TY - GEN
T1 - SVM-Based State Estimation of Biped Robot
AU - Gao, Chengzhi
AU - Xie, Ye
AU - Kong, Lingyu
AU - Chen, Xing Yu
AU - Xie, Anhuan
AU - Zhang, Dan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/2/3
Y1 - 2021/2/3
N2 - Currently, the research of biped robots is becoming more and more popular, the kinematics-based state estimation, as an important precondition for its dynamic walking, has also become the focus of many researchers. Owing to the hybrid dynamics of biped robot, ground contact of each foot has to be previously evaluated to complete state estimation when using kinematics method. The contribution of this paper is to introduce the soft SVM to detect the contact phase. This SVM-based method free biped from the use of force sensors, which are highly vulnerable under the impact between feet and contact surfaces. Moreover, accurate dynamic modelling is not requisited. These two advantages indicate the strong robustness of SVM-based contact detection. Based on this contact detection method, the paper studies the state fusion method by applying extended Kalman filter to combine kinematics estimation with IMU data. Finally, the SVM-based contact detection algorithm and the complete state fusion method are both verified on our biped robot, with several experiments. The accuracy of the SVM-based method is validated by the comparison with force sensor based method. In addition, in contrast to torque-based method, its accuracy does not highly rely on the selection of algorithm parameters, like torque threshold.
AB - Currently, the research of biped robots is becoming more and more popular, the kinematics-based state estimation, as an important precondition for its dynamic walking, has also become the focus of many researchers. Owing to the hybrid dynamics of biped robot, ground contact of each foot has to be previously evaluated to complete state estimation when using kinematics method. The contribution of this paper is to introduce the soft SVM to detect the contact phase. This SVM-based method free biped from the use of force sensors, which are highly vulnerable under the impact between feet and contact surfaces. Moreover, accurate dynamic modelling is not requisited. These two advantages indicate the strong robustness of SVM-based contact detection. Based on this contact detection method, the paper studies the state fusion method by applying extended Kalman filter to combine kinematics estimation with IMU data. Finally, the SVM-based contact detection algorithm and the complete state fusion method are both verified on our biped robot, with several experiments. The accuracy of the SVM-based method is validated by the comparison with force sensor based method. In addition, in contrast to torque-based method, its accuracy does not highly rely on the selection of algorithm parameters, like torque threshold.
KW - biped robot
KW - extended Kalman filter
KW - soft margin support vector machine
KW - state estimate
UR - http://www.scopus.com/inward/record.url?scp=85104893930&partnerID=8YFLogxK
U2 - 10.1109/ICMRE51691.2021.9384850
DO - 10.1109/ICMRE51691.2021.9384850
M3 - Conference article published in proceeding or book
AN - SCOPUS:85104893930
T3 - 2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
SP - 41
EP - 47
BT - 2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
Y2 - 3 February 2021 through 5 February 2021
ER -