An Orientation Navigation Approach Based on INS and Odometer Integration for Underground Unmanned Excavating Machine

High-precision navigation of the underground excavating machine is the core of realizing safe excavating and autonomous control, but it is difficult to realize high-precision navigation for the underground excavating machine. The integration of GNSS and inertial navigation system are generally adopted for the high-precision navigation of the ground equipment, which can provide high-precision navigation for a long time. However, the GNSS signals cannot be used in underground operation, so the navigation scheme applied to the ground cannot be used underground. At the same time, the velocity of the underground excavation machine is very slow and the operation time is very long, which makes it impossible to use pure inertial navigation scheme for navigation. Because the inertial navigation error accumulates with time, if the operation time is too long, the navigation accuracy cannot be guaranteed. The integration of inertial navigation and odometer is considered as a reliable scheme, but simulation and experimental analysis show that the traditional integration of inertial navigation and odometer cannot meet the operation requirements of the underground excavating machine in the presence of serious slipping and measurement noises. This paper proposes an integration of fully damped inertial navigation/odometer to provide long-term high-precision orientation navigation information for mining equipment. The advantage of the fully damped inertial navigation/odometer integration approach is that it can maintain high accuracy of orientation measurement in one week or longer under the condition of sensor drift error and odometer measurement error. Simulation and field tests are carried out to validate the performance of the proposed approach.

[1]  Arash Ebrahimabadi,et al.  Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM) , 2015 .

[2]  Ö. Aydan,et al.  Estimation of ground pressures on a shielded TBM in tunneling through squeezing ground and its possibility of jamming , 2019, Bulletin of Engineering Geology and the Environment.

[3]  Manuela Herman,et al.  Aided Navigation Gps With High Rate Sensors , 2016 .

[4]  Qingzhong Cai,et al.  An On-line Calibration Method of SINS/Odometer Integrated Navigation System , 2017, 2017 4th International Conference on Information Science and Control Engineering (ICISCE).

[5]  Yanbin Luo,et al.  Analysis of tunnel displacement accuracy with total station , 2016 .

[6]  Serdar Yasar,et al.  Vertical rock cutting rig (VRCR) suggested for performance prediction of roadheaders , 2019 .

[7]  Aminaton Marto,et al.  Predicting tunnel boring machine performance through a new model based on the group method of data handling , 2018, Bulletin of Engineering Geology and the Environment.

[8]  Wenbo Lu,et al.  Effects of Strain Energy Adjustment: A Case Study of Rock Failure Modes during Deep Tunnel Excavation with Different Methods , 2018 .

[9]  Rui Li,et al.  Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network , 2019, Sensors.

[10]  Sadi Evren Seker,et al.  Performance prediction of roadheaders using ensemble machine learning techniques , 2017, Neural Computing and Applications.

[11]  Lin Li,et al.  On Sigma-Point Update of Cubature Kalman Filter for GNSS/INS Under GNSS-Challenged Environment , 2019, IEEE Transactions on Vehicular Technology.

[12]  Elise Lachat,et al.  Investigation of a Combined Surveying and Scanning Device: The Trimble SX10 Scanning Total Station , 2017, Sensors.

[13]  Xing Huang,et al.  Application and prospect of hard rock TBM for deep roadway construction in coal mines , 2018 .

[14]  Gérard Lachapelle,et al.  Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation , 2018, Sensors.

[15]  Masoud Monjezi,et al.  Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques , 2017, Environmental Earth Sciences.

[16]  Gethin Wyn Roberts,et al.  Low-cost IMU and odometer tightly coupled integration with Robust Kalman filter for underground 3-D pipeline mapping , 2019, Measurement.

[17]  YUKUN ZHOU,et al.  Kinematic Measurement of the Railway Track Centerline Position by GNSS/INS/Odometer Integration , 2019, IEEE Access.

[18]  Muharrem Kemal Ozfirat,et al.  Numerical analysis of underground space and pillar design in metalliferous mine , 2017 .

[19]  Luis Mejías Alvarez,et al.  Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter , 2012, Int. J. Robotics Res..

[21]  Ning Zhang,et al.  Modified Q-index for prediction of rock mass quality around a tunnel excavated with a tunnel boring machine (TBM) , 2018, Bulletin of Engineering Geology and the Environment.

[22]  Karin Bäppler,et al.  New developments in TBM tunnelling for changing grounds , 2016 .

[23]  Yan Guo,et al.  Covert Spoofing Algorithm of UAV Based on GPS/INS-Integrated Navigation , 2019, IEEE Transactions on Vehicular Technology.

[24]  Yiming Li,et al.  Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion , 2017 .

[25]  Xiaodong Wang,et al.  Kalman-Filtering-Based In-Motion Coarse Alignment for Odometer-Aided SINS , 2017, IEEE Transactions on Instrumentation and Measurement.

[26]  D. Brox Technical considerations for the application of TBMs for mining projects , 2013 .

[27]  I. Lee,et al.  Soil Conditioning of Weathered Granite Soil used for EPB Shield TBM: A Laboratory Scale Study , 2019, KSCE Journal of Civil Engineering.

[28]  M. Horemuz,et al.  Optimal Vertical Placement of Total Station , 2018 .

[29]  Jian Wang,et al.  A Multi-Sensor Positioning Method-Based Train Localization System for Low Density Line , 2018, IEEE Transactions on Vehicular Technology.

[30]  Mohammad Omidalizarandi,et al.  Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station , 2018 .

[31]  Tao Zhang,et al.  A Hybrid IMM Based INS/DVL Integration Solution for Underwater Vehicles , 2019, IEEE Transactions on Vehicular Technology.

[32]  Maamar Bettayeb,et al.  Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter , 2017, International Journal of Automation and Computing.

[33]  Haijian Xue,et al.  In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding , 2017 .

[34]  Paul O'Leary,et al.  Robust machine vision based displacement analysis for tunnel boring machines , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[35]  Eun-Hwan Shin,et al.  Navigation kalman filter design for pipeline pigging , 2005 .

[36]  Kui Li,et al.  Study on Integration of FOG Single-Axis Rotational INS and Odometer for Land Vehicle , 2018, IEEE Sensors Journal.