Research on the Shearer Positioning Method Based on SINS and LiDAR with Velocity and Absolute Position Constraints

Accurate positioning of the shearer with a strapdown inertial navigation system (SINS) is the key technology to realize the automation of the longwall face. Unfortunately, the existing positioning methods have a strong dependence on the attitude accuracy of the SINS. The position errors gradually increase with the drift of the SINS attitude. To reduce the dependence on the SINS attitude and further increase the shearer positioning accuracy, this paper proposes a positioning method based on SINS and light detection and ranging (LiDAR) with velocity and absolute position constraints. A Kalman filter (KF) model based on these constraints was established. Simulation analysis shows that the attitude calibration between the shearer body, SINS and LiDAR, and the initial attitude alignment of the SINS are the keys to determining the shearer positioning accuracy. Even if there are small horizontal bends in the running track of the shearer and the features have small horizontal errors, an excellent positioning effect can still be obtained. In addition, four cutting processes were simulated with a reciprocating travel of 44.6 m and an advance distance of 1.2 m. Compared with the relative positioning method, the positioning accuracy of the proposed method was improved by 37%, 63%, 76%, and 69% from the first to the fourth cutting cycle, respectively, calculated by spherical error probable (SEP) values, and positioning accuracy had a lower dependence on the installation deflection angles between the SINS, the LiDAR, and the SINS attitude accuracy.

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Underwater Terrain Positioning Method Using Maximum a Posteriori Estimation and PCNN Model

Journal of Navigation ◽

10.1017/s0373463319000067 ◽

2019 ◽

Vol 72 (5) ◽

pp. 1233-1253 ◽

Cited By ~ 1

Author(s):

Pengyun Chen ◽

Pengfei Zhang ◽

Teng Ma ◽

Peng Shen ◽

Ye Li ◽

...

Keyword(s):

Inertial Navigation System ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

A Posteriori ◽

Positioning Accuracy ◽

Acoustic Arrays ◽

Positioning Method ◽

Anchor Points ◽

Underwater Navigation ◽

Posteriori Estimation

Conventional underwater navigation and positioning methods for Autonomous Underwater Vehicles (AUVs) either require the installation of acoustic arrays, which make AUVs less independent, or result in cumulative errors. This paper proposes an Underwater Terrain Positioning Method (UTPM) using Maximum a Posteriori (MAP) estimation and a Pulse Coupled Neural Network (PCNN) model for highly accurate navigation by AUVs. The PCNN model is used as a secondary discriminant to effectively identify pseudo-anchor points in flat terrain feature areas and to find the true positioning point, which significantly improves the matching positioning accuracy in these areas. Simulation results show that the proposed method effectively corrects Inertial Navigation System (INS) cumulative errors and has high matching positioning accuracy, which satisfy the requirements of AUV underwater navigation and positioning.

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Mobile robot positioning algorithm based on Kalman filtering method in network environment

MATEC Web of Conferences ◽

10.1051/matecconf/202032703005 ◽

2020 ◽

Vol 327 ◽

pp. 03005

Author(s):

Shuang Zhang

Keyword(s):

Mobile Robot ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Measurement Unit ◽

Relative Positioning ◽

Positioning Accuracy ◽

Reference Information ◽

Absolute Positioning ◽

Positioning Method

Positioning is the basic link in a multi-mobile robot control system, and is also a problem that must be solved before completing a specified task. The positioning method can be generally divided into relative positioning and absolute positioning. Absolute positioning method refers to that the robot calculates its current position by acquiring the reference information of some known positions in the outside world, calculating the relationship between itself and the reference information. Absolute positioning generally adopts methods based on beacons, environment map matching, and visual positioning. The relative positioning method mainly uses the inertial navigation system INS. The inertial navigation system directly fixes the inertial measurement unit composed of the gyroscope and the accelerometer to the target device, and uses the inertial devices such as the gyroscope and the accelerometer to measure the triaxial angular velocity and The three-axis acceleration information is measured and integrated, and the mobile robot coordinates are updated in real time. Combined with the initial inertial information of the target device, navigation information such as the attitude, speed, and position of the target device is obtained through integral operation [1-2]. The inertial navigation system does not depend on external information when it is working, and is not easily damaged by interference. As an autonomous navigation system, it has the advantages of high data update rate and high short-term positioning accuracy [3]. However, under the long-term operation of inertial navigation, due to the cumulative error of integration, the positioning accuracy is seriously degraded, so it is necessary to seek an external positioning method to correct its position information [4]

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A LiDAR-Aided Inertial Positioning Approach for a Longwall Shearer in Underground Coal Mining

Mathematical Problems in Engineering ◽

10.1155/2021/6616090 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jiangtao Zheng ◽

Sihai Li ◽

Nan Li ◽

Qiangwen Fu ◽

Shiming Liu ◽

...

Keyword(s):

Inertial Navigation System ◽

Longwall Mining ◽

Three Dimensional ◽

Underground Coal Mining ◽

Positioning Accuracy ◽

Selection Scheme ◽

Position Accuracy ◽

Position Errors ◽

Gyro Drift ◽

Hydraulic Supports

The absolute three-dimensional position of a longwall shearer is fundamental to longwall mining automation. The positioning of the longwall shearer is usually realized by the inertial navigation system (INS) and odometer (OD). However, the position accuracy of this positioning approach gradually decreases over time due to the gyro drift. To further increase the positioning accuracy of the shearer, this paper proposes a positioning approach based on the INS and light detection and ranging (LiDAR). A Kalman filter (KF) model based on the observation provided by detecting hydraulic supports which are part of the longwall face, using the LiDAR, is established. The selection scheme of the point features is studied through a set of simulations. In addition, compared with that of the approach based on the INS and OD, the shearer positioning accuracy obtained using the proposed approach is higher. When the shearer moves along a 350 m track for 6 cutting cycles and lasts about 7.1 h, both east and north position errors can be maintained within 0.2 m and the height error within 0.1 m.

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Bionic Integrated Positioning Mechanism Based on Bioinspired Polarization Compass and Inertial Navigation System

Sensors ◽

10.3390/s21041055 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1055

Author(s):

Qingyun Zhang ◽

Jian Yang ◽

Panpan Huang ◽

Xin Liu ◽

Shanpeng Wang ◽

...

Keyword(s):

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

System Model ◽

Degree Of Polarization ◽

Positioning System ◽

Integration Model ◽

Position Errors ◽

Model Combining ◽

Polarization Compass

In this paper, to address the problem of positioning accumulative errors of the inertial navigation system (INS), a bionic autonomous positioning mechanism integrating INS with a bioinspired polarization compass is proposed. In addition, the bioinspired positioning system hardware and the integration model are also presented. Concerned with the technical issue of the accuracy and environmental adaptability of the integrated positioning system, the sun elevation calculating method based on the degree of polarization (DoP) and direction of polarization (E-vector) is presented. Moreover, to compensate for the latitude and longitude errors of INS, the bioinspired positioning system model combining the polarization compass and INS is established. Finally, the positioning performance of the proposed bioinspired positioning system model was validated via outdoor experiments. The results indicate that the proposed system can compensate for the position errors of INS with satisfactory performance.

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An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.245.323 ◽

2012 ◽

Vol 245 ◽

pp. 323-329 ◽

Cited By ~ 4

Author(s):

Muhammad Ushaq ◽

Jian Cheng Fang

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Sensor ◽

Inertial Navigation ◽

Measurement Unit ◽

Navigation Systems ◽

Integrated Navigation ◽

Efficient Manner ◽

Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

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Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽

10.1155/2018/3039061 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Lijun Song ◽

Zhongxing Duan ◽

Bo He ◽

Zhe Li

Keyword(s):

Neural Networks ◽

Kalman Filter ◽

Navigation System ◽

Inertial Navigation System ◽

Estimation Error ◽

Inertial Navigation ◽

Transfer Alignment ◽

The Neural Networks ◽

Initial Attitude ◽

Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

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A Mathematical Model and Error Analysis of Shearer Cutting Path Based on Its Attitude

Mathematical Problems in Engineering ◽

10.1155/2018/1790602 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8

Author(s):

Shi-bo Wang ◽

Shijia Wang ◽

Zhaoliang Ge

Keyword(s):

Mathematical Model ◽

Error Analysis ◽

Inertial Navigation System ◽

Coordinate Frame ◽

Medium Thickness ◽

Position Errors ◽

Working Face ◽

Coordinate Position ◽

Thin Seam ◽

Cutting Path

The horizon control system is the key technology in the automation of a shearer. The achievement of accurate shearer cutting path plays an important role for horizon control. A mathematical model of cutting path in the local geographic coordinate frame was built. Error analysis based on genetic algorithm (GA) was studied to guarantee the accuracy of the shearer cutting path. Parameters from a MG1000/2660-WD shearer and data from a working face were used to obtain the shearer cutting path with reference to the local geographic coordinate frame. Also, with error analysis based on GA, the desired sensors were chosen, which allowed coordinate position errors of a shearer’s cutting path to be less than 0.01 m. The desired accuracies of the inertial navigation system and encoders mounted on the different shearers used in thin seam, medium-thickness seam, and thick seam were calculated.

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Indoor Positioning Method Based on Infrared Vision and UWB Fusion

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012070 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012070

Author(s):

Qianrong Zhang ◽

Yi Li

Keyword(s):

Real Time ◽

Indoor Localization ◽

Low Cost ◽

Ultra Wideband ◽

Coverage Area ◽

Positioning Accuracy ◽

Mobile Vehicle ◽

Positioning Method ◽

Positioning Algorithm ◽

Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

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Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

Wireless Communications and Mobile Computing ◽

10.1155/2021/9717629 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Hongbin Pan ◽

Yang Xiang ◽

Jian Xiong ◽

Yifan Zhao ◽

Ziwei Huang ◽

...

Keyword(s):

Nearest Neighbor ◽

Detection Method ◽

Access Point ◽

Combination Method ◽

Data Set ◽

Positioning Accuracy ◽

Multipath Effects ◽

Positioning Method ◽

Fingerprint Database ◽

Mismatch Rate

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

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