Using Machine Vision and Hand-Motion Control to Improve Crane Operator Performance

2012 ◽  
Vol 42 (6) ◽  
pp. 1496-1503 ◽  
Author(s):  
Kelvin Chen Chih Peng ◽  
William Singhose ◽  
Purnajyoti Bhaumik
Keyword(s):  
Machine Vision ◽  
Motion Control ◽  
Hand Motion ◽  
Operator Performance

Crane Operation Using Hand-Motion and Machine Vision

2009 ◽  
Author(s):  
Kelvin Chen Chih Peng ◽  
William Singhose ◽  
Jonathan Fonseca
Keyword(s):  
Experimental Data ◽  
Image Processing ◽  
Motion Control ◽  
Processing System ◽  
Bridge Crane ◽  
Hand Motion ◽  
Input Shaper ◽  
Control Interface ◽  
New Type ◽  
Human Operators

Payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. A new type of crane control interface that allows an operator to drive a crane by moving his or her hand freely in space has been implemented on an industrial bridge crane. An image processing system tracks the movement of a glove worn on the operator’s hand and its position is then used to drive the crane. Matlab simulations of the crane dynamics and hand-motion control were compared with actual experimental data. The results show that a combination of aggressive PD gains and an input shaper is able to generate the desired characteristics of fast payload response and low residual oscillations.

Machine vision and intelligence incorporating motion control

1999 ◽  
Vol 19 (1) ◽  
pp. 55-58 ◽  
Author(s):  
Ronald Brian Jennings ◽  
Glen Bright
Keyword(s):  
Machine Vision ◽  
Motion Control

scholarly journals Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks

2020 ◽  
Vol 5 (3) ◽  
pp. 382-394
Author(s):  
Suprapto Suprapto ◽  
Edy Riyanto
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Motion Control ◽  
Multilayer Perceptron ◽  
Multi Layer Perceptron ◽  
Drying Process ◽  
Different Temperatures ◽  
Motion Speed ◽  
Similar Area ◽  
Multilayer Perceptron Networks

This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control. Two different temperatures are employed on the machine, including 60 and 75°C. The results showed that the grape could be dried with similar area 3800 pixel at the 770th min using temperature 60°C and at the 410th min using temperature 75°C.  Comparing between them, the similar ratio could also be achieved at 0.64 with different time 360 min. Indeed, the temperature setting at 75°C resulted faster drying performance.

DYNAMIC HAND MOTION RECOGNITION BASED ON TRANSIENT AND STEADY-STATE EMG SIGNALS

2012 ◽  
Vol 09 (01) ◽  
pp. 1250007 ◽  
Author(s):  
DAPENG YANG ◽  
JINGDONG ZHAO ◽  
LI JIANG ◽  
HONG LIU
Keyword(s):  
Steady State ◽  
Motion Control ◽  
Feature Space ◽  
Recognition System ◽  
Support Vector ◽  
Motion Recognition ◽  
Hand Motion ◽  
Dynamic Motion ◽  
Detection Delay ◽  
The Time Domain

Traditional motion recognition methods developed on the basis of steady-state electromyography (EMG) signals cannot well deal with the transient EMG signals. Thus, a large quantity of incorrect classification outputs would be introduced during the dynamic stages of the motions. In order to achieve a high-accuracy recognition system especially for dynamic motion control, this paper combines the transient EMG and the steady-state EMG signals together for training the recognition system. A threshold decision method is utilized in the time-domain feature space to collect the combined EMG signals. Besides, a statistical classifier named support vector machine is adopted in the online recognition procedure to distinguish the motion types. Experiments are conducted to quantify the classification accuracy and response delay of the recognition systems, and compare these with traditional steady-state EMG-based methods. The results indicate that the recognition accuracy can be greatly improved and the detection delay of the motions can be significantly compressed in the transient stages of the hand motions. The method shows a promising application in the dynamic motion control of dexterous prosthetic hands in the future.

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