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Real-Time Cascaded State Estimation Framework on Lie Groups for Legged Robots Using Proprioception.

Botao Liu1, Fei Meng1, Zhihao Zhang1

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China.

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Summary
This summary is machine-generated.

This study introduces a novel robot state estimation framework using proprioception. The method enhances accuracy and real-time performance for legged robots, particularly during foot-ground contact.

Keywords:
Kalman filtermoving horizon estimationproprioceptionstate estimation

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Area of Science:

  • Robotics
  • State Estimation
  • Control Systems

Background:

  • Accurate state estimation is crucial for legged robot control.
  • Proprioceptive sensors offer a rich source of information for estimation.
  • Existing methods struggle with impact dynamics during locomotion.

Purpose of the Study:

  • To develop a cascaded state estimation framework for robots using proprioception.
  • To improve the accuracy and real-time performance of robot state estimation.
  • To effectively handle impact noise in legged robot locomotion.

Main Methods:

  • A generalized-momentum-based Kalman filter (GMKF) estimates ground reaction forces.
  • An error-state Kalman filter (ESKF) provides prior state estimates.
  • A moving horizon estimation (MHE) problem is formulated on Lie groups and solved with parallel real-time iteration (Para-RTI).

Main Results:

  • The proposed framework achieves tightly coupled estimation on manifolds.
  • Demonstrated higher accuracy and real-time performance compared to existing methods.
  • Effectively mitigates impact noise during foot-ground contact in legged robots.

Conclusions:

  • The cascaded proprioception-based framework offers a superior approach to robot state estimation.
  • The method is particularly effective for legged robots navigating dynamic environments.
  • Experimental validation on the BQR3 robot confirms the framework's effectiveness.