Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations

Han, Xiongzhe; Kim, Hak-Jin; Jeon, Chan Woo; Kim, Jung Hun

doi:10.1007/s42853-019-00035-9

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…An X-turn in the headland consists of a ≃90° turn while moving forward, then straight back, and a ≃90° turn back while moving forward. [15,24,25]. At the end of the first turn, the tractor stops, moves backwards, and turns again to begin a new furrow parallel to the master line.…”

Section: Rear Lift 7121 Kgmentioning

confidence: 99%

“…Soil penetration resistance of 1.02-1.24 MPa prevailed at a depth of 15 cm in all field test plots measured at various locations. Measurements were performed An X-turn in the headland consists of a 90 • turn while moving forward, then straight back, and a 90 • turn back while moving forward [15,24,25]. At the end of the first turn, the tractor stops, moves backwards, and turns again to begin a new furrow parallel to the master line.…”

Section: Rear Lift 7121 Kgmentioning

confidence: 99%

“…When ploughing the soil, the work efficiency is determined by how much time is effectively travelled on the field plot; when the agricultural implement is in the working position, all turning manoeuvres in the headlands are not counted, as they reduce the work efficiency [12,15]. Mechanized tillage, such as ploughing, starts from the edge of a cultivated plot, lowers the implement into a working position, accelerates to the technological speed, drives across the field, then slows down the speed of the tractor and finally rotates the unit at the end of the field plot [2,12].…”

Section: Introductionmentioning

confidence: 99%

“…There are long-known simple methods, according to which the direction of travel during tillage is determined according to the longest edge of the field plot, and then continues as in the rectangular plot scheme [17,18]. Optimizing tillage routes can reduce differences in fuel consumption in field plots of the same area with different dimensions, but the actual measured fuel consumption per hectare will still vary here [15,19,20]. In addition, energy consumption in agricultural production is closely related to economic indicators of farm development [21].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions

Damanauskas,

Janulevičius

2023

AgriEngineering

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Climate change is linked to CO2 emissions, the reduction of which has become a top priority. In response to these circumstances, scientists must constantly develop new technologies that increase fuel efficiency and reduce emissions. Agriculture today is dominated by arable fields of various sizes, shapes, and dimensions, and to achieve fuel economy and environmental impact requirements, it is not enough to know only the principles of optimization of tillage processes; it is also necessary to understand the influence of field size and its shape and dimensions on tillage performance. The purpose of this research is to present a methodology that allows predicting tractor fuel demand and CO2 emissions per unit of ploughed area when ploughing field plots with different shapes and dimensions and to confirm a suitable variable for such a prediction. Theoretical calculations and experimental tests have shown that the field ploughing time efficiency coefficient is a useful metric for comparing field plots of different shapes and dimensions. This coefficient effectively describes tractor fuel consumption and CO2 emissions during ploughing operations on differently configured field plots. A reasonable method for calculating the real field ploughing time efficiency coefficient is based on field and tillage data and a practical determination method using tractor engine load reports. It was found that during the research, when ploughing six field plots of different shapes and dimensions, with an area of 6 ha, the field ploughing time efficiency coefficient varied from 0.68 to 0.82, and fuel consumption between 15.6 and 16.5 kg/ha. In the field plot of 6 ha, where the field ploughing time efficiency coefficient was 15% higher, the fuel consumption per unit area was lower by about 5.5%. The results of this study will help to effectively predict tillage time and tractor fuel consumption required for different field shapes and dimensions.

show abstract

Section: Rear Lift 7121 Kgmentioning

confidence: 99%

Section: Rear Lift 7121 Kgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions

Damanauskas,

Janulevičius

2023

AgriEngineering

View full text Add to dashboard Cite

show abstract

“…Vahdanjoo et al [3] studied the path optimality of farms with more than one depot location and reported the total travelled distances for each scenario. Han et al [4] compared the path efficiency for different headland turning patterns. Santos et al [5] compared the previous studies on path planning in agriculture fields.…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

et al. 2022

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As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.

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Autonomous paddy field puddling and leveling operations based on full-coverage path generation and tracking

Jeon,

Kim,

Yun

et al. 2023

Precision Agric

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Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations

Cited by 12 publications

References 24 publications

Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions

Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions

Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

Autonomous paddy field puddling and leveling operations based on full-coverage path generation and tracking

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