Design and testing of a heavy-duty platform for autonomous navigation in kiwifruit orchards

Jones, Malcolm; Bell, Jamie; Dredge, Daniel; Seabright, Matthew; Scarfe, Alistair J.; Duke, Mike; MacDonald, Bruce A.

doi:10.1016/j.biosystemseng.2019.08.019

Cited by 37 publications

(25 citation statements)

References 5 publications

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“…In this section, we introduced the advantages of visual perception in agricultural intelligence, and introduced agricultural robots, crop pest and disease monitoring in detail which are more representative in the application of visual perception technology. In addition to these two parts, the application of machine vision in agricultural intelligence also includes agricultural product quality inspection [62], crop healthy growth monitoring [63], agricultural vehicle visual navigation [64], and UAV farmland information monitoring [65], which are shown in Fig. 3.…”

Section: B Intelligent Agricultural Productionmentioning

confidence: 99%

“…Agricultural robot [49]- [56] Disease and pest monitoring [46], [57]- [61] Agricultural product quality inspection [62] Crop healthy growth monitoring [63] Agricultural vehicle visual navigation [64] UAV farmland information monitoring [65] Object segmentation [51] Binocular-based 3D information acquisition [51] Object recognition [53], [57] Image enhancement [53] Improved vision sensor [60], [61] Intelligent Driving…”

Section: A Vision Acquisitionmentioning

confidence: 99%

“…Transportation [70] Military Agriculture [64] Object segmentation [82]- [87], [144] Multi-source information fusion [88] SLAM [88] Reinforcement learning [90] CNN-based object detection [71]- [73], 3D object detection [77], [78], object segmentation [74], [76], [91]- [93] Deviation warning [75] Map 3D reconstruction [79] Object ranging [80] Image Synthesis Generative adversarial networks (GAN) have made great progress in generating images. It can learn its distribution based on target data and does not need to infer hidden variables during training.…”

Section: A Vision Acquisitionmentioning

confidence: 99%

See 2 more Smart Citations

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

195

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Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

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Section: B Intelligent Agricultural Productionmentioning

confidence: 99%

Section: A Vision Acquisitionmentioning

confidence: 99%

Section: A Vision Acquisitionmentioning

confidence: 99%

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Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

195

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“…Therefore, global positioning sensors are used in automatic navigation of agricultural vehicles based on GNSS Positioning and heading compass technologies [11][12][13][14] .…”

Section: Introduction mentioning

confidence: 99%

Development of autonomous navigation controller for agricultural vehicles

Yin

Wang

Chen

et al. 2020

International Journal of Agricultural and Biological Engineering

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Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on the driving skills of the operator. Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades, which contributes considerably to straight-line navigation. The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three wheel-type vehicles were used as the test platform featuring automatic steering, hydrostatic transmission and speed control, which included a rice transplanter, a high-clearance sprayer and a tractor. A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble. A path planning method was proposed to create a straight-line reference path by giving two points, and the target path was determined according to the vehicle initial status and working assignment. Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland. The navigation controller hardware was fabricated for program execution, data processing and information communication with peripherals. A human-machine interface was designed for the operator to complete basic setting, path planning and navigation control by providing controls. Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland. Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation. The lateral error in straight-line tracking was no more than 6 cm, 6 cm and 5 cm for the rice transplanter, the high-clearance sprayer and the tractor, respectively. And the maximum lateral RMS error was 3.10 cm, 4.75 cm, 2.21 cm in terms of straight-line tracking, which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles.

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“…Interests of applying LiDAR in agriculture was sparked due to the reduced sensor price in recent years. In case of row-following tasks, the use of single-layer LiDAR sensors was reported in orchard navigation (Bergerman et al, 2015;Jones et al, 2019) and in less structured crop fields . In these applications, the general strategy was to place a single-layer LiDAR sensor levelly and close to the ground, aiming at the tree trunks or the crop stalks.…”

Section: Related Workmentioning

confidence: 99%

Navigation control of a robotic vehicle for field-based phenotyping

Gai¹

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Design and testing of a heavy-duty platform for autonomous navigation in kiwifruit orchards

Cited by 37 publications

References 5 publications

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Development of autonomous navigation controller for agricultural vehicles

Navigation control of a robotic vehicle for field-based phenotyping

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