scholarly journals Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2520 ◽  
Author(s):  
Jinlong Piao ◽  
Eui-Sun Kim ◽  
Hongseok Choi ◽  
Chang-Bae Moon ◽  
Eunpyo Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Force Control ◽  
Estimation Method ◽  
Parallel Robot ◽  
Force Estimation ◽  
End Effector ◽  
Cable Properties ◽  
Cable Force ◽  
Artificial Neural

In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.

Visual Control of Robotic Manipulator Based on Artificial Neural Network

1991 ◽  
Vol 3 (5) ◽  
pp. 394-400 ◽  
Author(s):  
Hideki Hashimoto ◽  
◽  
Takashi Kubota ◽  
Motoo Sato ◽  
Fumio Harashima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visual Information ◽  
Degrees Of Freedom ◽  
Robotic Manipulator ◽  
Inverse Kinematic ◽  
End Effector ◽  
Control Scheme ◽  
Neural Network System ◽  
Artificial Neural

This paper describes a control scheme for a robotic manipulator system which uses visual information to position and orientate the end-effector. In the scheme the position and the orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position, orientation of the workpiece and inverse kinematic calculation. An artificial neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientaion. The proposed system can control the robot so that it approach the desired position and orientaion from arbitary initial ones. Simulation for the robotic manipulator with six degrees of freedom is done. The validity and the effectiveness of the proposed control scheme are varified by computer simulations.

scholarly journals Discrimination of Object Curvature Based on a Sparse Tactile Sensor Array

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 583
Author(s):  
Weiting Liu ◽  
Binpeng Zhan ◽  
Chunxin Gu ◽  
Ping Yu ◽  
Guoshi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sensor Array ◽  
Estimation Method ◽  
Tactile Sensor ◽  
Radius Of Curvature ◽  
Curvature Radius ◽  
Average Curvature ◽  
Artificial Neural ◽  
Tactile Sensor Array

Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.

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