, M.A., Khaliq, A., Chiaberge, M., Aicardi, I. (2020). Automatic Path Planning for Unmanned Ground Vehicle Using UAV Imagery. In: Berns, K., Görges, D. (eds) Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980. Springer, Cham. https://doi.org/10.1007/978-3-030-19648-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-19648-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19647-9
Online ISBN: 978-3-030-19648-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)