Modeling of working environment and coverage path planning method of combine harvesters

Lu, En; Xu, Lizhang; Li, Yaoming; Tang, Zhong; Ma, Zheng

doi:10.25165/j.ijabe.20201302.5210

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The main goal of agricultural machinery operation planning is to guide agricultural machinery to achieve full coverage of the field efficiently [14]. At present, the research focus in this field is mainly on route planning [15], headland turning, and obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

et al. 2023

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The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods.

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Section: Related Workmentioning

confidence: 99%

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

et al. 2023

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“…However, this method lacks flexibility for large, intelligent agricultural machines when turning on land promontories. Lu et al [24] used the Canny algorithm to fit and synthesize polygons on the boundary lines of farmland to be harvested in the extracted satellite map and optimized the path by an arc transition algorithm, which had good obstacle avoidance and turning effect. Post et al proposed real-time obstacle avoidance based on Hector SLAM technology with a fast and efficient planning effect [25].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Wang

Zhao

et al. 2023

Electronics

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This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future.

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“…[1][2][3] By the end of 2019, China's total mechanization share in arable land, sowing, and harvesting has reached 70%. [4][5][6] Intelligent agriculture is the general trend of agricultural development all over the world. Intelligent agricultural machinery and uncrewed operation are the prerequisites to realizing intelligent agriculture, and the auto-follow row technology is the primary technology for all-weather unmanned corn harvest.…”

Section: Introductionmentioning

confidence: 99%

Design of a deviation detection sensor and an auto-follow row system for corn harvesters

Dong

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The auto-follow row technology is the basic technology to realize the uncrewed operation of corn harvesters, which helps to improve the quality and efficiency of corn harvest. A deviation detection sensor and an auto-follow row system are designed to address the difficulty for corn harvesters to harvest the corn sowing with different row spacing. The auto-follow row system is comprised of the auto-follow row controller, two deviation detection sensors, sensors for acquiring the state of the corn harvester, and the hydraulic steering system. The deviation detection sensor uses the touch rod to detect the lateral deviation and sends it to the auto-follow row controller through the Controller Area Network (CAN) bus. The auto-follow row controller obtained the lateral deviation, calculated the target steering angle according to the pure pursuit method based on the yaw angle estimation model and fuzzy control model, and adjusted the posture of the harvester by controlling the steering actuator. Two types of corn harvesters were reformed and mounted the auto-follow row system for simulation and field tests. The average errors of the auto-follow row system in the simulation tests and the field tests were 0.040 m and 0.046 m, respectively. The relative deviations within ±0.075 m were 98.33% and 97.22%, respectively, which met the operating requirements of corn harvesters. The test results show that the auto-follow row system can be applied to many kinds of corn harvesters and provide a reference for designing a completely unmanned system for corn harvesters.

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Modeling of working environment and coverage path planning method of combine harvesters

Cited by 7 publications

References 11 publications

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Design of a deviation detection sensor and an auto-follow row system for corn harvesters

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