Auto-steering based precise coordination method for in-field multi-operation of farm machinery

Wang, Jie; Zhu, Yuting; Chen, Zhibo; Yang, Lili; Wu, Caicong

doi:10.25165/j.ijabe.20181105.3827

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…Facing the demand for efficient production in modern agriculture, intelligent robots have also attracted many attentions of agricultural researchers. As a new concept of agricultural machinery [7][8], agricultural robots have huge economic benefits in the field of agricultural production, and have broad market prospects. The timely development and development of a new generation for agricultural machinery represented by agricultural robots is of great significance for my country's transition to modern agriculture.…”

Section: Introductionmentioning

confidence: 99%

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

Li¹,

Yin²,

Lin³

2021

Preprint

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In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by vision system. Then it extracts the route of processed images based on improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments, and has good practicability and applicability.

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Section: Introductionmentioning

confidence: 99%

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

Li¹,

Yin²,

Lin³

2021

Preprint

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“…In terms of machinery collaboration, Wang et al [2] proposed that in-field logistics can be subdivided into three levels, i.e., multi-operation coordination, multi-machinery coordination, and multi-factor coordination [4]. Typical cases of multi-operation collaboration include the master-slave approach [5][6][7] and leader-follower approach [8][9][10], which illustrate multiple machines working in parallel for different but related operating tasks.…”

mentioning

confidence: 99%

“…For instance, Cao et al [24] built a Web-GIS (Geographic Information System) to realize collaboration among multiple agricultural machinery units, and the John Deere company utilizes an MTG (Modular Telematics Gateway) intelligent gateway and JDLink software to share guidance lines between different agricultural machinery units [25]. To support multi-operation collaboration, Wang et al [2] designed a cloud-terminal-based coordinative operating system for in-field fleet management. The authors gave the definition of an operating strip to replace the path [26], made use of intervals for strip identification, and proposed an algorithm for strip state updating between adjacent machinery operations.…”

mentioning

confidence: 99%

“…The authors gave the definition of an operating strip to replace the path [26], made use of intervals for strip identification, and proposed an algorithm for strip state updating between adjacent machinery operations. The research demonstrated how to calculate the waiting time or flow step accurately to reduce the delay between adjacent machinery operations [2,19]. However, the validation of the system took place through simulation, while the strip state-updating algorithm provided a feasible-yet not optimized-procedure.…”

mentioning

confidence: 99%

“…In order to optimize the state-updating algorithm and to validate the functionality of the work presented in Wang et al [2] under real field experimental conditions, a new collaborative operating system for fleet management (FMCOS) was redesigned and developed, which can support all the operating stages of a field crop and incorporates an upgraded algorithmic process for strip state updating that simplifies the calculation process. The algorithm was tested through simulations using Matlab (MathWorks ® ) before the development of FMCOS.…”

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confidence: 99%

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A Cloud-Based In-Field Fleet Coordination System for Multiple Operations

Chen

Wang

et al. 2020

Energies

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In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations-hoeing cultivation and seeding-as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode.

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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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Auto-steering based precise coordination method for in-field multi-operation of farm machinery

Cited by 5 publications

References 22 publications

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

A Cloud-Based In-Field Fleet Coordination System for Multiple Operations

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

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