Navigation and Control Problems in Precision Farming

Rapoport, Lev; Generalov, A. A.; Shavin, M. Yu.; Tormagov, Timofey

doi:10.23919/icins43216.2021.9470810

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…This approach assumes that the tracks are run sequentially, such that the field is processed from one side to the other. This approach may not be the optimal solution for fields of complex shapes with internal or even substantially curved boundaries (Rapoport et al, 2021). The problem of the optimal order of guidance tracks is another topic, the aforementioned route planning, that is not covered in the scope of this paper.…”

Section: Cost Functionsmentioning

confidence: 99%

“…Despite its simplicity, this approach has some limitations, which are discussed in Rapoport et al (2021). The curvature of the guidance tracks produced by this method is not constant, which can lead to singularities in the tracks, as already mentioned in the case for the headland tracks in Section 2.…”

Section: Curved Guidance Tracksmentioning

confidence: 99%

“…To comply with the constraint, either uncovered or overlapping areas are produced by the machine while traversing the path. The approach presented in Rapoport et al (2021) allows but aims to minimize the total overlap of adjacent swaths while ensuring complete coverage and maintaining the maximum curvature constraint on the tracks. This is achieved by solving an optimization problem, formulated as an Second Order Cone program (SOCP), for which efficient computational solving methods exist.…”

Section: Curved Guidance Tracksmentioning

confidence: 99%

“…Appearence of uncovered areas (gray) in case of a maximum curvature constraint, adapted from Rapoport et al (2021). …”

Section: Curved Guidance Tracksmentioning

confidence: 99%

“…Although we concentrate on guidance track generation in this review, a complete picture of the three‐step process is nevertheless necessary when the reader is faced with her/his first CCPP assignment. Therefore we direct the reader to references (Bochtis & Vougioukas, 2008; Hameed et al, 2011; Utamima & Djunaidy, 2021; Utamima et al, 2022, 2018) to get an overview on route planning techniques, and to (Backman et al, 2015; Fraichard & Scheuer, 2004; Höffmann, Patel, et al, 2022; Rapoport et al, 2021; Ravankar et al, 2018) to tackle the path smoothing step.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal guidance track generation for precision agriculture: A review of coverage path planning techniques

Höffmann,

Patel,

Büskens

2024

Journal of Field Robotics

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The Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e., the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in‐depth review of optimization‐based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for the generation of guidance tracks for arbitrarily shaped two‐dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks. Finally, this survey serves as an introductory guide for research and practitioners to solve the CCPP problem effectively and efficiently.

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Section: Cost Functionsmentioning

confidence: 99%

Section: Curved Guidance Tracksmentioning

confidence: 99%

Section: Curved Guidance Tracksmentioning

confidence: 99%

“…Appearence of uncovered areas (gray) in case of a maximum curvature constraint, adapted from Rapoport et al (2021). …”

Section: Curved Guidance Tracksmentioning

confidence: 99%