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ABSTRACT
Object Identification plays an important role in various applications
ranging from robotics to computer vision. Artificial neural network (ANN) is being
used for in various pattern recognition applications due to the advantages of ability
as well as adaptability to learn. This paper presents identification of object
independent to size, position and orientation using the concept of ANN and
moments. The image of the object is taken with the help of the tactile sensing
system. This paper describes the complete hardware and software concepts of the
system for the object identification with the help of ANN.
Keywords: Tactile sensing, Object orientation, force sensing sites.
INTRODUCTION
Of the many sensing operations performed by the human beings, the one that is
probably the most likely to be taken for granted is that of touch. Touch is not only
complementary to vision, but it offers many powerful sensing capabilities. Tactile
sensing is another name of touch sensing, which deals with the acquisition of information
about the object simply by touching that object .Touch sensing gives the information
about the object like shape , hardness , surface details and height of the object etc.
Tactile sensing is required when an intelligent robot wants to perform delicate assembly
operations. During this assembly operation an industrial robot must be capable of
recognizing parts, determining their position, orientation and sensing any problem
encountered during the assembly from the interface of the parts.
Keeping all these things in mind a tactile sensing system is developed which uses
image processing techniques for preprocessing and analysis along with ANN’s for object
identification. Classification of the object independent of translation, scale, rotation is a
difficult task. The concept of artificial neural network is used in this system for the proper
identification of the object irrespective to their size, position and the orientation.
CONFIGURATION OF TACTILE SENSING SYSTEM
This system uses conductive plasterer as a sensor, which has property that its
conductivity changes as the function of the pressure. Fig 1 shows the configuration of the
system. The conductive elastomer is mounted on 8*8 force sensing sites for the
measurement of pressure distribution on the object. These force sensing sites are
connected through PC add on data acquisition card. Stepper motor is used to apply
specific amount of pressure for proper identification of the objects. Hardware for
scanning the matrix and related signal conditioning is designed along with the stepper
motor interface circuitry. Once the image of the object is acquired through the tactile
sensing system then it is further processed using image processing concepts for the
proper identification and inferring other properties of the objects. Tactile data for the
object identification is acquired using row-scanning technique. After the removal of noise
from this tactile data it passes to the different modules of the system for further
processing.
DATA
ACQUISITOIN
CARD
TACTILE
IMAGER
MOUNTED ON
STEPPER MOTOR
DATA
INPUT
OUTPUT CARD
COMPUTER FOR
TACTILE
DATA
PROCEESSING
Figure1. Configuration of the system
TACTILE DATA PROCESSING
Main modules of this system are preprocessing, data acquisition, matrix
representation graphical representation, edge detection and moments calculations for
generating a feature vector. Tactile image acquisition involves conversion of the pressure
image into an array of numbers that can be manipulated by the computer. In this system
tactile sensor is used to obtain the pressure data of the object and this data is further
acquired with the help of data acquisition and data input output card, which are interfaced
with the computer. The preprocessing module involves in the removal of the noise, which
is essential for acquiring the image of the object under consideration. The image is
analyzed by a set of numerical features to remove redundancy from data and reduce its
dimensions. Invariant moments are calculated in this module which is required by the
next module to the artificial neural for the identification of the object independent to
scale, rotation and position.
SYSTEM MODULES
Main module of this system includes the description of the system and gives
many options to the user for processing the tactile data in different forms like automatic
or manual processing of the data. Feature extraction module is used for the calculation of
the moments from the acquired tactile data. The application of moments provides a
method of describing the object in terms of its area, position and the orientation. These
invariant moments are used by the ANN as the input neurodes which is the important
data for the classification and identification of the object. Flow chart for taking the image
identification is shown in Fig.3.
START
N
Is
System
Ready
Y
Acquire the
Image
Pre processing of
the image
No Card
Detected
STOP
User Option for Image Identification
STOP
Figure 3. Flowchart of image identification
ARTIFICIAL NEURAL NETWORK
Artificial neural network ,which is inspired from the studies of biological
nervous system ,has been used for various applications like supervised classifier. Object
identification is decision making process that requires the neural network to identify the
class and category, which best represent the input pattern.
TACTILE
IMAGE
ACQUISITION
PRE
PROCESSING
OBJECT
IDENTIFICAITON
BY ANN
Figure 2. Tactile Data processing
To overcome the difficulty of identifying the object using the moment alone use of
artificial neural networks for the same purpose was investigated. The back propagation
algorithm was implemented in the software to generate three-layered network. The
feature vector was given as the input layer to the network. The number of input neurodes
was equal to the number of feature vectors (invariant moments). The number of output
neurodes was kept equal to the number of the objects to be identified. Here it was
considered only five objects like square, triangular, rectangular, bar, circle. Several
experiments were carried out and it was observed that for input neurodes equal to the
seven, hidden neurodes required were three .Increase in hidden neurodes increase the
complexity of the network and also increases the computational time of the system .Also
less number of hidden neurodes takes longer time to be trained .Hence the optimum value
of hidden value to be decided for the use of artificial neural network.
EXPERIMENTATION
Stepper motor is used for exerting specific amount of pressure on the object
,which is required for the handling of delicate objects, getting proper image of the object
and calculating the height of object .First of all stepper motor is arranged in such a way
that its angular motion is converted into the linear motion and it was designed in such a
way that it travels linear distance of 0.03 mm per step. This part of calibration is also
used for calculating the height of the object .To begin with ,motion of the stepper motor
was calibrated in terms of pressure applied and the linear displacement . Pressure
calibration of stepper motor is done with the help of capacitor pressure sensor in
which foam is used as dielectric between two parallel plates of
the capacitor .First
graph was plotted pressure verses the change in the value of the capacitance .Response
of the same is shown in Graph1. Then the same capacitive tactile was used to find the
number of steps verses change in capacitance response as shown in Graph 2.Then
with the help of the two graphs the third graph was plotted pressure verses number of
steps of the stepper ,Which is given in Graph 3.This process was repeated for the
many capacitive tactels. And the pressure calibration was completed in this way.
Finally It was found that stepper motor could exert pressure 19N/M2 per step.
After the calibration of the stepper motor the sensor (elastometer) was
calibrated in terms of the pressure and time response. Thus the system with stepper
motor used for applying specific amount of pressure along with the data acquisition
and data input Output card could be used for acquiring a tactile data. Programs were
developed to scan and digitize individual tactels and store the tactile information in
the form of a two dimensional array. The response of change in the ADC out put
voltage with the number of steps of the stepper motor is shown in Graph 4.
EXPERIMENTS ON OBJECT IDENTIFICATION WITH THE HELP OF ANN
After the calibration of the sensor and the system the experiments were
performed on the objects like square, triangular, circular, rectangular. The network was
trained by generating the learn data file having moments with change in the position
,size and orientation of the objects. The trained network was linked with the moments
module and derived moments were input to the neural network. Target pattern was set
at less than 0.5 for output not belonging to the class and greater than 0.5 for correct
class.
GRAPH 1
GRAPH 3
GRAPH 2
GRAPH 4
(A) Change in position
For this experiment the position of the object is varied over the array of the sensor.
And the moments were calculated for the same object. Then the network was trained
with the help of three sets of data by varying its position . Then is was observed that
network was successful to identify the object which was not included in the training set
of the object.
(B) Change in size
Size of square was varied as 2X2,4X4 ,6X6.Invariant moments calculated from
these objects and network was trained then network was given set of the data which was
not included in the training and it was observed that the network was successful to give
proper identification of the object.
(C) Change in orientation
The network was trained with set of data by changing its orientation and this
network was tested for the data, which was not included in the training set. Finally it
was observed that network can identify the objects independent of the size, orientation
and position of the object. These experiments have been carried out for the identification
of the square, triangular, circular , rectangular ,bar type of the object once the training.
Of the artificial neural network has been completed with the help of moment data file.
In this way different types of objects were tested and it was concluded that system
is intelligent enough to give best results for the identification of different types of
objects and describing the object under consideration in many ways. For further analysis
of the object the tactile data automatically passed to the different modules of the system.
CONCLUSION
This system is well suited for the classification of the object with the help of ANN
and moments. Classification and identification of the object is independent to size,
position and orientation of the object under consideration .This system has capability
of describing the object under test in different forms like identification , height ,edge,
contact area, pressure distribution ,on the object,3-D representation, orientation, position
etc of the object. The system has well support of software for processing the tactile data
in different forms and taking any decision after the identification of the object. Finally it
is observed that this system is intelligent enough for the identification of different
types of the object like square, rectangle ,circle, bar, triangle independent
to their
position ,orientation and scale and giving its many physical properties .This system has
various applications in robotics, medical and tele-operations and in computer vision.
REFERENCES:
[1].Prasant Kumar Patra, Neural network for invariant image Classification, Journal of
the IETE, vol 42, nos4-5, pp 282-290, July-October 1994.
[2].R.P.L. Rectier, Tactile Imaging, Security and Actuators, A (1992) pp 83-89.
[3].G.J. Awcock & R.Thomas , Applied image processing, McGraw Hill , pp162-166
[4]. Elepart &Bobbins (Eds), Neural Network PC Tools A practical guide
[5]. Philip D.Wasserman ANZA Reasearch, Inc Neural Computing