Navigation System of the Stacking Vehicle Based on Fuzzy Control and Laser Scanner

2017 ◽  
Author(s):  
Liqin Guo ◽  
Lixue Wang ◽  
Yongyi He ◽  
Sheng Bao
Keyword(s):  
Fuzzy Control ◽  
Navigation System ◽  
Laser Scanner

Accuracy improvement in 3D laser scanner based on dynamic triangulation for autonomous navigation system

2017 ◽  
Author(s):  
Oscar Real-Moreno ◽  
Julio C. Rodriguez-Quinonez ◽  
Oleg Sergiyenko ◽  
Luis C. Basaca-Preciado ◽  
Daniel Hernandez-Balbuena ◽  
...  
Keyword(s):  
Navigation System ◽  
Autonomous Navigation ◽  
Laser Scanner ◽  
Accuracy Improvement ◽  
Autonomous Navigation System

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

2018 ◽  
Vol 72 (3) ◽  
pp. 741-758 ◽  
Author(s):  
W.I. Liu ◽  
Zhixiong Li ◽  
Zhichao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Laser Scanning ◽  
Inertial Navigation System ◽  
Laser Scanner ◽  
Inertial Navigation ◽  
Integration Algorithm ◽  
Biased Estimation ◽  
State Estimations ◽  
Estimation And Filtering

A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

Development of a laser scanner-based navigation system for a combine harvester

2014 ◽  
Vol 7 (1) ◽  
pp. 7-13 ◽  
Author(s):  
Jongmin Choi ◽  
Xiang Yin ◽  
Liangliang Yang ◽  
Noboru Noguchi
Keyword(s):  
Navigation System ◽  
Laser Scanner ◽  
Combine Harvester

An enhanced inertial navigation system based on a low-cost IMU and laser scanner

2012 ◽  
Author(s):  
Hyung-Soon Kim ◽  
Seung-Ho Baeg ◽  
Kwang-Woong Yang ◽  
Kuk Cho ◽  
Sangdeok Park
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Laser Scanner ◽  
Inertial Navigation

scholarly journals Development of a Laser Scanner-based Navigation System for a Combine Harvester

2013 ◽  
Vol 46 (18) ◽  
pp. 103-108 ◽  
Author(s):  
Jongmin CHOI ◽  
Xiang Yin ◽  
Noboru Noguchi
Keyword(s):  
Navigation System ◽  
Laser Scanner ◽  
Combine Harvester

scholarly journals Performance Analysis of Adaptive Neuro Fuzzy Inference System Control for MEMS Navigation System

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ling Zhang ◽  
Jianye Liu ◽  
Jizhou Lai ◽  
Zhi Xiong
Keyword(s):  
Fuzzy Control ◽  
High Precision ◽  
Navigation System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Inference System ◽  
Neuro Fuzzy

Characterized by small volume, low cost, and low power, MEMS inertial sensors are widely concerned and applied in navigation research, environmental monitoring, military, and so on. Notably in indoor and pedestrian navigation, its easily portable feature seems particularly indispensable and important. However, MEMS inertial sensor has inborn low precision and is impressionable and sometimes goes against accurate navigation or even becomes seriously unstable when working for a period of time and the initial alignment and calibration are invalid. A thought of adaptive neuro fuzzy inference system (ANFIS) is relied on, and an assistive control modulated method is presented in this paper, which is newly designed to improve the inertial sensor performance by black box control and inference. The repeatability and long-time tendency of the MEMS sensors are tested and analyzed by ALLAN method. The parameters of ANFIS models are trained using reasonable fuzzy control strategy, with high-precision navigation system for reference as well as MEMS sensor property. The MEMS error nonlinearity is measured and modulated through the peculiarity of the fuzzy control convergence, to enhance the MEMS function and the whole MEMS system property. Performance of the proposed model has been experimentally verified using low-cost MEMS inertial sensors, and the MEMS output error is well compensated. The test results indicate that ANFIS system trained by high-precision navigation system can efficiently provide corrections to MEMS output and meet the requirement on navigation performance.

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