Emerging trends in robotics using neural network

1. M.S.P.VELAYUTHA NADAR POLYTECHNIC COLLEGE
PAVOORCHATRAM
BY:
S.DEVA JOHNSON
P.VENGADESH
EMERGING TREND ROBOTICS IN NEURAL NETWORK

2. Contents:
1. Abstract
2. Introduction
3. What is Robot?
4. Why it needed?
5. Quality
6. Laws of Robotics
7. Applications of Robotics.
 Military Services
 Car Production
 Space Exploration
 Underwater Exploration
 Commercialized Agriculture
8. What is Neural Network?
9. Involvement of neural networks.
10.Involvement of neural networks.
11.Top 5 Emerging Technologies In 2015
 Robotics 2.0
 Neuromorphic engineering
 Intelligent nanobots
 3D printing
12.Conclusion
13.References

3. ABSTRACT
The purpose of this paper is to provide an overview of the research being done in neural
network approaches to robotics, outline the strengths and weaknesses of current
approaches, and predict future trends in this area.
INTRODUCTION
An important area of application of neural networks is in the field of robotics. Usually,
these
networks are designed to direct a manipulator, which is the most important form of the
industrial
robot, to grasp objects, based on sensor data. Another applications include the steering
and
path-planning of autonomous robot vehicles.
In robotics, the major task involves making movements dependent on sensor data. There
are four, related, problems to be distinguished:

4. WHAT IS ROBOT?
A robot is a mechanical or virtual artificial agent, usually
an electromechanical machine that is guided by a computer program or electronic
circuitry, and thus a type of an embedded system.
WHY IT NEEDED?
There are many different reasons for using a robot but the central reason for most
applications is to eliminate a human operator. The most obvious reason is:
o To save labor and reduce cost.
Other classes of applications concern the product:
o Human is bad for the product for example semiconductor handling.
Within this class are other reasons for using robots for example food
handling, pharmaceuticals, etc.
o Product is bad for the human for example radioactive product.
Within the above are other reasons for using robots for example robots can
be used to replace human operators where the dangers are:
1. Repetitive strain syndrome.
2. Working with machinery that is dangerous for example presses, winders.
3. Working with materials which might be harmful in the short or long term.
QUALITY
while the main reason for using a robot is to save labor the biggest impact a robot has can
be on quality.
Applications where quality will be improved are:

5. 1. gluing,
2. spraying (glue or paint),
3. trimming and de-burring,
4. Testing and gauging.
5. assembly
6. laboratory routines
LAWS OF ROBOTICS
The term robotics was coined in the 1940s by science fiction writer Isaac Asimov. In a
series of stories and novels, he imagined a world in which mechanical beings were
mankind's devoted helpmates. They were constrained to obey what have become
known as Asimov's Laws of Robotics:
1. A robot may not injure a human being, or, through inaction, allow a human being to
come to harm.
2. A robot must obey the orders given it by human beings except where such orders
would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict
with the First or Second Law.
APPLICATIONS OF ROBOTICS.
Sometimes a human operator can do better than the robot in terms of quality or speed but
the robot will do the task consistently.
1. MilitaryServices: Military robots are some of the
most high-tech and important robots used today. These
state-of-the-art machines save lives by performing
extremely dangerous tasks without endangering
humans.

6. 2. Car Production: Robots are used in the automobile
industry to assist in building cars. These high-powered
machines have mechanical arms with tools, wheels and
sensors that make them ideal for assembly line jobs.
3. Space Exploration: One of the most amazing areas of
robotics is the use of robots in space. These state-of-the-art
machines give astronauts the chance to explore space in the
most mind-boggling ways.
4. Underwater Exploration: Underwater robots have
radically changed the way we see the world from the ocean
floor. Underwater robots can dive longer and deeper than any
human, and they provide an up-close look at marine life.
5. CommercializedAgriculture: Farming has been performed by man since the
beginning of time, but throughout the years robots have
been introduced to the world of commercial agriculture.
Like manufacturing jobs, robots have the ability to work
faster, longer and more efficiently than humans in agriculture.
WHAT IS NEURAL NETWORK?
An artificial neural network (ANN), usually called neural network (NN), is a
mathematical model or computational model that is inspired by the structure and/or
functional aspects of biological neural networks.

7. ADVANTAGE OF NEURAL
NETWORK:
 They provide a straightforward
mapping between sensors and
motors
 They are robust to noise (noisy
sensors and environments)
 They can provide a biologically
plausible metaphor
ROBOTIC IN NEURAL NETWORK:
 Deep Neural Networks (DNNs) are well known for doing amazing things, but why
are they not used more in robotics?
 If you have a neural network that can recognize things, why not couple it up to a
robot's camera and let it control the robot? At the moment we have reached the
point where if you look around the labs and the different work
 Neural Networks in Robotics is an integrated view of both the application of
artificial neural networks to robot control and the neuromuscular models from
which robots were created
 . The behavior of biological systems provides both the inspiration and the
challenge for robotics. The goal is to build robots which can emulate the ability of
living organisms to integrate perceptual inputs smoothly with motor responses,
even in the presence of novel stimuli and changes in the environment
 The ability of living systems to learn and to adapt provides the standard against
which robotic systems are judged. In order to emulate these abilities, a number of
investigators have attempted to create robot controllers which are modelled on
known processes in the brain and musculoskeletal system

8.  Neural Networks in Robotics provides an indispensable reference to the work of
major researchers in the field. Similarly, since robotics is an outstanding
application area for artificial neural networks, Neural Networks in Robotics is
equally important to workers in connectionism and to students for sensor monitor
control in living systems.
TOP 5 EMERGING TECHNOLOGIESIN 2015
Emerging Technologies – Most of the global challenges of the 21st century are a direct
consequence of the most important technological innovations of the 20st century.
New technology is arriving
faster than ever and holds the
promise of solving many of the
world’s pressing challenges
such as food and water
security, energy sustainability
and personalised medicine.
Lighter, cheaper and flexible
electronics made from organic materials have found endless practical applications and
drugs are being delivered via nanotechnology at the molecular level, at the moment just
in medical labs.
However, outdated government regulations, inadequate existing funding models for
research and uninformed public opinion are the greatest challenges in effectively moving
emerging technologies from the research labs to people’s lives.

9. 1. ROBOTICS 2.0
A new generation of robotics takes machines away from just automating the most
manual manufacturing assembly line tasks and orchestrates them to collaborate in
creating more advanced assemblies, subassemblies and complete products.
Collaborative robotics can accelerate time-to-market, improve production accuracy
and reduce rework. By using GPS technology that is commonly available in
smartphones, robots can
be used in precision
agriculture for weed
control and harvesting.
We’ve seen robots that
can walk like an ape and
run like a cheetah, robots
that can mix a perfect
martini, help the disabled,
or drive you to the store.
Robots could replace
soldiers on the battlefield.
In Japan, robots are being
tested in nursing roles: they help patients out of bed and support stroke victims in
regaining control of their limbs.
Artificial Intelligence, machine learning and computer vision are constantly
developing and perfecting new technologies that “enable the machine” to perceive
and respond to its ever changing environment. Emergent AI is the nascent field of
how systems can learn automatically by assimilating large volumes of information.
An example of this is how Watson system developed by IBM is now being deployed

10. in oncology to assist in diagnosis and personalised, evidence-based treatment options
for cancer patients.
1. NEUROMORPHIC ENGINEERING
Neuromorphic engineering, also known as neuromorphic computing started as a
concept developed by Carver Mead in the late 1980s, describing the use of very-large-
scale integration (VLSI) systems containing electronic analogue circuits to mimic
neurobiological architectures present in the nervous system.
A key aspect of neuromorphic
engineering is understanding
how the morphology of
individual neurones, circuits
and overall architectures
creates desirable computations,
affects how information is
represented, influences robustness to damage, incorporates learning and development,
adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic
Computing is next stage in machine learning.
IBM’s million “neurones” TrueNorth chip, revealed in prototype in August 2014, has
a power efficiencyfor certain tasks that is hundreds of times superior to conventional
CPU’s and comparable for the first time to the human cortex. The challenge here
remains creating code that can realise the potential of the TrueNorth chip, an area
IBM continues investing in today.
2. INTELLIGENT NANOBOTS
Again, Emergent AI and Computer Vision will provide drones with human like
capabilities allowing them to complete tasks too dangerous or remote for humans to do

11. like checking electric power lines or delivering medical supplies in an emergency for
example.
Autonomous drones will improve agricultural yields by collectingand processing vast
amounts of visual data from the air, allowing precise and efficient use of inputs such as
fertiliser and irrigation.
Ambulance drones that
can deliver vital medical
supplies and “on screen”
instructions. Drones with
mounted camera to
“learn” about
surroundings – with no
information about the
environment or the
objects within it- by
using reference points and different angles, it builds a 3D map of surroundings, with
additional sensors picking up barometric and ultrasonic data. Autopilot software then
uses all this data to navigate safely and even seek out specific objects. Autonomous.
Intelligent. Swarming. Nano Drones.
4) SPACE ROBOTICS
Valkyrie humanoid robot that will
help astronauts on a journey to Mars.
NASA Valkyrie Robots Prepare for
Life on Mars
NASA needs help improving robot
dexterity. Teams in the competition

12. must program a virtual robot to complete a series of tasks in a simulation that includes
periods of latency to represent communications delay from Earth to Mars. Each team’s
R5 will be challenged with resolving the aftermath of a dust storm that has damaged a
Martian habitat. This involves three objectives: aligning a communications dish, repairing
a solar array, and fixing a habitat leak.
This technology could also benefit humankind on Earth, as they could operate under
dangerous or extreme environments on our home planet.
“Precise and dexterous robotics, able to work with a communications delay, could be
used in spaceflight and ground missions to Mars and elsewhere for hazardous and
complicated tasks, which will be crucial to support our astronauts.
ROBOT TO PERFORMACTIONS OF SERVICE DOGS
SResearchers at Georgia Tech have built a
biologically inspired robotto perform actions
of service dogs
Users issue verbal commands to robot, and
indicate object with laser pointer.
Eg. Fetching items, or closing doors or
drawers.
Worked with trainers of dogs
Conclusion
The paper presents our first results that we obtained making use of the proposed path
planning algorithm working with the neural network and sensor data. The simulation
examples of the generation of the collision free path for point robot and for two
dimensional robot show that designed strategy are acceptable for solution of this
problem. We played the role of the supervisor to learn the robot to make it’s way

13. intelligently toward its target and to avoid obstacles. In future we will implement this
technique for safe motion of our experimental mobile vehicle in indoor conditions. We
suppose to use this algorithm not only for the robot motion in known environment but for
unknown one, as well. It is necessary to test different parameters in neural network with
the aim of reaching the optimal time for finding the (shortest possible) safe path. As the
robot collects environment data currently along its path it can avoid not only the static
obstacles but also the dynamic ones. We feel that this technique will be suitable also for
the motion of mobile devices in complex environment
REFERENCES:
http://www.learnartificialneuralnetworks.com/robotcontrol.html
http://link.springer.com/article/10.1007/BF00368972
https://wtvox.com/robotics/top-5-emerging-technologies-in-2015/
http://www.i-programmer.info/news/105-artificial-intelligence/8619-a-robot-learns-to-
do-things-using-a-deep-neural-network.html
https://en.wikipedia.org/wiki/Neurorobotics
http://students.iitk.ac.in/eclub/assets/documentations/summer09/NeuralNetworkRobot.pd
f
http://www.neurosolutions.com/apps/files/Neural-Networks-in-Mobile-Robot-Motion.pdf