Mathematics and Computer Science
Volume 4, Issue 6, November 2019, Pages: 142-148
Received: Nov. 12, 2019;
Accepted: Dec. 13, 2019;
Published: Dec. 30, 2019
Views 519 Downloads 161
Wenliang Zhu, School of Mechanical and Ocean Engineering, Jiangsu Ocean University, Lianyungang, China
Yanzhe Ni, School of Mechanical and Ocean Engineering, Jiangsu Ocean University, Lianyungang, China
Tingbo Huang, Jiangsu Spacecraft Co., Ltd., Taizhou, China
Jiahao Han, Lianyungang Technical College, Lianyungang, China
Laser scanning ranging radar is an important tool for machines to perceive the surrounding environment and is widely used in the power, forestry, surveying and mapping industries. At present, the loading of oil and grain oil in our country generally adopts the way of manual loading. The loading arm is inserted into the tank of the tanker for refueling, and the loading operation is very frequent. In order to realize automatic control of grain and oil loading, radar is needed to assist the robot to locate the oil port of the tanker. In this paper, a 360-degree laser scanning ranging radar is used to collect characteristic data of oil hole of tanker for the first time in simulated environment. Cubic spline interpolation was used to smooth and correct the radar scan data. Based on the feature that the distance data of oil port will change rapidly, an edge feature recognition algorithm is proposed to screen and calculate the target point, and then convert it to cartesian coordinate point, which can be used as the positioning target of the robot unit of quantitative loading system. The experimental results show that the method can locate the center of the circle accurately and meet the requirement of feature recognition accuracy.
A Tanker Port Positioning Method of Quantitative Loading Automation, Mathematics and Computer Science.
Vol. 4, No. 6,
2019, pp. 142-148.
W. Y Wang. Research on Self-tracking of Driverless Vehicle Based on Lidar [D]. Hunan: Chang'an University, 2018, unpublished.
J. S. Peng, J. Xu, J. Li. Application of UAV Airborne LiDAR Technology to Electric Power Industry [J]. Bulletin of Surveying and Mapping, 2018, 493 (04): 152-154.
L. Chen, C. X. Xu, W. T. Gao, et al. Laser SLAM based obstacle avoidance technology of UAV for cable trench inspection [J]. High Voltage Apparatus, 2018, 54 (09): 209-213+220.
S. Qu, X. L. Zhang, C. H. Zhu, et al. Design and test of airborne LiDAR system for forest resources survey [J]. Journal of Northwest Forestry University, 2018, 33 (04): 175-182.
M. J Campbell, P. E. Dennison, A. T. Hudak, et al. Quantifying understory vegetation density using small-footprint airborne lidar [J]. Remote Sensing of Environment, 2018, 215: 330-342.
B. BIGDELI, H. A. AMIRKOLAEE, P. PAHLAVANI. DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm [J]. Remote Sensing of Environment, 2018, 216: 289-300.
C. C Zheng, Y. L. Liang. Application Analysis of LiDAR Data Based on UAV in the Highway Survey [J]. Geomatics & Spatial Information Technology, 2018, 41 (09): 216-218.
J. Roelens, S. Dondeyne, J. V. Orshoven, et al. Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds [J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 64-75.
A. Sánchez-Rodríguez, B. Riveiro, M. Soilán, et al. Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets [J]. Automation in Construction, 2018, 96: 171-179.
N. Polat, M. Uysal, A. S. Toprak An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area [J]. Measurement, 2015, 75: 50-56.
H. S. Zhang. The oil tanker loading facilities and dispatching system for petrochemical refinery [J]. Petrochemical Automation, 2016, 52 (03): 1-9.
G. T. Wang, K. Cao, H. Liu. SLAM method based on lidar and visual information fusion [J]. Journal of Shandong University of Technology: Natural Science Edition, 2019 (1): 9-13.
G. S. Ge, Z. L Li, K. Yang, et al. Research on robot particle filtering localization technology based on laser scanning ranging [J]. Journal of Sensors and Microsystems, 2017, 36 (12): 36-39.
J. W. Wang, L. H. Yang, S. D Shi, et al. Indoor integrated navigation algorithm based on workshop measurement positioning system and lidar [J]. Laser & Optoelectronics Progress, 2018, 55 (10): 172-178.
J. Cao, B. Zeng, J. Liu, et al. A novel relocation method for simultaneous localization and mapping based on deep learning algorithm [J]. Computers & Electrical Engineering, 2017, 63: 79-90.
E. Javanmardi, Y. Gu, M. Javanmardi, et al. Autonomous vehicle self-localization based on abstract map and multi-channel LiDAR in urban area [J]. IATSS Research, 2019, 43 (1): 1-13.
J. Roelens, B. Höfle, S. Dondeyne, et al. Drainage ditch extraction from airborne LiDAR point clouds [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146: 409-420.
Jia. Wang, L. Y. Zhang, C. D. Lü, et al. Tree species identification methods based on point cloud data using ground-based LiDAR [J]. Transactions of the Chinese Society of Agricultural Machinery, 2018, 49 (11): 180-188.
S. Du, Y. Zhang, Z. Zou, et al. Automatic building extraction from LiDAR data fusion of point and grid-based features [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 294-307.
J. Michael, D. John, A. Barry, et al. Detection of coarse woody debris using airborne light detection and ranging (LiDAR) [J]. Forest Ecology and Management, 2019, 433, 678-689.
M. Yadav, C. G. Charudatta. Extraction of power lines using mobile LiDAR data of roadway environment [J]. Remote Sensing Applications: Society and Environment, 2017, 8: 258-265.
H. Chen, D. X. Hua, Y. K. Zhang, et al. Interpolation method for lidar data visualization based on cubic spline function [J]. Chinese Journal of Scientific Instrument, 2013, 34 (04): 831-837.
H. Chen, D. X. Hua, Y. K. Zhang, et al. A method of vertical and horizontal plus cubic spline interp olation for Mie scattering lidar profile data [J]. Acta Phys. Sinica, 2014, 63 (15): 167-174.
D. X. ZHAO, L. L. Wang, Y. L. LI, et al. Extraction of preview elevation of road based on 3D sensor [J]. Measurement, 2018, 127: 104-114.