This paper presents a novel approach for navigation in complex and unknown environments. At present, existing local path planners whose control output is the mapping of current sensor data have been widely studied. However, these methods cannot really solve the problem of being trapped by obstacles. We analyzed the reasons and made improvements, and finally our approach can avoid being trapped in complex environments. The proposed method is based on 2D LIDAR. A central part of the approach is finding out gaps in the environment by analyzing sensor data. Then, we choose one of the gaps we find as the sub-goal. Linear and angular velocities are provided by the approach considering nonholonomic mobile robots. The method does not rely on global planners and environment maps. Therefore, it has the advantages of low computational complexity and fast response, which is of great significance to robots with low computing power; it will also help to reduce the manufacturing cost of robots. In addition, simulations and real tests were performed using the Turtlebot2 robotic platform. Successful results are achieved in both simulations and experimental tests.
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http://dx.doi.org/10.3390/s20205802 | DOI Listing |
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Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies.
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January 2025
The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.
The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan.
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management.
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January 2025
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA.
Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Guangqiao Load, Shenzhen, 518132, CHINA.
To develop and validate a novel multidimensional readout method that significantly reduces the number of readout channels in PET detectors while maintaining high spatial and energy performance. Approach: We arranged a 3×3×4 SiPM array in multiple dimensions and employed row/column/layer summation with a resistor-based splitting circuit. We then applied denoising methods to enhance the peak-to-valley ratio in the decoding map, ensuring accurate crystal-position determination.
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