TBM-MSE: A Multi-Engine State Estimation Based on Inertial Enhancement for Tunnel Boring Machines in Perceptually Degraded Roadways

Abstract

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.

Keywords

• Multi-engine estimation
• inertial enhancement
• multisensor
• perceptually degraded roadways
• tunnel boring machine