Abstract
The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.
Highlights
Coverage path planning (CPP) refers to a kind of static global path planning in which a robot can completely cover the entire work area through autonomous tasks [1]
In the agricultural production field, CPP schemes are mainly to solve actual field production needs: on the one hand, demand is the result of agricultural machinery modernization; on the other hand, it is a combination of problems arising from the natural environment and agricultural system. us, the CPP technology in the field of agriculture has particularity and complexity
In terms of the details of the two-dimensional farmland CPP algorithm, Jin and Tang [8] and Meng et al [13], respectively, analysed the cost of various headland turning methods and turning type decisions. erefore, considering the actual operation situation and operation requirements from different perspectives, the planning results obtained are only a certain aspect of field operations to achieve the optimization of the two-dimensional farmland CPP scheme
Summary
Coverage path planning (CPP) refers to a kind of static global path planning in which a robot can completely cover the entire work area through autonomous tasks [1]. Hameed et al [14, 15] first studied the problem of large-scale farmland CPP with three-dimensional terrain based on a genetic algorithm from the perspective of energy consumption and proposed a “side-to-side,” three-dimensional coverage method for the problem of skips/ overlaps of the operating area by agricultural machinery. Both Jin’s team and Dogru’s team [16, 17] divided the threedimensional farmland into plane areas and slope areas for zoning research. If we ignore this aspect, it will cause the decrease of mechanical operation efficiency and the increase of energy consumption, which will increase the cost of mechanized operation for farmers. erefore, it is reasonable and positive to evaluate the planned path in the field from the perspective of energy consumption
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