Wireless positioning and path tracking for a mobile platform in greenhouse

Yao, Lijian; Hu, Dong; Zhao, Chao; Yang, Zidong; Zhang, Zhao

doi:10.25165/j.ijabe.20211401.5627

Cited by 12 publications

(5 citation statements)

References 16 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, with the development of computer technology and edge computing equipment, the sensors used for greenhouse operating environment perception and localization have included ultrasonic technology (Palleja and Landers, 2017;Chen et al, 2018;Lao et al, 2021), ultra-wideband (UWB) technology (Hou et al, 2020;Yao et al, 2021), LiDAR technology (Chen et al, 2019;Zhang et al, 2020;Saha et al, 2022;Sun et al, 2022), and machine vision technology (Nissimov et al, 2015;Wang et al, 2022a;Wang et al, 2022b). Huang et al (2021) designed a robot localization system based on spread spectrum sounds for a greenhouse containing a strawberry ridge and achieved centimeter-level localization accuracy in a small greenhouse.…”

Section: Introductionmentioning

confidence: 99%

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

Tan,

Zhao,

Zhai

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945–5.301 m2/m3. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots.

show abstract

Section: Introductionmentioning

confidence: 99%

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

Tan,

Zhao,

Zhai

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The realization of path planning is a prerequisite for the application of autonomous navigation functions. Therefore, scholars worldwide have realized the application in different environments from the perspectives of communications technology, satellite navigation technology, image processing, visual recognition, SLAM (Simultaneous Localization and Mapping) technology, geo-fencing, and obstacle-avoidance processing [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhao

et al. 2023

Electronics

View full text Add to dashboard Cite

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.

show abstract

“…The UWB-based positioning system can carry out very high data transmission rate over short ranges and pinpoint the exact location in real-time. Yao et al [9] proposed and validated an automatic navigation method based on UWB positioning technology, using the time difference of arrival approach to track the UWB positioning. De Preter et al [10] developed an automatic strawberry-picking robot equipped with a UWB positioning system.…”

Section: Introductionmentioning

confidence: 99%

Precise visual positioning of agricultural mobile robots with a fiducial marker reprojection approach

Zhang,

Gong,

Sun

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Semi-structured greenhouse environment often features repetitive and weak texture, naturally bringing challenges to high-precision vision-based positioning techniques. This paper proposes a precise visual positioning method for agricultural mobile robots in the greenhouse, which improves their positioning accuracy via discriminatively minimizing fiducial marker reprojection errors. First, fiducial markers are used to enhance environment features, and a markers-based visual positioning task is formulated as a Perspective-n-Point (PnP) problem. The projection constraints of keypoints and the pose constraints of the coordinate systems provide a theoretical basis for robot positioning. Second, a reprojection error minimization approach is proposed by taking into account the markers’ distance and image noise. Far-away markers are more prone to greater observation errors than those close to the robots, the improved PnP algorithm considering distance weighting ensures higher positioning accuracy. Synthetic and field experiments are carried out to evaluate the performance of the proposed method. Synthetic experiments show that the rotation error and translation error of the proposed method are less than 0.7 degrees and 0.5% within a range of 12 m. The MAE and RMSE of field dynamic positioning experiments are 8.57cm and 8.59cm, respectively. Experimental results show that the proposed method is significantly better than traditional methods in dealing with distance-related noise at keypoints.

show abstract

Wireless positioning and path tracking for a mobile platform in greenhouse

Cited by 12 publications

References 16 publications

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

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

Precise visual positioning of agricultural mobile robots with a fiducial marker reprojection approach

Contact Info

Product

Resources

About