International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 5- Dec 2013
Intelligent Servant Robot with Artificial Neural
Network
Prof. Hari Ram Vishwakarma#, M S Ishwarya*
#Senior Professor, *PG student,
SITE, VIT, Vellore
Abstract: This work aims at proposing a design of a self
learning intelligent porter robot that is constructed with
ANN. The basic functionality of the robot includes
obstacle avoidance and path learning through its codes as
well as ANN. The robot is expected to work in an
industrial or domestic environment, where it port objects
from one place to another provided place is being
instructed to it. The robot is fed with blue print of its
working environment. The robot has a skid wheels and an
arm of 3 degrees of freedom.
Key Words: Robot, Artificial neural network and obstacle
I. INTRODUCTION
Every factory, industry, home always has a need to
port things from one place to another place for one or other
reasons. Since porting objects needs human involvement, it
costs in terms of effort, money and time. Moreover, it may be
tiring if it involves more complicated tasks in terms of
number and their weight. So, this project concentrates on
developing a simple, self learning, obstacle avoidable porter
robot. So, the first and foremost requirement is obstacle
avoidance. The robot should be capable of tackling the
obstacles and proceed forward to complete its given task. If
this is solved, then the robot should be capable of moving
along the path to finish the task by itself. Later, robot learns
the obstacles that it is facing every time and checks for the
same the other time while using the same path.
II. RELATED WORK
An obstacle avoiding robot from a recent work [1]
by a group of UG students from Siliguri Institute of
Technology has promising results. This robot is capable of
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avoiding obstacle when they are in right front of them. The
mechanical design of the robot has two sensors, the eyes for
the robot. The robot always moves in forward direction
unless there is an obstacle, else it decides whether to move
left or right. And now there is quite a possibility when two
robots are working in same environment then there may be
clash of robot when the other robot is not in front of the
robot. Another similar work on self-learning robot by Meyer
and Filliat was successful on map-based navigation to reach
the destination [2]. The robots designed were capable of
navigating through the path with reflex action either going
behind or avoiding obstacle that were registered into the
robot’s memory during previous navigation. They tackle the
memorized objects and expect obstacles in certain fashion.
The problem with the above self-learning robot is that it can
prove its intelligence only in expected or controlled
environment. Robotic arm presented in ARSO [3] with two
fingered parallel soft gripper is used to pick and place thin
objects. The drawback of this work is, it can pick and place
only thin, light weight objects which humans can do easily. It
cannot port the objects that are found challenging by humans.
III. PROPOSED WORK
The proposed work overcomes the drawbacks from
the related work mentioned above. It involves developing an
intelligent porter robot that can port objects from one place to
another based on the pre-fed blue print. Moreover, this robot
will be capable of learning about the obstacles that it might
face and checks for same when it is encountering an almost
similar scenario for the other time. This robot will have an
arm that has up to more than two degrees of freedom.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 5- Dec 2013
Fig 1. Flow Chart Explaining the Proposed Algorithm
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 5- Dec 2013
A. THE PROPOSED ROBOT AND IS CONSTITUENTS
The mechanical parts of the robot include arm,
wheels, motor, sensors and supporting stand. The control
board of the robot will have a microcontroller that is
programmed with basic blue print of the place as well as the
learning logic. The basic blue print helps the robot to
complete its given task where as the learning logic helps the
robot to makes it smarter as it is aging. We also present the
robot with training data set, which will help the robot to
prepare itself for learning.
The arm of the robot is of high load tolerant metals like
iron with three parts namely shoulder, elbow and wrist. Also,
it is attached with an adjustable clamp at the end to pick up
the object and carry them.
This robot will use the skid steering wheels with 4
wheels which gives additional balance for the robot in normal
as well as porting state. In addition to this, the robot will have
a supporting stand.
The robot will have an infrared sensor that can be fitted in
maximum possible angles so that it will have a vision almost
in its entire circumference. But the order in which it is
sensing its circumference will decide the next move of the
robot towards the destination. Robot will have a DC gear
motors with heavy load characteristics needed to pull the
robot along with the object that is being carried.
B. INTELLIGENCE
The framework that is being used to make the robot
intelligent is Leabra [4] (Local Error-driven and Associative,
Biologically Realistic Algorithm). A variety of template
under this framework is available in Emergent software. This
model helps the subject to have balanced learning between
Hebbian and Error-driven learning with other network-driven
characteristics. And Lebra is default algorithm in Emergent
[5].
The robot has
Input, decide and move.
communicates the robot
supposed to take. The
learned value decides
destination.
three well known layers namely
But the decide layer computes and
regarding the next move that it is
training set, input value and the
the robot’s move to reach the
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The blue print will act as a part of robot’s knowledge and the
expected obstacles and the learned obstacles will act as a
training set of the robot which is expanding as per the weight
of the network. The more frequent path as well as the
obstacle that is encountered for more number of times will be
registered in the network with highest weight. The weights of
the network tend to change based on the learning of the
robot. If needed, the robot will be capable of creating a new
network branch as a result of learning. The above flow chart
explains the proposed algorithm.
The function of the robot can be explained
better with a scenario in its working environment. Assume a
scenario; robot is waiting in production plant to carry the
instructed goods to instructed destination. The robot is
instructed to carry ‘Batch X’ to ‘warehouse Y’. Now the
robot picks up the batch objects and moves towards the path
to the destination with the help of the blue print of the
building that is being loaded into the robot’s memory.
Assume that the robot is encountering an obstacle or another
robot. Now the robot stops immediately and scans for the
obstacles in its circumference. The future robot undergoes the
computation of the shortest path to reach the destination and
confirms if there is no obstacle in the newly decided path,
they will move along the decided path. If the same robot is
made to move to carry a new batch of objects to the same
‘Warehouse Y’ and if the robot encounters the same obstacle
even this time, it skips the computation but confirms that
there is no obstacle in the already decided path before it
makes a move. In short, every computation made by the
robot is registered in robot’s mind for its next move. The
robot retains the memory of such instance only if they occur
frequently else it erases by itself if it exceeds the threshold
memory for unused instances.
V APPLICATIONS
This robot can be used in the industries and factories.
The terminologies (places and paths are in countable numbers
inside a factory) and the objects (similar objects are
produced) are limited in number. The robot can also be used
in railway stations, hospitals, malls, etc.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 5- Dec 2013
VI. CONCLUSION
The prototype of proposed work is evident as expected.
Under normal circumstances, the robot avoids obstacles for 9
out of 10 times.
In simulating with Emergent, the
intelligence given to robot decides the best solution for the
given input obstacle based on the training data set and rules.
In terms of porting objects, the robot works well while
simulating a robotic arm with the above specified features.
VII. SCOPE
This proposed work can be extended for general purpose
porting if the robot is equipped with super intelligence and
better situation handling skills.
VII. REFERENCE
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