Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering, Human Computer Interaction, Plant Sciences Institute

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

11-25-2019

Journal or Book Title

Journal of Agriculture and Food Research

First Page

100009

Research Focus Area(s)

Advanced Machinery Engineering and Manufacturing Systems

DOI

10.1016/j.jafr.2019.100009

Abstract

As an essential part of field coverage path planning process, mobile agricultural field equipment headland turning is a process that should be done in a manner that can maximise the equipment’s operational efficiency through minimising the time or travel distance during the turning. However, this headland turning trajectory optimisation task represents a challenging dynamic nonlinear optimisation problem which is difficult to solve by using traditional indirect numerical methods. In this research, we investigated the possibility of using direct numerical methods to solve such a nonlinear optimisation problem in a restricted parameter neighborhood with constraints. We developed the kinematic models of the tractor and the tractor-implement(s) systems and formulated their headland turning optimisation problems through incorporating their models and the operational constraints. A range of headland turning scenarios from symmetrical bulb turn to fishtail turn and to turns with single and double trailers. With integration of the tractor and trailer models and by implementing the optimization process with the TOMLAB/SNOPT software tool, results for diverse circumstances of the tractor/trailer headland turning scenarios were generated and illustrated in this paper.

Comments

This is a manuscript of an article published as Tu, Xuyong, and Lie Tang. "Headland Turning Optimisation for Agricultural Vehicles and Those with Towed Implements." Journal of Agriculture and Food Research (2019): 100009. DOI: 10.1016/j.jafr.2019.100009. Posted with permission.

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Open

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Published Version

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