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Learning of Robots by Using & Sharing The Cloud Computing Techniques Pratik 3 rd Year IT Branch MITS Engineering College Rayagada, Odisha [email protected] Rahul Abhishek 3 rd Year IT Branch MITS Engineering College Rayagada, Odisha rahulmithu.abhishek @gmail.com Payal Sinha 3 rd Year IT Branch MITS Engineering College Rayagada, Odisha [email protected] ABSTRACT Robots are shaping a new era in the technology, many algorithms has been developed for their learning which they can apply to find the solution for the problems that they are facing. That is, we have tried to provide them a basic intelligence (like recognition, collision avoidance, etc.). But besides that basic intelligence we are interested in developing an intelligent system in which robots will use their experiences and share it with each other just like humans, who have got all its intelligence by his experience and imaginations and sharing it with each other. To implement this intelligent system we can use a local server (word interchangeable to database) that will be restricted to a robot only and a Cloud (global) server where the authorized robots can upload their experiences which can be used by every robot (which is authorized to that server) to solve their problems. Keywords Artificial Intelligence (AI), Robotics, (Global Server(cloud Computing), Local Server, Memory function. 1. INTRODUCTION 1.1 What is Robot? The term “Robot” is coined by Karl Capek in his play R.U.R (Rossum’s Universal Robots), which opened in Prague in 1921. Robot is Czech word for forced labour Robots are reprogrammable, multifunctional manipulator design to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of test or we can say that a robot is a simple device who can move itself They have sensors to perceive their environment and effectors to assert physical forces on it. Since robots has proven that they are now the part of human life and provide benefits to us and for getting these benefits we need some human like intelligence in them and it can be obtained by AI. 1.2. Robots Theory A robot consists of various electronics circuit, ports, sensors, effectors for its functioning. Although it seems a very complicated design but it is not so. A sensor works as an interface between the robot and environment. A robot should contain both active and passive sensor. In robots three types of sensor is used first record the distance of the object second tells about the environment by capturing the image and third one measure the property of robot that how much efficiency has it work the or to solve the problem. The effectors in robots help it to manipulate or understand the environment and with the help of effectors it can move and change its shape of body according to the environment. There is a concept known as Degree of Freedom (DOF) with the help of it we test the ability of robot to interact with the environment. We count one degree of freedom for each move of robot and its effectors. The DFOs define the kinematic state of a robot. An additional advantage of learning a robot from its experience is that it can also learn from the experience of its peers. A key feature is that, we are using two servers one is the local server restricted to a robot only while other is the global server which is shared globally with all the robots. And both these servers will hold the experiences of the robots. Fig. (2) Chart showing the interaction of robot with the environment

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Page 1: Learning of robots by using & sharing the cloud computing techniques

Learning of Robots by Using & Sharing The Cloud Computing Techniques

Pratik 3

rd Year IT Branch

MITS Engineering College Rayagada, Odisha

[email protected]

Rahul Abhishek 3

rd Year IT Branch

MITS Engineering College Rayagada, Odisha

rahulmithu.abhishek

@gmail.com

Payal Sinha 3

rd Year IT Branch

MITS Engineering College Rayagada, Odisha

[email protected]

ABSTRACT Robots are shaping a new era in the technology, many

algorithms has been developed for their learning which

they can apply to find the solution for the problems that

they are facing. That is, we have tried to provide them a

basic intelligence (like recognition, collision avoidance, etc.). But besides that basic intelligence we are interested

in developing an intelligent system in which robots will

use their experiences and share it with each other just like

humans, who have got all its intelligence by his

experience and imaginations and sharing it with each

other. To implement this intelligent system we can use a

local server (word interchangeable to database) that will

be restricted to a robot only and a Cloud (global) server

where the authorized robots can upload their experiences

which can be used by every robot (which is authorized to

that server) to solve their problems.

Keywords Artificial Intelligence (AI), Robotics, (Global

Server(cloud Computing), Local Server, Memory

function.

1. INTRODUCTION

1.1 What is Robot?

The term “Robot” is coined by Karl Capek in his play R.U.R (Rossum’s Universal Robots), which opened in

Prague in 1921. Robot is Czech word for forced labour

Robots are reprogrammable, multifunctional manipulator

design to move material, parts, tools, or specialized

devices through various programmed motions for the

performance of a variety of test or we can say that a robot

is a simple device who can move itself They have sensors

to perceive their environment and effectors to assert

physical forces on it. Since robots has proven that they are

now the part of human life and provide benefits to us and

for getting these benefits we need some human like

intelligence in them and it can be obtained by AI.

1.2. Robots Theory A robot consists of various electronics circuit, ports,

sensors, effectors for its functioning. Although it seems a

very complicated design but it is not so. A sensor works

as an interface between the robot and environment. A

robot should contain both active and passive sensor.

In robots three types of sensor is used first record the

distance of the object second tells about the environment

by capturing the image and third one measure the

property of robot that how much efficiency has it work

the or to solve the problem. The effectors in robots help it

to manipulate or understand the environment and with the

help of effectors it can move and change its shape of body according to the environment. There is a concept known

as Degree of Freedom (DOF) with the help of it we test

the ability of robot to interact with the environment. We

count one degree of freedom for each move of robot and

its effectors. The DFOs define the kinematic state of a

robot.

An additional advantage of learning a robot from its

experience is that it can also learn from the experience of

its peers. A key feature is that, we are using two servers

one is the local server restricted to a robot only while

other is the global server which is shared globally with all

the robots. And both these servers will hold the

experiences of the robots.

Fig. (2) Chart showing the interaction of robot with the environment

Page 2: Learning of robots by using & sharing the cloud computing techniques

2. What is Cloud Computing?

As a metaphor for the Internet, "the cloud" is a familiar

cliché, but when combined with "computing," the

meaning gets bigger and fuzzier. It has been envisioned as

the next generation architecture of IT Enterprise.

According to the IEEE Computer Society cloud

computing is “a paradigm in which information is

permanently stored in servers on the internet and cached

temporarily on clients that include desktop, entertainment

centers, notebooks, etc.” The Internet is commonly

visualized as clouds; hence the term “cloud computing”

for computation done through the Internet. Cloud

computing allows consumers and businesses to use

applications without installation and access their personal

files at any computer with internet access. The concept of

cloud computing dates back to the 1960s, when John

McCarthy opined that "computation may someday be

organized as a public utility." Cloud computing is a new

consumption and delivery model for IT services. The

concept of cloud computing represents a shift in thought,

in those end users need not know the details of a specific

technology. The service is fully managed by the provider.

Users can consume services at a rate that is set by their

particular needs. This on-demand service can be provided

at any time. The term "cloud" is used as a metaphor for

the Internet, based on the cloud drawing used in the past

to represent the telephone network, and later to depict the

Internet in computer network diagrams as an abstraction

of the underlying infrastructure it represents. Cloud

computing is a natural evolution of the widespread

adoption of virtualization, service-oriented architecture,

autonomic, and utility computing. It is popular for its pay-

as-you-go pricing model. Consumers of cloud services

may see increased reliability, even as costs decline due to

economies of scale and other production factors.

Fig. (1) Model of a cloud

3. RELATED WORK & PREVIOUS WORK

3.1 RoboEarth[2]

The RoboEarth project (i.e. World Wide Web for robots)

is implementing the global server for sharing the

experiences of robot. The objective of RoboEarth Project

is to enable robots from all over the world to share their

knowledge about actions, objects and the environment the

RoboEarth platform offers the ability to store reusable data in a generic and open format.

3.2 Optimization Learning Algorithms

Many algorithms have been developed for optimizing the

behavior of a machine. But here we are using

reinforcement learning algorithm for optimizing

performances of robots.

3.3 Reinforcement Learning

Reinforcement learning is the study of how animals and

the machines can learn to optimize their behavior in the

face of rewards and punishments. Reinforcement learning

algorithms have been developed that are closely related to methods of dynamic programming, which is a general

approach to optimal control. Reinforcement learning

phenomena have been observed in psychological studies

of animal behavior, and in neurobiological investigations

of neuro-modulation and addiction [3].

Reinforcement learning algorithm consist of

Set of Environment States E

Set of actions required A (Solution of a problem may

consists of sequence of actions)

Rules of transition between states

Rules that determine reward for the transition.

Rules that describe what the agent observes.[4]

4. CLOUD AND ROBOT

Cloud Computing Technology is discussing recent flare to

integrate in the world of robotics. Cloud Robot is the

result of the merger of these two technological advances.

Cloud Robot is different from all the robots that exist

today and said that the intelligence is extraordinary, even

unlimited because of a “Cloud Brain” consisting of

processor and unlimited data in the cloud (Internet). As

cloud computing has the role of the hardware devices in

the user getting smaller (transition from notebook to the

net book, tablet PCs and smart phones). Similarly, the

cloud is said in the future robots will become cheaper

Page 3: Learning of robots by using & sharing the cloud computing techniques

manufacturing costs. Due to the needs of processors in the

brain that does not require sophisticated first of all data

processing is done centrally on the server cloud. Cloud

robot is simply consisting only of input and output

devices and the entire process focuses on the cloud. Best

of the cloud robot is able to communicate with other robots of clouds to humans, even with colleagues via the

Internet. Entering Cloud computing allows a robot to do

the job, even superior to human intelligence, known as

robot cloud. Several research groups are exploring the

idea of robots that rely on cloud-computing infrastructure

to access vast amounts of processing power and data. This

approach, which some are calling "cloud robotics," would

allow robots to offload compute-intensive tasks like

image processing and voice recognition and even

download new skills instantly, Matrix-style.

A researcher suggests that the future of robotics is in cloud-computing. This means that robots could offload

their more complex tasks to remote servers that could do

the heavy computations.

A cloud connected robot can:-

Perceive

Understand

Share

React

Fig. (3) Robot connected with cloud

5. PROPOSED APPROACH

To realize this whole scenario we will use two servers one

is local to a particular robot, while other is centralized

server which can be accessed by every robot which is

authorized to that server.

The purpose of making two servers is that the local server will handle or assist the robot to work in a particular

environment and with the particular objects and as soon

as the robot will continue to work in the same

environment it will gain experience of working in that

environment and hence it will work more intelligently

while the global server will keep the solution for all the

problems (tasks assigned to robots are referred as

problems) which are faced by all the robots present

globally, and these solutions will be generalized rather

than specialized e.g. a robot working in your garden and

trimming a plant, if we look at the optimal solution then

your robot will be trimming the plant according to the size

and shape of that particular plant. But the robot at the remote location working in another garden will have

slightly different environment (difference may be very

slight) and different specification of object of interaction

(i.e. in this case, shape & size of plant). So the solution of

first robot cannot be used by second robot directly. Hence

the solution of first robot will be considered as

generalized one at the global server and the robot at

remote location will use this solution after applying few

optimization algorithms and making slight changes in the

environment variables, and specifications of object of

interaction etc as per its requirement.

5.1 Local Server (Local Database)

The scope of creating a local database is that every robot

is having a different environment to work and they deal

with different-different objects. As the robot will deal

with the objects in its surroundings, it will gain

experience of dealing with these objects by optimizing the

solution on the basis of its environment, object of

interactions and saving this solution in its local database.

So the local database will act as memory of a robot to let

it work more intelligently in a particular environment e.g.

a robot working in a hospital will have experiences about

dealing with the objects present in that hospital, these experiences will get collected in the local database and as

the time passes the robot will have good intelligence to

handle every object (of interaction) in the hospital

expertly.

5.2 Cloud Server (Global Database)

The second database we are talking about is the global

database, it is the idea through which we will share the

learning (experience) of one robot with the other robot. It

is just like the learning of human being who learnt

everything by his ideas and experiences and sharing his

experiences with each other. In broader context it is about sharing the knowledge of robot with each other to get

their task completed.

It will be implemented in the following ways. When any

problem comes or a task is assigned to a robot, first the

robot will check for the solution of that problem in its

local database if it does not exist there, it means robot is

novice to that problem then it will look at the global

server for the solution if the solution exist on the global

server then it will download it from there and use

reinforcement learning to optimize the solution according

Page 4: Learning of robots by using & sharing the cloud computing techniques

to its own environment. But if it does not exist on the

global server then the robot will apply its own learning to

find the solution of the given problem and save this

solution at both the server i.e. local server as well as

global server.

5.3.1 Problem Characteristics & variables for

global database –

Global problem Id (GPID) – Identification of the problem globally corresponding to

the problem statement.

Problem Statement – Key for searching the problem.

Environment – Description about the working condition (i.e. hospital,

railway platform, library etc)

Global Environment Id attached to a

problem (GEID) –

Generated for different-different environments of the

same problem.

Description of Object of interaction – To which the robot is interacting, may be Door, rain, train

etc or it can be null.

5.3.2 Algorithms for global server The global server has two main tasks one is uploading the

solution received from the robot to its database and

second one is granting the permission for downloading

request made by robot. The algorithms are as follows-

Algorithm for Uploading

Step1 – Create a GPID for the problem and

GEID for the environment.

Step2 – Get the values of various variables like

problem statement and the environment

variables, object of interaction etc from the

robot.

Step3 – The global server will generalize this

solution by applying some algorithms.

Step4 – Save it at global server.

Step5 – Exit.

5.3.3 Algorithm for Downloading

Step1 – Check for the problem and environment

variables at the global server if the solution corresponding

to this problem – environment set exists there then

download the solution.

Step2 – Specialize this solution by making slight

changes in its variables as required and apply the

reinforcement learning algorithm for optimization

according to its own environment.

Step3 – Use the solution, initialize memory function

and save it at local database. Step4 – Exit.

5.3.4 Constraints/Protocols

Fuzzy logic and various matching algorithms may be used

for the match when the robot is looking for the solution at

the global server.

Only authorized Robots can access the global server.

5.3.5 Global Database Functionality Robots will have to register themselves once to get access to the Global database. A robot ID (RID) will be

generated at the time of registration that will be used to

identify robot while accessing the database. As soon as

the time passes the Global database will keep expanding

hence the robots will have a good store of knowledge.

6. Representation of how the Robots will use

Cloud Server (global database)

Page 5: Learning of robots by using & sharing the cloud computing techniques

7. CONCLUSION & FUTURE WORK We have seen that how we can make robots more

intelligent to work in a particular environment by

enabling them to use their own experience to deal with the

objects present in their surrounding environments i.e. as

soon as the time passes the robot will have a good

experience of dealing with the objects present in their

surroundings. We have also seen that how a robot will use the experience of other robots to solve its own problem by

using the cloud server. In near future we can also work on

developing an intelligent agent for Robot – Human

interaction by having the similar approach that we have

seen in this paper.

8. References

[1] Hyde, Andrew Dean (Sept 28, 2010), “The

future of Artificial Intelligence”.

[2] Patterson, Dan W, (2007), “Introduction to

Artificial Intelligence & Expert Systems”,

Prentice-Hall India, New Delhi.

[3] Rich, Elaine, and Kevin Knight, (2006),

“Artificial Intelligence”, McGraw Hills Inc.

[4] Chun-Wang WEI, I-Chun HUNG, Ling LEE

& Nian-Shing CHEN April 2011 A joyful

classroom learning system with robot learning

companion for children to learn mathematics

multiplication.

[5] ICT Call 4 RoboEarth Project 2010-248942

Deliverable D6.1: Complete specification of the

RoboEarth platform December 1, 2010.

[6] Peter Dyan Gatsby, University College

London & Christopher JCH Watkins, University

of London: Reinforcement Learning.

[7] Wikipedia: Reinforcement Learning, 20th

November 2011 http://en.wikipedia.org/ wiki/

Reinforcement_learning.

[8] Prof. Hamid D. Taghirad “Robotics:

Evolution, Technology and Applications” K.n.

Toosi University of Technology.

[9] Introduction To AI Robotics, Robin R.

Murphy.

[10] Artificial Intelligence and Mobile Robots, D.

Kortenkamp, P.Bonasso, R.Murphy, editors, MIT

Press, 1998.

[10] Pratik, Rahul Abhishek, Payal Sinha. “Future

Aspect Of Artificial Intelligence”. ICWET-2012,

pp336.

[11] Pratik, Rahul Abhishek “The Relationship

Between Artificial Intelligence and Psychological

Theories”, Proceeding ICETM – Sept 2012