16.Robotics & Visions Warm Intelligence & Traffic Safety

8/8/2019 16.Robotics & Visions Warm Intelligence & Traffic Safety

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A Technical Paper Presented

ROBOTICS & COMPUTER VISION IN

SWARM INTELLIGENCE & TRAFFIC SAFETY

BY

V.RAMASAMY D.ANBUMANIEmail:[email protected] [email protected]

TO

E-GLITZ08

DEPARTMENT OF ELECTRONICS & COMMUNICATION

ENGINEERING,

MAHA ENGINEERING COLLEGE,

CHENNAI,HIGHWAY,

SALEM-106.


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ABSTRACT

An automotive controller that

complements the driving experiencemust work to avoid collisions, enforce

a smooth trajectory, and deliver the

vehicle to the intended destination as

quickly as possible. Unfortunately,

satisfying these requirements with

traditional methods proves intractable

at best and forces us to consider

biologically -inspired techniques like

Swarm Intelligence. A controller is

currently being designed in a robot

simulation program with the goal of

implementing the system in real

hardware to investigate these

biologically inspired techniques and to

validate the results. In this paper I

present an idea that can be

implemented in traffic safety by the

application of Robotics & Computer

Vision through Swarm Intelligence.

.

In eighteenth century the concept of

programmable machine came into

existence and since then modernization

of machine has taken place which has

given momentum to the branch ofrobotics. The history of automotion

characterized by periods of rapid

changes in popular methods. But since

1960, the robots became a versatile

device in worlds technology and has

given significant contribution to same.

There are several devices that not truly

robot but are often called by this name

by media, so they also need to be

classified along with classification of

the robots. The architecture and the

different methods in which a can be

programmed is also included in this

paper. It will not an exaggeration to

say robot as unique device in todays

technology world as it has got a huge

number of applications such as in

medicine, space, military, etc. Looking

over these applications of robots, we

can call future age as Age of

Robotics .

INTRODUCTION

We stand today at the culmination of

the industrial revolution. For the last

four centuries, rapid advances in

science have fueled industrial society.

In the twentieth century,

industrialization found perhaps its

greatest expression in Henry Ford's

assembly line. Mass production affects

almost every facet of modern life. Our

food is


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mass-produced in meat plants,

commercial bakeries, and canaries.

Our clothing is shipped by the ton from

factories in China and Taiwan.

Certainly all the amenities of our lives

- our stereos, TVs, and microwave

ovens - roll off assembly lines by the

truckload. Today, we're presented with

another solution that hopefully will

fare better than its predecessors. It goes

by the name of post-industrialism, and

is commonly associated with our

computer technology.

ARTIFICIAL INTELLIGENCE.

Robots are today where computers

were 25 years ago. They're huge,

hulking machines that sit on factory

floors, consume massive resources and

can only be afforded by large

corporations and governments. Then

came the PC revolution of the 1980s,

when computers came out of the

basements and landed on the desktops.

So we're on the verge of a "PR"

revolution today. Personal Robotics

revolution, which will bring the robots

off the factory floor and put them in

our homes, on our desktops and inside

our vehicles

WHAT IS A ROBOT?

Figure 1: Robotic Automobile

Assembly System.

Robots can take many forms-contrast

Star Wars R2D2 and C3PO with the

Sojourner Rover that ambled around

the Martian countryside last year, and

with a factory robot arm that spends 24

hours a day welding.

But every robot has two attributes:

A "brain," which could be anything

from a sophisticated computer down to

a primitive control program.

Movement (either the computer itself

moves, or it controls an arm or other

moveable part).

There is no standardized definition,

although efforts are under way to

develop one.

A robot has three essentialcharacteristics:

It possesses some form of mobility


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It can be programmed to accomplish

a large variety of tasks

After being programmed, it operates

automatically

Figure 2: Robot Mine Detectors

A computer is not a robot because it

lacks mobility. Special-purpose

machines are not robots because they

automate only a few tasks. Remote

control devices work only with human

participation and therefore are not

robots.

The word 'robot' entered the English

language in 1923 when the play

'R.U.R. (Rossum's Universal Robots)',

written by the Czech author Karel

Capek, was produced in London. (In

Czech the word 'robota' means 'heavy

labour'.) The robot concept remained

science fiction until 1961 when

Unimation Inc. installed the world's

first industrial robot in the US.

Australia's first robot, also made by

Unimation Inc., was introduced in

1974. Up to now, most of the

approximately 650,000 robots installed

worldwide have been used in

manufacturing. Typical applications

are welding cars, spraying paint on

appliances, assembling printed circuit

boards, loading and unloading

machines, defense, in satellite and

telecommunication, surgery, and

placing cartons on a pallet. The

automobile and metal-manufacturing

industries have been the main users.

The mobility of these robots generally

has been limited to a programmable

mechanical arm. In some installations

the platform on which the robot arm is

mounted can travel automatically

along a fixed rail.

The International Organization for

Standardization (ISO) has developed

an international standard vocabulary

(ISO 8373) to describe 'manipulating

industrial robots operated in a

manufacturing environment'.

According to this standard such a robot

must possess at least three

programmable axes of motion.

The International Federation of

Robotics (IFR) and the Australian

Robot Association follow this ISO

standard when compiling robot

statistics. Machines working in a


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manufacturing environment that have

only one or two programmable axes of

motion therefore are not included in

these statistics.

Although the vast majority of robots

today are used in factories, advances in

technology are enabling robots to

automate many tasks in non-

manufacturing industries such as

agriculture, construction, health care,

retailing and other services. Australias

most famous robotics research project

was concerned to develop a robot

capable of shearing sheep.

Technologies that are being developed

to extend robot capabilities include

machine vision and other sensors,

vehicles that can travel automatically

on a variety of, and mechanisms able

to manipulate flexible materials

without damaging them. It is

anticipated that robots will be utilized

in the 21st century not only in industry

but also at home. Potential domestic

applications include assisting

elderly or busy people to carry out

tasks such as cleaning or cooking. The

ISO has not yet produced a surfaces

standardized definition of a robot used

in non-manufacturing applications.

According to the IFR such a robot is 'a

machine which can be programmed to

perform tasks which involve

manipulative and in some cases

locomotive actions under automatic

control'. Robots initially have been

installed in factory environments

where the tasks to be done can be

precisely controlled. However, it is

impossible to program a robot so that it

always acts correctly in an

environment that is poorly understood

or loosely structured. Occasional

human intervention will be required to

provide high-level guidance to robots

working in such

Environments. The design of suitable

human/robot interfaces is expected to

become an important priority.

MOTIVATION

The goal of this project is to work

toward developing a complementing

automotive controller that improves

upon the driving experience. The

controller will monitor certain road

conditions and will override the human

driver only in emergency situations.

When overriding, it should have three

critical priorities:

Minimize propensity and severity of

collisions. No control system is

perfect. It is impossible to

guarantee the elimination of

automobile collisions. Automobiles are

complex mechanical and (increasingly)

electronic systems. In the rare case that

enough components fail at the same


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time, no amount of redundancy can

immediately restore correct operation

of the vehicle. The goal of any system,

given a certain cost, is to minimize the

probability of a collision and, if a

collision is unavoidable, lessen the

severity of the impending collision.

Enforce a smooth ride. A control

system, which causes an automobile to

violently weave through traffic, should

be considered inferior to a system,

which sends the car along a smoother,

more predictable trajectory. An

uncomfortable and unpredictable ride

is unpleasant for the passengers and

may be dangerous for other drivers on

the road. A smoother ride also results

in less wear and tear on the

components of the automobile and

prolongs the life span and reliability of

critical parts.

Get the passenger from point A to

point B as quickly and efficiently as

possibly. Of course, the

ultimate goal of any automatic

vehicular controller is to deliver the

passenger to his/her intended

destination. If this proves to be

unrealizable (hardware fault, streets

closed, etc) then the system should

give the passenger the option to abort

the trip or transport the passenger to a

point as close as possible to the

original intended destination.

Figure 3. Junction Of Streets.

These requirements imply the

necessity of introducing a priority-

based architecture for the

complementing controller. The

controller will do all it can to deliver

the passenger(s) to the destination as

quickly as possible unless this results

in an un smooth ride. Likewise, the

controller will enforce a smooth ride

unless the safety of the passenger(s)

is/are threatened. The approach I

propose is the insertion of an

intermediate controller situated

between the human driver and the

automobile actuators. In addition to the

above constraints, the automobile

control system must also be able to

cope with a diversity of vehicles and

drivers while coping with theenvironment in a robust way.


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RESEARCH

Satisfying all of these conditions

would be a tall order for traditional

control algorithms. As a result, welook for inspiration from biological

systems. The principal advantage of a

biologically inspired approach is that

such techniques have stood the test of

eons of competition and evolution. Not

only are these techniques robust, they

also have the advantage of scalable and

distributed operation, as well as

acceptance of existing heterogeneous

agents. A specific biologically inspired

approach that seems well optimized for

understanding collective phenomena

(like automobile traffic) is Swarm

Intelligence. Swarm Intelligence

provides a framework in which

autonomy, emergence, and distributed

robustness replace centralized control.

This is analogous to comparing birds

flocks to a complex man- made air-

traffic control system that results in

countless flight delays and lost

luggage.

Figure4. Screenshot Of Webots2.0

Simulation Program.

IMPRESSION

Sample traffic situations will be

simulated in the WEBOTS 2.0

simulator. The simulated

automobiles are controlled by a

subsumption architecture. A simplified

model of a human driver (which is

aware of his/her speed, orientation, and

what lies within his/her field of view)

will just try to avoid other cars and

follow the lanes. If, for

whatever reason, the simulated human

driver causes the car to enter any

undesirable situation, the driver will

first be warned. Only when the

situation becomes dire and requires

immediate evasive action, will the

complementing controller override the

driver. In all other cases, the

commands given by the driver

(steering wheel angle, gas/brake pedal


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deflection, etc) are passed directly to

the actuators.

Figure 5. Prototype Implementation

Of Traffic Scenario With Real

Robots.

The complementing controller will

have access to data from on-board

obstacle sensors and lane sensors in

order to have an awareness of the state

of the environment. The sensors on the

Road that uses Radio signals will

provide the necessary traffic

information. Evolutionary techniques

will be used to suggest optimal

placements and configurations for the

sensors on the vehicle as well as other

controller parameters. Using the GPS

(Globe Positioning Satellite) System

the on-board computer systems gives

relative location of the vehicle. The

initial simulations will take place on a

straight three-lane highway but curved

streets may be added later (see Figure

4). Currently, the Webots simulator

does not simulate the holonomicity of

real automobiles. Specifically, Webots

was designed to simulate small round

robots with two wheels on either side.

This presents a problem in regards to

how a real traffic situation should be

scaled so that a simulation can be

realistic. An impending software

revision of the simulator should

resolve the issue. The Webots

simulator is also not optimized for very

large macroscopic simulations

(hundreds of automobiles and drivers).

For extremely large simulations,

cellular automata-based platforms

(e.g. Transits) may eventually have to

be leveraged. Finally, implementing

this system in real robots (see Figure 5)

would provide concrete confirmation

or refutation of any conclusions

obtained during simulation.

BIBLIOGRAPHY

Swarm Intelligence: From Natural to

Artificial Systems. Bonabeau, E.,


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Dorigo, M., Theraulaz, G. New York:

Oxford University Press, 1999.

A Robust Layered Control System for

a Mobile Robot. Brooks, R. IEEE

Journal of Robotics and Automation,

1996.

Robot Herds: Group Behaviors for

Systems with Significant Dynamics.

Proceedings of Artificial Life IV,

1994. Reynolds, C.

Personal Robotics by Brent Baccala.

http://www.linux-guruz.org/

http://www.robot-

automation.com/robot-automation-

system.htm

http://www.sony.com/

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16.Robotics & Visions Warm Intelligence & Traffic Safety