Optimization Modeling for Attitude Measurement of a Tunnel Boring Machine


This paper presents a low-cost solution to compute the attitudes of a tunnel boring machine (TBM) during microtunneling. Given the traditional method, three rotation angles of yaw, pitch and roll can be computed by the coordinates of three observation points on TBM. The improved modeling compensates shortcomings of the traditonal technique. The optimization method is proposed to decide better layout of the three observation points. And then, for concept reducing error due to measurement asynchronism of observation points during tunneling, Kalman filter algorithm is given to predict the coordination of observation points. Finally, Monte Calro simulation shows that the performance of this method is better than those traditional methods. The simulation experiment and measurement experiment both successfully demonstrated the proposed algorithm in terms of:(1) that Kalman filter algorithm reduced the error of attitude measurement;(2) the inhibiting effect of measurement error caused by random noise.

  • Abstract
  • Keywords
  • Introduction
  • Position and Attitude Represent
  • Transformation Matrix Algorithm
  • Position Optimization
  • Model of Kalman Filter Algorithm
  • Parameters of Kalman Filter
  • Simulation Experiment
  • Measurement Experiment
  • Conclusion
  • Acknowledgments
  • References

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