Knowledge Discovery by Image Recognition in Robots
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Transcript of Knowledge Discovery by Image Recognition in Robots
8/7/2019 Knowledge Discovery by Image Recognition in Robots
http://slidepdf.com/reader/full/knowledge-discovery-by-image-recognition-in-robots 1/7
A Technical paper presentation on the topic
KNOWLEDGE DISCOVERY BY IMAGE
RECOGNITION IN ROBOTS Authorised By
SANTOSH BHARADWAJ REDDY
Email: [email protected]
Engineeringpapers.blogspot.com
More Papers and Presentations available on above
site
Abstract:
Till now we have the ROBOTS
which will only execute the program and run
those parts according to that. But, my
project aim is to provide the social
intelligence, common sense to the robots i.e.
we have to design the ROBOTS that will
make decision itself according to the
situation based on how the things are going
on surrounding it. The robot that acts similar
to the human brain. For example, If there is
no one in the room in which AC is ON, the
robot must be able to switch OFF the AC to
save the power, and many more as human
brain takes decision. In this paper we will
discuss about the small topic in my whole
project i.e. human face recognition
technique.
Introduction:
Definition of a Robot
• "A reprogrammable, multifunctional
manipulator designed to move material,
parts, tools, or specialized devices through
8/7/2019 Knowledge Discovery by Image Recognition in Robots
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various programmed motions for the
performance of a variety of tasks" .
• Or a simpler version
• An automatic device that performs
functions normally ascribed to humans or a
machine in the form of a human.
What is a Robot
• The term robot derives from the Czech
word robota, meaning forced work or
compulsory service, or robotnik, meaning
serf.
Sensors:
• Sensors that tell the robot
position/change of joints: odometers,
speedometers, etc.
• Force sensing. Enables compliant
motion--robot just maintains contact
with object (video: compliant)
• Sonar. Send out sound waves and
measure how long it takes for it to be
reflected back. Good for obstacle
avoidance.
• Vision systems
Effectors:
• Conve
rts
software commands into physical
motion
• Typically electrical motors or
hydraulic/pneumatic cylinders
• Two main types of effectors:
locomotion
manipulation
Locomotion:
Legs!
traditional (video: honda human)
Other types Statically stable locomotion:
can pause at any stage during
its gate without falling
Dynamically stable
locomotion: stable only as
long as it keeps moving
(video: hopper)
• Still, wheeled or tread locomotion
like Shakey is still most practical for
typical environments
• Other methods: reconfigurable
robots, fish robots, snake-like robots.
(video: mod-robot)
Manipulation:
Manipulation of objects
Typical manipulators allow for:
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Prismatic motion (linear
movement)
Rotary motion (around a
fixed hub)
• Robot hands go from complex
anthromorphic models to simpler ones
that are just graspers
(video: manipulation)
(video: heart surgery)
There are two basic problems for the robots
to work according to the environment. They
are:
1) Localization and Mapping
2) Motion Planning
Localization:
Finding the location where it is
right now????
How Do We Solve Localization??
There are many algorithms to
solve Localization problems. They are:
1) Markov localization (simplest)
2) Kalman filters (historically most
popular)3) Monte Carlo localization /
particle filters
Mapping:
Finding map of the environment
of the robot by using implicit sensors
(location of the robot as it moves).
Motion Planning:
• Simplest task that a robot needs to
accomplish
• Two aspects:
Finding a path robot should
follow
Adjusting motors to follow
that path
• Goal: move robot from one
configuration to another.
AssumptionThis part of the paper deals with the
basic idea of us i.e. recognizing the human
faces and providing the information about
the person. This is done by the process as
given below:
First, if a person stands before the
robot and talked with that then while talking
to the human, robot follows a procedure of
retrieving the required data. The procedure
is:
1) The robot activates the recording
device i.e. camera to record the
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video of the human being behind
to it.
2) It recognizes the human face by
using one of the face recognition
techniques like video based
tracking and recognition.
3) After identifying the human face
it checks the database for the
same face that is previously
stored.
4) If the image is already exists then
it recollects the information
about the person that is stored
previously in the last interaction.
5) If it is not present then it follows
the following steps:
a.First it asks for the name of the
person.
b. Stores the face with the name
of that person in the database.
c.Creates the instance to the
variable which can store a large
amount of the data related to
that person.
d. It formally listens to the
person and converts the voice
into text.
For all the above discussion consider an
example of the conversation of the human
being with the robot:
In the above figure, a person is talking to the
robot: when he said Hi! to the robot then in
response the robot will also say Hi! with
some expression and body movements
accordingly as he moves. Mean time, it
records the face of that person and checks
the database for the exact face, if it is not
present in the database then it stores the face
of that human being by asking the name of
that person as:
“May I know your Name Please?”
Then in response the person will tell his
name as: “Eigen ville” then it store the face
with the name of that person and create an
instance to store further information
regarding to that person.
And further the interaction may be done
like this:
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Man: “what’s your name?”
Robot: “this is Android, from where you
are?”
Man: ……..
……….
With this conversation according to the
keywords used by the human being it will
stores the important information like Name,
Place, Age, Education, and Personal Details
and even according to the conversation and
reaction of the person it can also come to
estimation about the character of that person
and accordingly him it will give the
response to the interaction.
This is what we mean as
commonsense, as human brain acts
according to the situation. Similarly, we can
design the robot as providing the ability to
act according to the situation.
Here the face recognition is done
from a video using the Hue value. The
process is according to this:
8/7/2019 Knowledge Discovery by Image Recognition in Robots
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In this paper we used only the face
recognition technique. And we have many
more topics to cover to run the robot
according to our aim. The concepts that we
have to cover are: the face comparison
techniques, technique used in Voice to Text
converter, techniques used to provide the
commonsense, video database i.e. storing
the pictures and videos for further recall of
the situation and many more.
Conclusion:
In this paper we have covered the
topics like important points about the robots,
use of robots, purpose of robots main parts
of robots, problems facing for automation of
robots, technique used to recognize the
human face. With this paper we came to an
idea on our aim to provide the information
about the persons that are talking to robot.
References:
• Automatic Face Recognition Using
Color Based Segmentation and
Intelligent Energy Detection,
Michael Padilla and Zihong Fan.
• Robotics, Dr.Julie and A. Adams
from Vanderbilt University.
8/7/2019 Knowledge Discovery by Image Recognition in Robots
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Authorised By
SANTOSH BHARADWAJ REDDY
Email: [email protected]
Engineeringpapers.blogspot.com
More Papers and Presentations available on above
site