Name
Abstract | ||
|---|---|---|
Human-robot interaction force information is of great significance for realizing safe, compliant and efficient rehabilitation training. In order to accurately estimate the interaction force during human-robot interaction, an adaptive method for estimation of human-robot interaction force is proposed in this paper. Firstly, the dynamics of human-robot system are modeled, which allows to establish a state space equation. Then, the interaction force is described by a polynomial function of time, and is introduced into the state space equation as a system state. Meanwhile, the Kalman filter is adopted to estimate the extended state of system online. Moreover, in order to deal with the uncertainty of system noise covariance matrix, sage-husa adaptive Kalman filter is used to correct the covariance matrices of system noises online. Finally, experiments were carried out on a lower limb rehabilitation robot, and the results show that the proposed method can precisely estimate the interaction force and also has good real-time performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-36808-1_59 | NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV |
Keywords | DocType | Volume |
Human-robot interaction, State estimation, Rehabilitation robot, Interaction force estimation | Conference | 1142 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Liang | 1 | 27 | 10.67 |
| Weiqun Wang | 2 | 25 | 12.73 |
| Zeng-Guang Hou | 3 | 2293 | 167.18 |
| Shixin Ren | 4 | 0 | 3.38 |
| Jiaxing Wang | 5 | 3 | 2.77 |
| Weiguo Shi | 6 | 0 | 2.03 |
| Tingting Su | 7 | 0 | 0.34 |