ORCID
- Li, Chunxu: 0000-0001-7851-0260
Abstract
In this brief, an enhanced robotic learning interface has been investigated using Beetle Antennae Search (BAS) and Extreme Learning Machine (ELM). The initial values of learning weights and bias of the network have significant effect on the performance of the ELM, hence, BAS algorithm was employed to optimize the initial values of learning weights and bias. Kinect v2 camera sensor was applied to obtain the endpoint's position of the upper limb, MYO armband was used to measure the corresponding joint angle values. Those aforementioned data formed the dataset to be trained by ELM and after training the ELM model was able to generate angle values by only giving position as input without a need to carry out kinematic calculations. The proposed method has been validated by conducting series of experimental studies on a KUKA iiwa robot.
DOI
10.1109/tcsii.2020.3034771
Publication Date
2020-10-29
Publication Title
IEEE Transactions on Circuits and Systems II: Express Briefs
First Page
1
Last Page
1
ISSN
1549-7747
Embargo Period
2020-11-13
Organisational Unit
School of Engineering, Computing and Mathematics
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Li, C., Zhu, S., Sun, Z., & Rogers, J. (2020) 'BAS Optimized ELM for KUKA iiwa Robot Learning', IEEE Transactions on Circuits and Systems II: Express Briefs, , pp. 1-1. Available at: https://doi.org/10.1109/tcsii.2020.3034771
