High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

被引:2
作者
Kaewkorn, Supod [1 ]
Ekpanyapong, Mongkol [1 ]
Thamma, Ukrit [2 ]
机构
[1] Sch Engn & Technol, Asian Inst Technol, Khlong Nueng 12120, Thailand
[2] King Mongkuts Univ Technol North Bangkok, Coll Ind Technol, Bangkok 10800, Thailand
关键词
Laser tracking system; localization; inertial measurement unit (IMU); sensor fusion; position control; RTK-GNSS; LOCALIZATION; FUSION; ATTITUDE;
D O I
10.1109/ACCESS.2021.3071554
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel, low-cost approach to high-accuracy robot localization for agricultural applications using an image-processing triple-laser-guided (TLG) system coupled with an inertial measurement unit (IMU) is presented in this paper. The TLG system consists of a laser-pointing unit (LPU) at the base station and a laser-target unit (LTU) at the mobile robot. The robot's XYZ position and heading are determined from the positions and the angles relative to the field of both LPU and LTU. The robot's roll and pitch are determined by the IMU sensor fusion with complementary filter. The IMU-coupled TLG system is demonstrated on an outdoor, 20 x 21 m flat field at various light intensities. The overall lateral and vertical accuracies of the IMU-coupled TLG system are 1.68 cm and 0.59 cm, respectively. The overall heading, roll, and pitch accuracies of the IMU-coupled TLG system are 0.90 degrees, 0.78 degrees, and 0.76 degrees, respectively. The lateral and heading accuracies of the IMU-coupled TLG system are found to be comparable to commercially available GNSS-INS systems from NovAtel and Trimble, while the total cost of the IMU-coupled TLG system is only a fraction of the total cost of the commercially available localization systems.
引用
收藏
页码:54325 / 54337
页数:13
相关论文
共 27 条
[1]   Attitude Determination and Localization of Mobile Robots Using Two RTK GPSs and IMU [J].
Aghili, Farhad ;
Salerno, Alessi .
2009 IEEE-RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, 2009, :2045-2052
[2]  
Bo S., 2020, 2020 CHIN CONTR DEC, P5409, DOI [10.1109/CCDC49329.2020.9164809, DOI 10.1109/CCDC49329.2020.9164809]
[3]   Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras [J].
Canedo-Rodriguez, A. ;
Alvarez-Santos, V. ;
Regueiro, C. V. ;
Iglesias, R. ;
Barro, S. ;
Presedo, J. .
INFORMATION FUSION, 2016, 27 :170-188
[4]   Precise and Robust RTK-GNSS Positioning in Urban Environments with Dual-Antenna Configuration [J].
Fan, Peirong ;
Li, Wenyi ;
Cui, Xiaowei ;
Lu, Mingquan .
SENSORS, 2019, 19 (16)
[5]   Ground-Texture-Based Localization for Intelligent Vehicles [J].
Fang, Hui ;
Yang, Ming ;
Yang, Ruqing ;
Wang, Chunxiang .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2009, 10 (03) :463-468
[6]   Monte Carlo localisation of a mobile robot using a Doppler-Azimuth radar [J].
Guan, Robin Ping ;
Ristic, Branko ;
Wang, Liuping ;
Evans, Rob .
AUTOMATICA, 2018, 97 :161-166
[7]  
Gui PF, 2015, C IND ELECT APPL, P1998, DOI 10.1109/ICIEA.2015.7334442
[8]   An Integrated GNSS/LiDAR-SLAM Pose Estimation Framework for Large-Scale Map Building in Partially GNSS-Denied Environments [J].
He, Guojian ;
Yuan, Xingda ;
Zhuang, Yan ;
Hu, Huosheng .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[9]  
Hoppenot P, 1997, INTELLIGENT COMPONENTS AND INSTRUMENTS FOR CONTROL APPLICATIONS 1997 (SICICA'97), P131
[10]   Integrated RTK/INS Navigation for Precision Agriculture [J].
Lan, Haiyu ;
Elsheikh, Mohamed ;
Abdelfatah, Walid ;
Wandan, Ahmed ;
El-Sheimy, Naser .
PROCEEDINGS OF THE 32ND INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2019), 2019, :4076-4086