Obstacle avoidance for orchard vehicle trinocular vision system based on coupling of geometric constraint and virtual force field method

Liu, Siyao; Wang, Xiaoyan; Li, Sizhe; Xiangan, Chen; Zhang, Xuemin

doi:10.1016/j.eswa.2021.116216

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…If the number of pixels on the left side is greater than the number of pixels on the right side, the right side of the deviation path is determined, and then the fuzzy control system makes a shift to the left centerline. If the number of pixels on the right is greater than the number of pixels on the left, the left side of the deviation path is determined, and then the fuzzy control system will make an offset motion to the right center line [48]. In this paper, the fuzzy logic vision control system is designed with two inputs and two outputs.…”

Section: Fuzzy Logic Vision Control System Based On Visual Pixelsmentioning

confidence: 99%

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

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Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

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Section: Fuzzy Logic Vision Control System Based On Visual Pixelsmentioning

confidence: 99%

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

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“…Orchard weeds can compete aggressively with trees for resources, provide habitat for pests, and shade and stunt young trees (Riczu et al 2015). Poorly controlled orchard weeds can hamper operations of irrigation equipment and automated sprayers and interfere with the accurate placement of water, fertilizers, and pesticides (Brunharo et al 2020;Liu et al 2022). Therefore, intensive weed control is necessary in orchards both during establishment and throughout their life span.…”

Section: Weed Management In Orchardsmentioning

confidence: 99%

New directions in weed management and research using 3D imaging

et al. 2022

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Recent innovations in 3-D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2-D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3-D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatio-temporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seed bank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3-D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to IWM tactics, including weed-crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide resistance evolution, and improving environmental health.

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“…A previous study ( Chen et al, 2021 ) adopted a binocular vision method to build a simultaneous localization and mapping (SLAM), which realized the perception of orchard environment through vision and generated a detailed global map supporting long-term, flexible, and large-scale orchard picking. On the basis of binocular vision, the study discussed in Liu et al (2022) proposed a trinocular vision system for orchard vehicle based on a wide-angle camera and binocular stereo vision system, which finally realized orchard row detection and obstacle detection simultaneously. Besides, based on the vision technology, the study discussed in Li Y. et al (2020) proposed a visual perception method based on convolutional neural network, and realized obstacle detection and colligation avoidance in robot harvesters.…”

Section: Introductionmentioning

confidence: 99%

Design and development of orchard autonomous navigation spray system

Wang

Song

et al. 2022

Front. Plant Sci.

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Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system “perception-decision-control,” the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

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Obstacle avoidance for orchard vehicle trinocular vision system based on coupling of geometric constraint and virtual force field method

Cited by 12 publications

References 26 publications

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

New directions in weed management and research using 3D imaging

Design and development of orchard autonomous navigation spray system

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