Emerging trends in robotics using neural network

Emerging trends in robotics using neural network

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  M.S.P.VELAYUTHA NADAR POLYTECHNICCOLLEGE PAVOORCHATRAM BY: S.DEVA JOHNSON P.VENGADESH EMERGING TREND ROBOTICS IN NEURAL NETWORK
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  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
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  ABSTRACT The purpose ofthis 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:
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  WHAT IS ROBOT? Arobot 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:
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  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.
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  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.
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  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
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   Neural Networksin 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.
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  1. ROBOTICS 2.0 Anew 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
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  in oncology toassist 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
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  like checking electricpower 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
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  must program avirtual 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
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  intelligently toward itstarget 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