Metric Map Generation for Autonomous Field Operations
by
Kun Zhou 1,2,*, Allan Leck Jensen 2, Dionysis Bochtis 2,3, Michael Nørremark 2, 
Dimitrios Kateris 3 and 
Claus Grøn Sørensen 2,*
Kun Zhou 1,2,*, Allan Leck Jensen 2, Dionysis Bochtis 2,3, Michael Nørremark 2, Dimitrios Kateris 3 and Claus Grøn Sørensen 2,*
1
Research & Advanced Engineering, Global Harvesting, AGCO A/S, Dronningborg Alle 2, 8930 Randers, Denmark
2
Department of Engineering, Aarhus University, Blichers Allé 20, P.O. Box 50, 8830 Tjele, Denmark
3
Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research & Technology Hellas—(CERTH), 6th km Charilaou-Thermi Rd, 57001 Thessaloniki, Greece
*
Authors to whom correspondence should be addressed.
Agronomy 2020, 10(1), 83; https://doi.org/10.3390/agronomy10010083
Received: 19 October 2019 / Revised: 2 January 2020 / Accepted: 6 January 2020 / Published: 7 January 2020
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Advanced systems for manned and/or agricultural vehicles—such as systems for auto-steering, navigation-adding, and autonomous route planning—require new capabilities in terms of the internal representation for the autonomous system of the working space; that is, the generation of a metric map that provides by numerical parameters any operation-related entity of the working space. In this paper, a real-time approach was developed for the generation of the field metric map, based on a row generation method (polygons-based geometry). The approach can deal with fields with or without in-field obstacles, where the generated field-work tracks can be either straight or curved. The functionality of the approach was demonstrated on 12 fields with different number of obstacles ranging from one to six. The test results showed that the computational times were in the range of 0.26–24.51 s. The presented tool brings a number of advancements on the process of generating a metric map for arable farming field operations, including the real-time generation feature, the potential to deal with multiple-obstacle areas, and the reduction in the overlapped area.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Zhou, K.; Jensen, A.L.; Bochtis, D.; Nørremark, M.; Kateris, D.; Sørensen, C.G. Metric Map Generation for Autonomous Field Operations. Agronomy 2020, 10, 83.
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