Research Article
Synchronous Monitoring Method of Multi-manipulator Trajectory Signals Based on Machine Learning
@INPROCEEDINGS{10.1007/978-3-030-94182-6_10, author={Xiao-zheng Wan and Song Zhang and Ji-ming Zhang and Hui Chai and Huan-yu Zhao}, title={Synchronous Monitoring Method of Multi-manipulator Trajectory Signals Based on Machine Learning}, proceedings={IoT and Big Data Technologies for Health Care. Second EAI International Conference, IoTCare 2021, Virtual Event, October 18-19, 2021, Proceedings, Part II}, proceedings_a={IOTCARE PART 2}, year={2022}, month={6}, keywords={Machine learning Multiple manipulators Manipulator trajectory Signal synchronization Signal monitoring}, doi={10.1007/978-3-030-94182-6_10} }- Xiao-zheng Wan
Song Zhang
Ji-ming Zhang
Hui Chai
Huan-yu Zhao
Year: 2022
Synchronous Monitoring Method of Multi-manipulator Trajectory Signals Based on Machine Learning
IOTCARE PART 2
Springer
DOI: 10.1007/978-3-030-94182-6_10
Abstract
The traditional trajectory signal monitoring method uses a combination of sensors and mathematical models to detect the trajectory signal of the manipulator, which is susceptible to interference from uncertain factors, resulting in low monitoring accuracy and poor real-time performance. In response to the above problems, this research designed a method for synchronous monitoring of multi-manipulator trajectory signals based on machine learning. After the multi-manipulator dynamic model is established, the coordinate system of the trajectory acquisition equipment is calibrated according to the principle of binocular vision. After preprocessing the trajectory image of the manipulator, the CLDNN model is used to realize the synchronous monitoring of the trajectory signal. The simulation experiment results show that the monitoring method shortens the monitoring time by about 62.5%, and has higher monitoring accuracy, which proves that its performance is better.
