Discrete-time practical robotic control for human–robot interaction with state constraint and sensorless force estimation

2022 ◽  
Author(s):  
Zhiqiang Ma ◽  
Zhengxiong Liu ◽  
Panfeng Huang
Keyword(s):  
Discrete Time ◽  
State Constraint ◽  
Human Robot Interaction ◽  
Robotic Control ◽  
Force Estimation ◽  
Robot Interaction

scholarly journals Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985613
Author(s):  
Xiangxing Liu ◽  
Guokun Zuo ◽  
Jiaji Zhang ◽  
Jiajin Wang
Keyword(s):  
Friction Force ◽  
Disturbance Observer ◽  
Human Robot Interaction ◽  
Rehabilitation Robot ◽  
End Effect ◽  
Force Estimation ◽  
Robot System ◽  
Robot Interaction ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

In human robot interaction systems, human intent detection plays an important role to improve the interactive performances and then the rehabilitation effects. A study is proposed to estimate the interactive forces that indirectly detect the human motion intent. A disturbance observer is designed to estimate interactive torques and friction forces without force sensors, and then a friction force model is constructed to estimate the friction force in the robot system. To detect the human–robot interaction force, we subtract the friction force from disturbance observer estimation result. Several experiments were performed to test the performances of the proposed methods. Those methods were applied in an end-effect upper limb rehabilitation robot system. The results show that the precision of the estimated sensor force can increase 5% than the force sensor. The senseless force estimation method we proposed in this article can be an alternative option in force control tasks when force sensors are not suitable.

scholarly journals Real-Time Classification of Hand Motions Using Electromyography Collected from Minimal Electrodes for Robotic Control

2021 ◽  
Vol 3 (1) ◽  
pp. 13
Author(s):  
Richard Scott Byfield ◽  
Richard Weng ◽  
Morgan Miller ◽  
Yunchao Xie ◽  
Jheng-Wun Su ◽  
...  
Keyword(s):  
Real Time ◽  
Human Subjects ◽  
Active Surface ◽  
Human Robot Interaction ◽  
Machine Learning Algorithms ◽  
Robotic Control ◽  
Robot Interaction ◽  
Minimum Number ◽  
Real Time Classification

In recent years, advances in human robot interaction (HRI) has shown massive potential for universal control of robots. Among them, electromyography (EMG) signals generated by motions of muscles have been identified as an important and useful source. Powered by recently emerged machine learning algorithms, real-time classification has been proved applicable to control robots. However, collecting EMG signals with minimum number of electrodes for real-time classification and robotic control is still a challenge. In this paper, we demonstrate that twenty five robotic commands in a robotic arm can be controlled in real time by using the EMG signals collected from only two pairs of active surface electrodes on each forearm of human subjects. To achieve this task, a variety of tested ML models for this classification were tested. Among them, the Gaussian Naïve Bayes (GNB) achieved an accuracy of >96%. This unprecedented level of classification accuracy of the EMG signals collected from the least number of active electrodes suggest that by combination of optimized electrode configuration and a suitable ML model, the capability of robotic control can be maximized.

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