Industrial robotics

1. SALEM COLLEGE OF ENGINEERING AND
TECHNOLOGY
SALEM - 636 111.
PAPER PRESENTATION ON
INDUSTRIAL ROBOTICS
DEPARTMENT OF MECHATRONICS ENGINEERING
Presented by,
HASSIFUL HUSSAN ALI A (III MECHATRONICS)
PRINCE MAMMAN (III MECHATRONICS)
EMAIL
hasimajid@gmail.com

2. ABSTRACT:
A reprogrammable, multifunctional
manipulator designed to move material,
parts, tools, or specialized devices. Its
various programmed motions for the
performance of a variety of tasks.
“Intelligence” as their motto, industrial
equipment manufacturers are focusing on
developing technology. It have the joints in
many links on joints provide relative
motion and links are rigid members
between joints. The stress on “sensing”,
“representation” and “action” is motivated
by the need of distributing AI principles
and methods at various levels of robot
architectures, with respect to both
hardware and software aspects. It proposes
techniques to capture some of the search
space pruning that dual evolution offers in
the domain of robot programming. It
explores the relationship between robot
morphology and program structure, and
techniques for capturing regularities
across this mapping.
Index Terms - Cognitive Spatial
Representation, Robot Mapping, Special
Issue on Artificial Intelligence in
Robotics: Sensing, Representation and
Action.
1. INTRODUCTION
In the past, factory production lines
were automated for mass production, and
many industrial robots and specialized
machines were introduced. Recently,
flexible manufacturing systems, such as the
cell production system (unlike in the line
production method, an entire product is
assembled by one worker), are being
introduced in an increasing number of
production sites in order to deal with
differentiation of products and to meet
diversified needs. However, many of the
tasks in flexible manufacturing systems rely
heavily on workers because the number of
parts to be handled is larger so the time and
costs required to switch product types on
robots and specialized machines is greater.
Recently, because of the decrease in
the working population due to Japan's aging
society with a falling birth rate, there are
expectations that tasks which rely heavily on
workers will be automated by using
industrial robots in combination with
sensing technology and production know-how.
With “intelligence” as their motto,
industrial equipment manufacturers are
focusing on developing technology that will
automate tasks that are currently performed
by humans, but the types of tasks that have
so far been automated are very limited.
This paper introduces the concept of
a system in which industrial robots are
applied to (1) picking work, (2) medium
payload handling work, (3) assembly work,
etc. and also introduces the research and
development of such a system.
1.1. ROBOT ANATOMY:
It is the study of structure of robot.
The mechanical structure of robots consist
of rigid bodies (links) connected by means
of joints is segmented into an arm that
ensures mobility and reach ability, a wrist
that confers the orientations and an end
effectors that performs the required task.
Manipulator is constructed of series of joints
and links. A joint provides relative motion
between the Input links and output links.
Each joint provides the robot with one
degree of freedom.

3. 1.1.1. THE ROBOTIC JOINTS:
A robotic joint is mechanism that
permits relative motion between parts of a
robotic arm. The joints of a robot are
designed to move its end effector along a
path from one position to another as
desired.
· LINEAR JOINT
· ROTATIONAL JOINT
· TWISTING JOINT
· REVOLVING JOINT
1.1.2. ROBOT CONFIGURATION:
i. Polar configurations
ii. Cylindrical configurations
iii. Cartesian coordinate configurations
iv. Jointed arm configurations
1.1.3. POLAR CONFIGURATIONS:
It has axes.
· One linear joint, two rotary
joints.
· The rotational axis, the bent
axis, and the reach axis.
· It is also called spherical robots.
· In this arm configuration sit is
connected to base with twisting
joint.
2.1. CYLINDRICALCOFIGURA -
TIONS:
· One rotary joint and two linear joints
· The rotational axis,The bent axis,and
the reach axis
· It is found mostly in pick and place
arms for assembly purpose.
2.1.2. CARTESIAN COORDINATES:
It has three slide joints of which two
are orthogonal
i. Three slides are parallel to
three axes
ii. All arm joints are linear
Movement along the entire three axis can
occur simultaneously.
Jointed arm coordinate system:
It has three rotational axes
(a) Waist rotation
(b) Shoulder rotation
(c) Elbow rotation
2.2. ROBOT WELDING
In the use of mechanized
programmable tools (robots), which
completely automate a welding process by
both performing the weld and handling the
part. Processes such as gas metal arc
welding, while often automated, are not
necessarily equivalent to robot welding,
since a human operator sometimes prepares
the materials to be welded. Robot welding is
commonly used for resistance spot

4. welding and arc welding in high production
applications, such as the automotive industry
Robot welding is a relatively new
application of robotics, even though robots
were first introduced into US industry
during the 1960s. The use of robots in
welding did not take off until the 1980s,
when the automotive industry began using
robots extensively for spot welding. Since
then, both the number of robots used in
industry and the number of their applications
has grown greatly. In 2005, more than
120,000 robots were in use in North
American industry, about half of them for
welding.] Growth is primarily limited by
high equipment costs, and the resulting
restriction to high-production applications.
Robot arc welding has begun
growing quickly just recently, and already it
commands about 20% of industrial robot
applications. The major components of arc
welding robots are the manipulator or the
mechanical unit and the controller, which
acts as the robot's "brain". The manipulator
is what makes the robot move, and the
design of these systems can be categorized
into several common types, such as
the SCARA robot and Cartesian coordinate
robot, which use different coordinate
systems to direct the arms of the machine.
3. HAND GUIDING SYSTEM
Assembly and handling of
workpieces with complicated shapes
requires precise positioning. To fully
automate such tasks, expensive sensors and
advanced and complicated controls are
needed. In addition, there are still many
problems to solve, for example, it may be
impossible to measure some parts of a work
piece.
(a) Configuration of the entire system
The positioned depending on its shape, and
even if automation is successful, “minor
stoppage” (equipment does not fail but
temporarily stops due to minor
abnormalities, though it can be restored in a
short time) occurs frequently, preventing the
utilization ratio from increasing.
One possible solution to address

5. these problems is to classify tasks into those
that robots are suited for and those that
humans are suited for so that robots and
humans can work cooperatively.
(a) Positions of the automation
IHI developed a hand guiding system that
complies with ISO 10218-1: 2006 in order to
enable cooperative operations between
industrial robots and humans can be operated
in manual and automatic modes, and the robot
performs tasks that can be performed based on
teaching-playback and sensing.
4. COOPERATIVE HANDLING
At production sites, tasks that require
handling various types of parts are
performed mainly by workers. When
handling workpieces of different sizes,
robots need to change tools, but people can
handle them by using both hands with
dexterity.
In general, one large robot that fits the
largest workpiece is used when handling
work pieces of different sizes. IHI is
developing a new system based on the idea
that using two or more robots that fit smaller
work piece provides enhanced versatility.
a) Appearance of the system
.
In addition, because a large
workpiece is handled by two or more robots,
the load can be distributed. Therefore, the
size of the hand can be reduced, the
structure of the hand can be simplified, and
more typesof workpieces can be handled
with just one type of hand.
(b) Result of 3D object recognition
Moreover, because the robot and its
hand are smaller, it is easier to keep them
from interfering with workpieces as they

6. approach the workpieces, offering the
advantages of storing parts in bins.
Furthermore, the holding positions can
easily be changed by changing the robot’s
program, facilitating the addition of work
piece types.
5. ROBOT WELDING
In the use of mechanized
programmable tools (robots), which
completely automate a welding process by
both performing the weld and handling the
part. Processes such as gas metal arc
welding, while often automated, are not
necessarily equivalent to robot welding,
since a human operator sometimes prepares
the materials to be welded.
Robot welding is commonly used
for resistance spot welding and arc
welding in high production applications,
such as the automotive industry
Robot welding is a relatively new
application of robotics, even though robots
were first introduced into US industry
during the 1960s. The use of robots in
welding did not take off until the 1980s,
when the automotive industry began using
robots extensively for spot welding. Since
then, both the number of robots used in
industry and the number of their applications
has grown greatly.
In 2005, more than 120,000 robots
were in use in North American industry,
about half of them for welding. Growth is
primarily limited by high equipment costs,
and the resulting restriction to high-production
applications.
Robot arc welding has begun
growing quickly just recently, and already it
commands about 20% of industrial robot
applications. The major components of arc
welding robots are the manipulator or the
mechanical unit and the controller, which
acts as the robot's "brain".
The manipulator is what makes the
robot move, and the design of these systems
can be categorized into several common
types, such as the SCARA robot and
Cartesian coordinate robot, which use
different coordinate systems to direct the
arms of the machine.
6. AUTOMATED GUIDED
VEHICLE
An automated guided vehicle or automatic
guided vehicle (AGV) is a mobile robot that
follows markers or wires in the floor, or uses
vision or lasers. They are most often used in
industrial applications to move materials
around a manufacturing facility or a

7. Ware house. Application of the automatic
guided vehicle has broadened during the late
20th century.
Automated guided vehicles (AGVs)
increase efficiency and reduce costs by
helping to automate a manufacturing facility
or warehouse. The first AGV was invented
by Barrett Electronics in 1953. The
AGV can tow objects behind them in trailers
to which they can autonomously attach. The
trailers can be used to move raw materials or
finished product.
The AGV can also store objects on a
bed. The objects can be placed on a set of
motorized rollers (conveyor) and then
pushed off by reversing them. AGVs are
employed in nearly every industry,
including, pulp, paper, metals, newspaper,
and general manufacturing.
7. RoboLogix
Is a robotics simulator which uses
a physics engine to emulate robotics
applications. The advantages of using
robotics simulation tools such as RoboLogix
are that they save time. In the d
They can also increase the level of
safety associated with robotic equipment
since various "what if" scenarios can be
tried and tested before the system is
activated.
Robot Logic provides a platform to
teach, test, run, and debug programs that
have been written using a five-axis industrial
robot in a range of applications and
functions. These applications include pick-and-
place, palletizing, welding, and
painting.
8. Industrial paint robots
It have been used for decades
in automotive paint applications from the
first hydraulic versions - which are still in
use today but are of inferior quality and
safety - to the latestelectronic offerings. The
newest robots are accurate and deliver
results with uniform film builds and exact
thicknesses.
Originally industrial paint robots
were large and expensive, but today the
price of the robots have come down to the
point that general industry can now afford to
have the same level of automation that only

8. the big automotive manufacturers could
once afford.
The selection of today’s paint robot
is much greater varying in size and payload
to allow many configurations for painting
items of all sizes. The prices vary as well as
the new robot market becomes more
competitive and the used market continues
to expand.
Painting robots are generally
equipped with five or six axis, three for the
base motions and up to three for applicator
orientation. These robots can be used in any
explosion hazard Class Division
environment.
9. Robotics Simulator
A robotics simulator is used to
create embedded applications for
a robot without depending physically on the
actual machine, thus saving cost and time. In
some case, these applications can be
transferred on the real robot (or rebuilt)
without modifications. The term robotics
simulator can refer to several different
robotics simulation applications. For
example, in robotics applications, behavior-based
robotics simulators allow users to
create simple worlds of rigid objects and
light sources and to program robots to
interact with these worlds. Behavior-based
simulation allows for actions that are more
biological in nature when compared to
simulators that are more binary, or
computational. In addition, behavior-based
simulators may "learn" from mistakes and
are capable of demonstrating
the anthropomorphic quality of tenacity.
9.1. Genetic Programming
One way to solve the programming
problem might be to use Artificial Life
techniques to evolve behavior-based
programs. Previously many workers have
used genetic algorithms to program software
agents, typically running in cellular worlds.
It demonstrates the evolution of both
neural networks and finite state machines
through a genetic algorithm running on a bit
string representation. More conventional
computer programs have also been
processed with genetic algorithms, such as
the pioneering work of.
Robot programs, and in particular
behavior-based robot programs, are much
more complex than any programs that have
been reported in the literature to have been
so evolved. A reasonable comparison might
be in terms of the memory taken to represent
the programs. By this measure behavior-based
robot programs axe three orders of

9. magnitude larger than those mutated
competitively by genetic techniques.
Recently, however, has shown a
number of stimulating results by applying
genetic algorithms directly to lisp-like
programs rather than to more traditional bit
strings.
He has been very successful in a
number of domains with this technique;
rekindling earlier interest in the idea of
mutating lisp program structures directly
shows an example of synthesizing the base
behaviors of behavior-based robot programs.
He makes a number of simplifying
assumptions, and reduces the search space
significantly by carefully selecting the
primitives by hand after examining Metric’s
source code.
• Most likely the evolution of robot
programs must be carried out on simulated
robots unfortunately there is a vast
difference (which is not appreciated by
people who have not used real robots)
between simulated robots and physical
robots and their dynamics of interaction with
the environment.
• The structure of the search space of
possible programs is very dependent on the
representation used for programs and the
primitives available to be incorporated.
Careful design is necessary.
• Natural evolution co-evolved the structure
of the physical entities and their neural
controllers in a way which arguably cut
down the size of its search space.
9.2. Simulations of Physical Robots
The number of trials needed to test
individuals precludes using physical robots
for testing the bulk of the control programs
produced for them by genetic means. The
obvious choice is to use simulated robots
and then run the successful programs on the
physical robots.
Previously we have been very careful to
avoid using simulations for two fundamental
reasons.
• Without regular validation on real robots
there is a great danger that much effort will
go into solving problems that simply do not
come up in the real world with a physical
robot..
• There is a real danger (in fact, a near
certainty) that programs which work well on
simulated robots will completely fail on real
robots because of the differences in real
world sensing and actuation very hard to
simulate the actual dynamics of the real
world.
At the time of writing no complete
experiments have been carried out using the
ideas in this paper.
We have built a simulator for
multiple R-2 robots1 It is not grid-based, but
instead the coordinates of a robot can be
arbitrary floating point numbers within the
workspace. R-2 robots have a two wheeled.
Differential drive with passive castors for
stability. The simulator handles arbitrary
independent velocities on the two wheels.
There is a simple physics associated with
motion of a robot when it has collided with
in obstacle (which is all modeled as
immovable cylinders).

10. The sensors currently modeled are a
ring of eight bump sensors, a ring of eight
infrared proximity sensors, and three
forward looking beacon sensors. We expect
to add more sensor models. No explicit
uncertainty is built into the sensor or
actuator models.
Noise is therefore introduced into the system
by the load on the computer collector. The
complete simulation is about 500 lines of
combined Common Lisp and BL code. We
have no hypothesis at this point about how
well programs developed on the simulator
will transfer to the real.
CONCLUSION
This report has introduced the
concept of a system in which industrial
robots were applied to picking work,
medium payload handling work, assembly
work, etc. by combining IHI’s hard-earned
innovations, such as advanced sensing
technology, control technology, and
mechanics technology with industrial robots,
and has also introduced the research and
development of such a system.
It is becoming possible to apply
industrial robots to tasks that cannot easily
be automated and thus rely heavily on
human workers. In addition, robots work
long hours and handle heavy objects without
getting tired or making mistakes, leading to
improved quality