scholarly journals Robot arm control method using forearm EMG signals

2020 ◽  
Vol 309 ◽  
pp. 04007
Author(s):  
Minjie Chen ◽  
Honghai Liu
Keyword(s):  
Continuous Improvement ◽  
Control Method ◽  
Recognition Rate ◽  
Robotic Arm ◽  
Support Vector ◽  
Robot Arm ◽  
Svm Algorithm ◽  
Input Signals ◽  
Robot Arm Control ◽  
The Cost

With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals are finally transmitted to the robotic arm, so that people can teleoperate the robotic arm via sEMG signals in real time. Reduce the number of channels to lower the cost while ensuring a high and usable recognition rate. Experiments were performed by collecting EMG signals from the forearm surface of eight healthy volunteers. The experimental results show that the system’s overall gesture recognition accuracy rate can reach up to 90%, and the system responds fast, laying a good foundation for manipulating artificial limbs in the future.

scholarly journals Position and Speed Control of 2 DOF Industrial Robotic Arm using Robust Controllers

2020 ◽  
Author(s):  
Mustefa Jibril ◽  
Messay Tadese ◽  
Reta Degefa
Keyword(s):  
Control Method ◽  
Open Loop ◽  
Pole Placement ◽  
Robotic Arm ◽  
Robot Arm ◽  
H Infinity ◽  
Optimal Controllers ◽  
Input Signals ◽  
System Output ◽  
Regional Pole Placement

In this paper, a 2 DOF industrial robotic arm is designed and simulated for elbow and wrist angle and velocity performance improvement using robust control method. Mixed H2/H infinity synthesis with regional pole placement and H2 optimal controllers are used to improve the system output. The open loop response of the robot arm shows that the elbow and wrist angles and velocities need some improvement. Comparison of the proposed controllers for an impulse and step input signals have been done and a promising results have been obtained.

Motion-Assist Arm with a Passive Joint for an Upper Limb

2020 ◽  
Vol 32 (1) ◽  
pp. 183-198
Author(s):  
Hiroaki Kozuka ◽  
Daisaku Uchijima ◽  
Hiroshi Tachiya ◽  
◽  
Keyword(s):  
Upper Limb ◽  
Control Method ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Robot Arm ◽  
Passive Joint ◽  
Target Path ◽  
Three Degree Of Freedom ◽  
Actuated Joints ◽  
Human Upper Limb

This study proposes a motion-assist arm that can accurately support the positioning of a human upper limb. The motion-assist arm is a three-degree-of-freedom (DOF) planer under-actuated robotic arm with a 1-DOF passive joint that can be driven by an human. A control method for the robot arm is as follows. First, when the human moves an output point of the arm manually, the passive joint is rotated with the movement of the output point. Then, for accurate positioning of the output point on a target path, the actuated joints are controlled according to the displacement of the passive joint. Based on the above method, the human can adjust the velocity of the output point deliberately while its position is accurately corrected by the actuated joints. To confirm its effectiveness, the authors conducted tests to assist the human’s upper limb movement along straight target paths, a square path, and free curves paths such as italic letters with the proposed robot arm prototype. From the results of the tests, the authors confirmed that the proposed robot arm can accurately position the upper limb of the human on the target paths while the human intentionally moves the upper limb. It is expected that the proposed arm will be used for rehabilitation because it can aid patients to move their arms correctly. In addition, the proposed arm will enable any human to achieve complex work easily.

Control of Low-Cost Customizable Robot Arm Actuated by Elastic Tendons

2016 ◽  
Vol 28 (4) ◽  
pp. 509-522 ◽  
Author(s):  
Junki Togashi ◽  
◽  
Kazuhisa Mitobe ◽  
Genci Capi ◽  
Keyword(s):  
Force Control ◽  
Control Method ◽  
Low Cost ◽  
String Tension ◽  
Machining Accuracy ◽  
Robot Arm ◽  
Compliant Joints ◽  
Low Stiffness ◽  
Static Conditions ◽  
The Cost

[abstFig src='/00280004/09.jpg' width='300' text='Elastic tendon driven robot arm' ] This paper presents a low-cost, lightweight robot arm with very low stiffness actuated by elastic tendons. To simplify the string tension control, a new winding device was developed. Small pulleys were incorporated into the winding drum to reduce friction between the tendon and the drum. A marionette-style two-link robot arm with compliant joints was prototyped. Because the arm and winding devices were separate from each other, the cost and weight of the robot were reduced. The links are made with lightweight wood connected by simple shaft joints. The robot design can be easily modified by the user because the mechanical parts do not require high machining accuracy. This robot is intended for implementation in tasks that do not require high positioning accuracy using a simple force control under environmental constraints. Because of its low stiffness, simple and sensor-less force control can be easily implemented based on the relationship between forces under static conditions. The proposed simple control method was evaluated experimentally by conducting position, static force, and hybrid position/force control tasks and was shown to perform well. The results also demonstrate that employing additional sensors, such as a camera, improves the accuracy of the controller.

scholarly journals Research on Control Strategy of Manipulator Based on Simulation

2021 ◽  
Vol 2093 (1) ◽  
pp. 012007
Author(s):  
JiaLei Su
Keyword(s):  
Control Method ◽  
Impedance Control ◽  
Rapid Development ◽  
Space Robot ◽  
Space Environment ◽  
Robotic Arm ◽  
Control Element ◽  
Feedback Signal ◽  
Robot Arm ◽  
Control Effect

Abstract The force supple control method of robotic arm has been widely researched internationally for many years, and its specific use varies according to the structure of the robotic arm, the location of the sensor, the working space environment, and other factors. Based on the force control principle and control method of the space robot arm, this paper adopts the position-based Cartesian spatial impedance control and proposes an effective forcesmoothing control method after pre-processing the feedback signal of the six-dimensional force sensor installed at the end of the space robot arm with the coordinate system conversion. In addition, the proposed position-based Cartesian spatial impedance control method is modeled and simulated to analyze the effect of each control element on the force-following control effect, to find out the control conditions that can optimize the force-position control effect, and finally to optimize the impedance parameters. This study aims to promote the rapid development of the field of robotic arm control.

scholarly journals A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Le Cao ◽  
Wenyan Zhang ◽  
Xiu Kan ◽  
Wei Yao
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Low Frequency ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Semg Signal ◽  
Svm Algorithm ◽  
Svm Model ◽  
Semg Signals

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

scholarly journals A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3051 ◽  
Author(s):  
Minah Kim ◽  
Byungyeon Kim ◽  
Byungjun Park ◽  
Minsuk Lee ◽  
Youngjae Won ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Support Vector ◽  
Robot Arm ◽  
K Nearest Neighbors ◽  
Tooth Color ◽  
Color Determination ◽  
Svm Algorithm ◽  
Shade Matching ◽  
Optimum Solution

In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.

scholarly journals Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton Using Electromyography

2020 ◽  
Vol 10 (20) ◽  
pp. 7146
Author(s):  
Lucas D. L. da Silva ◽  
Thiago F. Pereira ◽  
Valderi R. Q. Leithardt ◽  
Laio O. Seman ◽  
Cesar A. Zeferino
Keyword(s):  
Upper Limb ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Hybrid Control ◽  
Impedance Control ◽  
Robotic Arm ◽  
Average Error ◽  
Acceptable Error ◽  
Upper Limb Exoskeleton ◽  
Input Signals

Exoskeletons are wearable mobile robots that combine various technologies to enable limb movement with greater strength and endurance, being used in several application areas, such as industry and medicine. In this context, this paper presents the development of a hybrid control method for exoskeletons, combining admission and impedance control based on electromyographic input signals. A proof of concept of a robotic arm with two degrees of freedom, mimicking the functions of a human’s upper limb, was built to evaluate the proposed control system. Through tests that measured the discrepancy between the angles of the human joint and the joint of the exoskeleton, it was possible to determine that the system remained within an acceptable error range. The average error is lower than 4.3%, and the robotic arm manages to mimic the movements of the upper limbs of a human in real-time.

Design and performance analysis of an industrial robot arm for robotic drilling process

2019 ◽  
Vol 46 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Longfei Sun ◽  
Fengyong Liang ◽  
Lijin Fang
Keyword(s):  
Control Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Ball Screw ◽  
Robotic Arm ◽  
Drilling Process ◽  
Robot Arm ◽  
Content Type ◽  
Robotic Drilling

Purpose The purpose of this paper is to present a robotic arm that can offer better stiffness than traditional industrial robots for improving the quality of holes in robotic drilling process. Design/methodology/approach The paper introduces a five-degree of freedom (DOF) robot, which consists of a waist, a big arm, a small arm and a wrist. The robotic wrist is composed of two DOFs of pitching and tilting. A parallelogram frame is used for robotic arms, and the arm is driven by a linear electric cylinder in the diagonal direction. Double screw nuts with preload are used in the ball screw to remove the reverse backlash. In addition, dual-motor drive is applied for each DOF in the waist and the wrist to apply anti-backlash control method for eliminating gear backlash. Findings The proposed robotic arm has the potential for improving robot stiffness because of its truss structure. The robot can offer better stiffness than industrial robots, which is beneficial to improve the quality of robotic drilling holes. Originality/value This paper includes the design of a five-DOF robot for robotic drilling tasks, and the stiffness modeling of the robot is presented and verified by the experiment. The robotic system can be used instead of traditional industrial robots for improving the hole quality to a certain extent.

KDA based WKNN-SVM Method for Activity Recognition System from Smartphone Data

2021 ◽  
Vol 9 (4) ◽  
pp. 0-0
Keyword(s):  
Activity Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Support Vector ◽  
Kernel Discriminant Analysis ◽  
Nearest Neighbours ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Physical Activity Recognition ◽  
Public Datasets

This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as Walking, Standing or Sitting. It combines Weighted K-Nearest Neighbours (WKNN) and Support Vector Machines (SVM). The signals generated from the sensors are processed and then reduced using the Kernel Discriminant Analysis (KDA) by selecting the best discriminating components of the data. We performed different tests on four public datasets where the participants performed different activities carrying a smartphone. We demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances, and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.

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