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International Journal of Lean Thinking Volume 3, Issue 2 (December 2012)

Lean Thinkingjournal homepage: www.thinkinglean.com/ijlt

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INTELLIGENT ROBOT USED IN THE FIELD OF PRACTICAL APPLICATION OF ARTIFICIAL NEURAL NETWORK & MACHINE VISION

M. Mohan Prasad* Assistant Professor Department of Mechanical Engineering P.A.College of

Engineering and Technology Pollachi, Coimbatore – District 642002, Tamil

Nadu, India E-Mail Address: [email protected]

T. Varun Kumar Assistant Professor Department of Mechanical Engineering P.A.College of

Engineering and Technology Pollachi, Coimbatore – District 642002, Tamil

Nadu, India

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

Artificial Neural Network, Image processing,

Machine vision,

SPIHT,

Back propagation.

Received 25 May 2012

Accepted 05 July 2012

Available online 01 October 2012

Advances in Mechatronics have resulted in widespread of

robotics. But Robot performances on difficult and hazardous tasks

are not satisfactory, due to lack of automatic extraction of

information and reaction as humans. Humans react faster than

robots because his eyes capture the situation and brain process that

data using sixth sense. To solve this, machine vision with artificial

intelligence can be incorporated in robots as humans. This robot

will group even the worn-out, greasy commonly used industrial

components. In this paper, a machine vision system based on

Artificial Neural Network (ANN) is incorporated for robots. This

work is broadly divided into four stages, in the first stage; a mini

robot capable of pushing the components is fabricated with two

cameras for vision. In second stage, image processing of

components are captured and processed. This image is altered in

size using SPIHT method. In the third stage, Generalized ANN with

Back propagation algorithm is used to identify these images. In

final application stage, components are identified and pushed by

robots into respective bins as per requirement. Thus, the

developed VB software module provides a total solution for robots

in industry.

________________________________

* Corresponding Author

1. Introduction and literature survey

With their computational and agile skills robots perform tasks that are difficult or

hazardous to humans. Advances in microchips, microprocessor, sensors, control systems,

mechanical engineering, transducers and telecommunications have resulted in widespread

growth of robotic processes in industries. Today robots are controlled by computers that are

programmed to perform a range of activities. They are establishing themselves in

manufacturing automation systems to produce a range of products with great precision. The

emerging era of robots calls for different types of skills. Technical advances are gradually

increasing their similarity to humans. Engineers are attempting to add sensors to the industrial

robots, so that they can see, touch, and hear. Machines with this extra power will obtain

PRASAD, KUMAR / International Journal of Lean Thinking Volume 3, Issue 2(December 2012)

39

information about events in the outside world and react according to the changes. Instead of simply

repeating a fixed routine of instructions. Generally robots processors are becoming faster and more

sophisticated, imbibed with reasoning powers that may match those of humans.

2. NEED FOR INTELLIGENCE

Artificial intelligence (AI) to robot gives the power to make decisions and logical inferences. Such

robots would pick up information from their surroundings by using sensors and be able to move around.

They would make decisions, for instance, adjusting their pattern of operations in a work depending on

situations, rather than blindly following a preordered sequence of movements irrespective of outside

events. Robots use sensors and cameras, to obtain information on events around them. The information

enables the robot to adjust the operating instructions as per events, but the performance is not

satisfactory if the robot faced new situations or accidents. Generating artificial intelligent of humans is one

of the greatest challenges facing in the robotics fraternity. Robots should be provided both the

information required to understanding the real world and the information needed to deal with or

respond to the real world. It is not enough for robots simply to perceive and understand the world as

passive observers. Their senses should provide them with feedback on how their actions influence or

change the world. The microprocessor in the robot not only generate and transmit instructions in the

shape of signals to an electric motor but also receive and interpret information to judge the effect on real

world.

3. NEED FOR ROBOT VISION

By providing vision to robots will result in free roaming in real world and ready to tackle dangerous

tasks, is an exciting one, but many research hurdles remain to be crossed before this becomes a reality.

Generally industrial robots are programmed so that they pick components from conveyors for assembly

operations. But in industries components changing frequently and also the new designs are introduced

frequently. In such situations robots should reprogrammed accordingly. Once a manufacturer installs a

new manufacturing unit, time in installing a new assembly program is considerably increased. Thus there

is increase in lead-time. In order to avoid such situation robot should sense the surrounding

environments, judge and act accordingly. To achieve this goal, Vision for robots and Artificial intelligence

for robots are essential. Machine vision can be defined as the acquisition of image, followed by processing

and interpretation of this image by computer for some useful application. The use of machine vision in

industries can provide solutions for variety of problems associated with the recognition of the

components on-line. This vision can be provided to the robots by using the cameras.

4. DEVELOPED MINI ROBOT

The developed robot is to push the required industrial components of maximum weight of 5 Kg into

a collecting bin. The robot consists of three wheels. Two wheels powered by means of two stepper

motors and the third wheel is used as supporting wheel to provide stability. Two digital cameras are

mounted at 90 to capture the front and top view of the components. Based on the top and front view

components are identified. The minimum resolution required for the cameras is 128 X 128. There is a


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gripper which is used to place the components into the collecting bins. The electromagnetic gripper is

used to hold the components. The gripper, cameras and stepper motors are controlled using computer.

5. IMAGE PROCESSING

The captured image should be processed perfectly to obtain the best result. In this work, for

processing the image separate module is developed in Visual Basic. This module consists of three sub

modules, they are Image acquisition and digitization, Image preprocessing and Image processing.

5.1 Image Acquisition

In this stage, the captured image is read by dividing the bit map image into matrix of discrete

picture elements called pixels. Each pixel has a value that is proportional to the light intensity of that

small portion of the scene. The data structure of the bitmap file is shown in figure 1.

It consists of header, info header, and palette and image data [2]. Actual image data starts from 54th

byte of the bitmap file. In bitmap file, the image is stored from bottom to top. So, the actual figure is

stored as inverted image, as shown in the figure 2. In image data section of the format each pixel is

represented by three-color intensities namely Red, Blue and Green and each intensity ranges from 0 to

256.


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5.2. Image Preprocessing

ANN developed in such a way that it will operate only on binary codes i.e. 0’s and 1’s. But the

captured image contains the red, blue and green and their intensities ranges from 0 to 256. So the

colored image should be converted to black and white image. To convert the image, separate module is

developed. This module convert the color image in to binary monochrome image based on the degree of

darkness. This image contains large amount of noises (unwanted background disturbances). These noises

are due to various parameters like orientation, size, illumination, background, resolutions, etc. In order to

reduce the noises due to improper illumination, a suitable light source is used behind the object. Placing

the white screen behind the object eliminates noise due to background. Minimum resolution required is

128 X 128. Even though some noises are present on the images, to filter this noise each and every pixel

are compared with its surrounding eight pixels. If this pixel and maximum number of the surrounding

pixels are different then the pixel is converted to the surrounding pixel value. This is explained through

figure 3.

5.3. Image Processing

The size of the captured image is varying due to external factors like focal length, orientation of

camera, angle of capturing, etc. The image contains large quantity of raster data i.e. for minimum

resolution of 128 x 128 contains 49,152 datas, for 1024 x 768 resolution contains 23,59,296 datas or

codes. The amount of data that need to be processed and the speed of processing are significant. With the

parallel processing systems it is easy for computing, but the module is developed in such a way that it is

independent of processing machine. In order to achieve this goal, the obtained image should be reduced

to a standard smaller size (equals to number of neurons in ANN input layer) without loss of image datas.

On other hand, if the image obtained is very small, then that size should be enlarged to a standard size.


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The main criteria to be considered while resizing these images is that, there should not be any loss or

deviation from the actual image shape.

5.3.1. Set Partitioning In Hierarchical Trees Method (SPIHT)

As per Arnir Said and William a Pearlman [1], To achieve image sizing, the technique named Set

Partitioning In Hierarchical Trees Method (SPIHT) is used. The output of SPIHT gives the image of

reduced size or of enlarged size that can be directly given as input to the developed ANN. In this method,

the total image is divided into four equal quadrants. Again each and every quadrant is divided into four

equal quadrants i.e. at this stage total image is divided into 16 equal quadrants. This dividing procedure

will continue till the image matrix size reaches to the least minimum size i.e. the stage in which further

dividing cannot be possible. This stage attained only if the quadrant contains four binary digits and the

image data in that small quadrant are read. The maximum repeated code might be assumed as code for

that small quadrant. In this way the size is reduced by ¼ th of the total size. Again the same procedure is

repeated until the required size is obtained. By this method, exact compressed replica of the original

image is obtained without any shape loss. This is explained by the figure 5.


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6.0 ARTIFICIAL INTELLIGENCE

To provide artificial intelligence to robots will create lot of hurdles and difficulties. Researchers were

tried to solve these hurdles and found out several techniques like Artificial Neural Network (ANN),

Genetic Algorithm, Fuzzy logic, Simulated Annealing, etc. Among this techniques ANN only resembles to

human brain, so in this work ANN is used to create intelligence to robots.

6.1 Artificial Neural Network

Human brain is subjected to a number of different pieces of information processed simultaneously.

This basic model assumes that information processing takes place through the interaction of large number

of highly interconnected processing elements called neurons. Researchers have developed a similar tool

with same mechanism as that of human brain works, and are called as “Artificial Neural Networks” (ANN).

The simple ANN structure is shown in Fig.6.

According to Freeman and Skapura, ANN is a network of processing elements capable of doing

processing in parallel and in distributed manner. ANN can enhance speed of processing and also record

useful information to analyze various conditions. This allows the machine to directly perceive what the

user is doing in the real industrial environments. Several ANN models like Adaline, Madaline, Back

propagation, BAM, Hopfield model, Counter propagation, etc., are available. Back propagation[3] has

certain advantage over the other algorithms i.e. varying step size, flexible learning speed, any number of

stopping criteria can be fixed, network size is user defined, network pruning is easy, etc. By taking

advantage of the above in this paper Back propagation algorithm is used.

6.2 Back propagation Network (BPN)

Back propagation rule [7] is a kind of gradient descent technique with backward error propagation.

The name back propagation comes from the fact that the error of the hidden neurons (layers) is reduced

by propagating backward the errors associated with output unit. The target values for the hidden neurons

are not given. In the Back propagation network, the activation function chosen is the tan sigmoid

function, which compresses the output value in a range between 0 and 1. The sigmoid function is


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advantageous since, it can accommodate large signals without saturation and allowing the small signals

without excessive attenuation. Tan sigmoid function used is given below.

7. EXPERIMENTAL SETUP

The software developed for the identification of the industrial components is a generalized one. The

software is more users friendly. In this work for experimental purpose, module is initialized to some

specified settings.

7.1 ANN Architecture

ANN is constructed with 70,[125, 250, 75] and 21 neurons in input, hidden and output layers

respectively[4]. Number of neurons in hidden layer is set to 125, 250, and 75 in order to improve the

efficiency and accuracy. This architecture is shown in figure 7.

7.2 ANN Parameters

Mean squared error (MSE) has been set to 0.01 for this network in order to obtain acceptable results

and at the same time to keep the total processing time low. Maximum number of neurons in the hidden

layer improves efficiency (125, 250, and 75). In the present work, the total output error T error = ∑ Dop –

Oop, is less than 0.01 or maximum number of iteration reaches 100 then the training stops, Where Dop is

desired output and Oop is obtained output. If network-learning rate is high then it stops at local minima

and if the learning rate is very low then convergence time is high. So, in this work learning rate is set as

0.3.

7.3 Vision System

The vision system for robots is given by means of two cameras. One camera is placed at the front and

the other at 90. The resolution of the camera is set to 128 x128 for this work. The height of the top

camera is 15 cm from the bottom camera. The GUI is shown in figure 8.

OUTPUT = 1/ (1 + eai)

ai=wij*oj + ti

Where a is activation level


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8. APPLICATION

Various objects commonly used in the real industrial application are passed through the conveyor.

The required object is given as input to the already trained neural network. Once it gets the input it starts

capturing the images of the components traveling on the conveyor.

The captured image is processed and ANN identifies that component. (The way of identification is

shown in the figure 9). After identification, robot will move and pushes the component from the conveyor

and dropped it in the bin placed behind the conveyor. Then robot comes back to its original position.

Again it searches for the input component until we stop the robot.

9.0 MERITS AND DEMERITS

9.1 Merits

Camera does not require a driver to operate. i.e. independent of the OS.

Brightness, resolutions and other settings can change as per user and environment.

Separate module is provided for controlling the robot.

ANN is a generalized network.

Any number of images can be trained.

Image identification time is faster.


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9.2 Demerits

Learning time increases exponentially as the number of training cases increases.

It requires some memory for Database operations.

If the position of the cameras changed then it requires training.

10. CONCLUSION

The proposed system can be implemented without substantial modifications, using standard and

commonly available equipments such as digital camera and a personal computer. Thus the cost involved

is very less. The developed ANN system has been tested and the results are satisfactory. Further

development of this research is being contained in the two main directions, namely improvement in the

integration of the system for more complex situations. Second, the identification of components at

various light sources and various environments.

References

Arnir Said And William A Pearlman, “Set Partitioning In Hierarchical Tree Method”,

Http://Www.Cipr.Rpi.Edu/Research/Spiht.Html.

Bill Green, 2002,”Raster Data Tutorial”, http:/www.Pages.Drexel.Edu/~Weg22/Colorbmp.Html.

Francoise Fogelman Soulie., Patrick Gallinari., 1998, “Industrial Applications Of Neural Network”,

World Scientific Publishing Co. Ltd, Singapore,.

Gori M., Frosoni A., Pistolesi L., 1998, “Number Plate Recognition In Practice: The Role Of Neural

Network”, World Scientific Publishing Co. Lid, Singapore, Pp. 249 -254.

Richard I.Mammone, 1994, “Artificial Neural Networks For Speech And Vision”, Chapman & Hall.

Dr.S.R.Sivanandam, 2000’ Artificial Neural Networks’, Aicte Paper, January.

Thomas Riga, “Backpropagation Algorithm”, Http://Www.Thomas/Magister/Magi.Html

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