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This paper introduces a state estimation framework designed for a wheel-legged quadruped robot, providing the necessary state information to the motion controller to satisfy specified control performance metrics. We propose an Extended Kalman Filter (EKF) for state estimation, which fuses joint encoder data with IMU measurements to estimate the robot's position, velocity, and attitude. The proposed estimation method is validated using a whole-body control framework and tested in a simulation environment on a wheeled-legged quadruped robot we developed.

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Extended Kalman Filter-Based State Estimation for Wheel-Legged Quadruped Robots

  • 会议论文

  • 发表日期：

    2025年

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  • Bin Lan,
  • Kai Liu,
  • Houde Liu,
  • Lunfei Liang,
  • Xiaojuan Mo,
  • Bangguo Wei

摘要

This paper introduces a state estimation framework designed for a wheel-legged quadruped robot, providing the necessary state information to the motion controller to satisfy specified control performance metrics. We propose an Extended Kalman Filter (EKF) for state estimation, which fuses joint encoder data with IMU measurements to estimate the robot's position, velocity, and attitude. The proposed estimation method is validated using a whole-body control framework and tested in a simulation environment on a wheeled-legged quadruped robot we developed.