TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways
Autonomous localization and operation of tunnel boring machines in perceptually degraded roadways is essential for intelligent upgrading of tunneling. Tunneling robots are far less intelligent than anticipated owing to the darkness, dust, vibration, and geometrically degraded roadways. We presented...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10143640/ |
| _version_ | 1797296353436499968 |
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| author | Yu Liu Hongwei Wang Lei Tao |
| author_facet | Yu Liu Hongwei Wang Lei Tao |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | Autonomous localization and operation of tunnel boring machines in perceptually degraded roadways is essential for intelligent upgrading of tunneling. Tunneling robots are far less intelligent than anticipated owing to the darkness, dust, vibration, and geometrically degraded roadways. We presented a multi-engine state estimation method for mapping and localizing tunnel boring machines (TBM-MSE). TBM-MSE designed a novel inertial enhancement model that maintains a global consistent posture in violent vibrations. TBM-MSE constructed lever arm error compensation terms for the total station and inertial component to improve the accuracy of position constraints. Meanwhile, the multi-engine framework of the TBM-MSE adaptively adjusts the weight of the multiple sensors in dusty environments. TBM-MSE was tested on dust-free and dusty roadways. The results demonstrate that TBM-MSE was more suitable for the state estimation of tunnel boring machines than LINS and RRR-MF. TBM-MSE estimation accuracy meets actual excavation requirements. In addition, the ablation experiments further confirm the effectiveness of inertial enhancement in handling perceptually degraded environments. |
| first_indexed | 2024-03-07T22:03:25Z |
| format | Article |
| id | doaj.art-9fbeee10f81b4edcaf7c52b01f36d9bb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T22:03:25Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9fbeee10f81b4edcaf7c52b01f36d9bb2024-02-24T00:00:13ZengIEEEIEEE Access2169-35362023-01-0111559785598910.1109/ACCESS.2023.328260610143640TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded RoadwaysYu Liu0https://orcid.org/0000-0002-2332-7909Hongwei Wang1Lei Tao2College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaShanxi Engineering Research Center for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan, ChinaAutonomous localization and operation of tunnel boring machines in perceptually degraded roadways is essential for intelligent upgrading of tunneling. Tunneling robots are far less intelligent than anticipated owing to the darkness, dust, vibration, and geometrically degraded roadways. We presented a multi-engine state estimation method for mapping and localizing tunnel boring machines (TBM-MSE). TBM-MSE designed a novel inertial enhancement model that maintains a global consistent posture in violent vibrations. TBM-MSE constructed lever arm error compensation terms for the total station and inertial component to improve the accuracy of position constraints. Meanwhile, the multi-engine framework of the TBM-MSE adaptively adjusts the weight of the multiple sensors in dusty environments. TBM-MSE was tested on dust-free and dusty roadways. The results demonstrate that TBM-MSE was more suitable for the state estimation of tunnel boring machines than LINS and RRR-MF. TBM-MSE estimation accuracy meets actual excavation requirements. In addition, the ablation experiments further confirm the effectiveness of inertial enhancement in handling perceptually degraded environments.https://ieeexplore.ieee.org/document/10143640/Multi-engine estimationinertial enhancementmultisensorperceptually degraded roadwaystunnel boring machine |
| spellingShingle | Yu Liu Hongwei Wang Lei Tao TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways IEEE Access Multi-engine estimation inertial enhancement multisensor perceptually degraded roadways tunnel boring machine |
| title | TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways |
| title_full | TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways |
| title_fullStr | TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways |
| title_full_unstemmed | TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways |
| title_short | TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways |
| title_sort | tbm mse a multi engine state estimation based on inertial enhancement for tunnel boring machines in perceptually degraded roadways |
| topic | Multi-engine estimation inertial enhancement multisensor perceptually degraded roadways tunnel boring machine |
| url | https://ieeexplore.ieee.org/document/10143640/ |
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