A Potential 4-D Fingertip Force Sensor for an Underwater Robot Manipulator

2010 ◽  
Vol 35 (3) ◽  
pp. 574-583 ◽  
Author(s):  
Qiaokang Liang ◽  
Dan Zhang ◽  
Quanjun Song ◽  
Yunjian Ge
Keyword(s):  
Robot Manipulator ◽  
Force Sensor ◽  
Underwater Robot ◽  
Fingertip Force

Fingertip Force/Torque Sensor With High Isotropy and Sensitivity for Underwater Manipulation

2010 ◽  
Author(s):  
Qiaokang Liang ◽  
Dan Zhang ◽  
Zhongzhe Chi ◽  
Yunjian Ge
Keyword(s):  
Robot Manipulators ◽  
Control Strategies ◽  
High Sensitivity ◽  
Force Sensor ◽  
Interaction Force ◽  
Underwater Robot ◽  
Loop Control ◽  
Fingertip Force ◽  
Underwater Manipulation ◽  
Close Loop Control

Control strategies for robotic manipulators in underwater applications are still immature compared with the strategies of the manipulators on land. Part of the reason is that there is no precise force/torque information, which is essential to the close-loop control. Unlike the sensor applied on the ground, the sensors for underwater applications have to endure the high-pressure, low-temperature and corrosive environment. Therefore, aimed at obtaining the accurate interaction force/torque between underwater robot manipulators and objects, a novel four-dimensional fingertip force sensor is presented based on e-type membrane for underwater robot manipulators. A seal technique is described. Experimental results demonstrate the design could detect force/torque with good linearity, high sensitivity and weak couplings.

A Prototype of Fingertip Force Sensor Using Small Piezoelectric Element

2021 ◽  
Vol 2021 (0) ◽  
pp. 1P3-I04
Author(s):  
Haruki YAMAMOTO ◽  
Takuma AKIDUKI ◽  
Atsuo HONNA ◽  
Tomoaki MASIMO
Keyword(s):  
Piezoelectric Element ◽  
Force Sensor ◽  
Fingertip Force

Fingertip Force Measurement of GloveMAP by Using a Flexi Force Sensor

2015 ◽  
Vol 780 ◽  
pp. 1-5
Author(s):  
Khairunizam Wan ◽  
H.E. Nabilah ◽  
Nor Farahiya ◽  
M. Hazwan Ali ◽  
Rashidah Suhaimi ◽  
...  
Keyword(s):  
Force Measurement ◽  
Current Trend ◽  
Force Sensor ◽  
Force Sensors ◽  
Gaussian Filter ◽  
Natural Interaction ◽  
Fingertip Force ◽  
Grasping Force ◽  
Interface Devices ◽  
A Current

Modernization of human technologies overtime results the need of more freedom technology likes the use of natural interaction to replace a current trend interface devices such as joysticks, mice, keyboards and other related output devices. Dataglove is one of the interface devices that could serve a natural interaction between user and computers. In this paper, a dataglove called GloveMAP is introduced which has the capability of measuring fingertip force. The flexible force sensors are attached to the fingers location of the glove. Several object grasping experiments are conducted and the grasping force signals are measured. A Gaussian filter is introduced to smoothen the acquired force signals.

scholarly journals A Finger Grip Force Sensor with an Open-Pad Structure for Glove-Type Assistive Devices

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 4 ◽  
Author(s):  
Junghoon Park ◽  
Pilwon Heo ◽  
Jung Kim ◽  
Youngjin Na
Keyword(s):  
Degrees Of Freedom ◽  
Grip Force ◽  
Force Sensor ◽  
Assistive Devices ◽  
Finger Joint ◽  
Assistive Device ◽  
Force Estimation ◽  
Joint Angles ◽  
Fingertip Force ◽  
Grasping Force

This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture.

A Modular 2-DOF Serial Robot Manipulator for Education in Robot Control

2016 ◽  
Author(s):  
Stephen Mascaro
Keyword(s):  
Robot Control ◽  
Inverse Dynamics ◽  
Position Control ◽  
Sliding Mode ◽  
Feedforward Control ◽  
Robot Manipulator ◽  
Impedance Control ◽  
Force Sensor ◽  
Control Techniques ◽  
Serial Robot

This paper describes a modular 2-DOF serial robot manipulator and accompanying experiments that have been developed to introduce students to the fundamentals of robot control. The robot is designed to be safe and simple to use, and to have just enough complexity (in terms of nonlinear dynamics) that it can be used to showcase and compare the performance of a variety of textbook robot control techniques including computed torque feedforward control, inverse dynamics control, robust sliding-mode control, and adaptive control. These various motion control schemes can be easily implemented in joint space or operational space using a MATLAB/Simulink real-time interface. By adding a simple 2-DOF force sensor to the end-effector, the robot can also be used to showcase a variety of force control techniques including impedance control, admittance control, and hybrid force/position control. The 2-DOF robots can also be used in pairs to demonstrate control architectures for multi-arm coordination and master/slave teleoperation. This paper will describe the 2-DOF robot and control hardware/software, illustrate the spectrum of robot control methods that can be implemented, and show sample results from these experiments.

Hybrid Position and Force Control Without Force Sensor

1996 ◽  
Vol 8 (3) ◽  
pp. 226-234
Author(s):  
Kiyoshi Ohishi ◽  
◽  
Masaru Miyazaki ◽  
Masahiro Fujita ◽  
Keyword(s):  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Hybrid Control ◽  
Robot Manipulator ◽  
Reaction Force ◽  
Force Sensor ◽  
Force Estimation ◽  
Estimation System ◽  
Dynamics Calculation ◽  
Torque Observer

Generally, hybrid control is realized by sensor signal feedback of position and force. However, some robot manipulators do not have a force sensor due to the environment. Moreover, a precise force sensor is very expensive. In order to overcome these problems, we propose the estimation system of reaction force without using a force sensor. This system consists of the torque observer and the inverse dynamics calculation. Using both this force estimation system and <I>H</I>∞ acceleration controller which is based on <I>H</I>∞ control theory, it takes into account the frequency characteristics of both sensor noise effect and disturbance rejection. The experimental results in this paper illustrate the fine hybrid control of the three tested degrees-of-freedom DD robot manipulator without force sensor.

Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

1990 ◽  
Vol 2 (4) ◽  
pp. 273-281 ◽  
Author(s):  
Masatoshi Tokita ◽  
◽  
Toyokazu Mitsuoka ◽  
Toshio Fukuda ◽  
Takashi Kurihara ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Force Control ◽  
Pid Controller ◽  
Robot Manipulator ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
The Neural Network ◽  
The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

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