Version 1
: Received: 18 September 2024 / Approved: 18 September 2024 / Online: 19 September 2024 (20:17:12 CEST)
How to cite:
Wan, M.; Liu, D.; Liu, Z. State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer. Preprints2024, 2024091555. https://doi.org/10.20944/preprints202409.1555.v1
Wan, M.; Liu, D.; Liu, Z. State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer. Preprints 2024, 2024091555. https://doi.org/10.20944/preprints202409.1555.v1
Wan, M.; Liu, D.; Liu, Z. State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer. Preprints2024, 2024091555. https://doi.org/10.20944/preprints202409.1555.v1
APA Style
Wan, M., Liu, D., & Liu, Z. (2024). State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer. Preprints. https://doi.org/10.20944/preprints202409.1555.v1
Chicago/Turabian Style
Wan, M., Daoguang Liu and Zhigui Liu. 2024 "State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer" Preprints. https://doi.org/10.20944/preprints202409.1555.v1
Abstract
Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an Invariant Extended Kalman Filter (InEKF) with a disturbance observer, aiming at estimating the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter's robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains.
Keywords
quadruped robots; invariant extended Kalman filter; state estimates; non-stationary terrain
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
