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European Summit on Robots, Artificial Intelligence and Autonomous Vehicles , will be organized around the theme “A Review on Innovations and Advancements in Technology”
Euro Robotics 2019 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Euro Robotics 2019
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Robots can be used in any situation and for any purpose, but today many are used in dangerous environments, manufacturing processes, or where humans cannot survive. Robots can take on any form but some are made to resemble humans in appearance. This is said to help in the acceptance of a robot in certain replicative behaviours usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, and basically anything a human can do. Many of today's robots are inspired by nature, contributing to the field of bio-inspired robotics. After various recent achievements in medical robotic research, people have begun to recognise the distinctive advantages of using robots for medical purposes. The main reasons that have drawn much attention to robotic systems result from their capability in carrying out a variety of surgical and other medical tasks with high accuracy and repeatability, and their ability to provide surgeons with enhanced visual feedback.
Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences. Human has been a topic of both science fiction and academic speculation even before any robots existed. Because HRI depends on knowledge of (sometimes natural) human communication, many aspects of HRI are continuations of human communications topics that are much older than robotics.
Sophisticated technology, for a majority of manufacturing activities in fabrication, forming, machining and assembly facilities, will be a significant contributor to productivity improvement with substantial gains in the quality of products in the face of tough challenge and competition Industrial Robots have been in use for about 50 years. The Present-Day Robots at Work: Industrial Robots have come to play a widespread and crucial role in many industrial operations today. These robots are almost always of the Jacquard type—with few human features— rather than the Jacquet-Droz, doll-like style. The work that robots do can be classified into three major categories: in the assembly and finishing of products; in the movement of materials and objects; and in the performance of work in environmentally difficult or hazardous situations.
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Robotic surgery or robot-assisted surgery, allows doctors to perform many types of complex procedures with more precision, flexibility and control than is possible with conventional techniques. Robotic surgery is usually associated with minimally invasive surgery procedures performed through tiny incisions. It is also sometimes used in certain traditional open surgical procedures. Robots are currently used not just for prostate surgery, but for hysterectomies, the removal of fibroids, joint replacements, open-heart surgery and kidney surgeries. They can be used along with MRIs to provide organ biopsies. Since the physician can see images of the patient and control the robot through a computer, he/she does not need to be in the room, or even at the same location as the patient. This means that hospitals must evaluate the cost of the machine vs. the cost of traditional care. If robotic surgery cuts down on the trauma and healing time, there is money saved in terms of the number of days the patient stays in the hospital. There is also a reduction in the amount of personnel needed in the operating room during surgery.
The rehabilitation community is at the threshold of a new age in which orthotic and prosthetic devices will no longer be separate, lifeless mechanisms, but intimate extensions of the human body—structurally, neurologically and dynamically. Prosthetics and orthotics devices employ a force controllable actuator and a biomimetic control scheme that automatically modulates ankle impedance and motive torque to satisfy patient-specific gait requirements. Although the device has some clinical benefits, problems still remain. The force-controllable actuator comprises an electric motor and a mechanical transmission, resulting in a heavy, bulky, and noisy mechanism. As a resolution of this difficulty, we argue that electroactive polymer-based artificial muscle technologies may offer considerable advantages to the physically challenged, allowing for joint impedance and motive force controllability, noise-free operation, and anthropomorphic device morphologies.
Flying opens new opportunities to robotically perform services and tasks like search and rescue, observation, mapping or even inspection and maintenance. As such, substantial interest in aerial robots has grown in recent years. Key areas to be addressed include, but are not limited to, innovative Unmanned Aerial Vehicles design, autonomous missions, guidance, navigation and control, safety and certification, risk assessment, multi-vehicle coordination, UAS traffic management (UTM).
An autonomous car is a vehicle that can guide itself without human conduction. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. An autonomous car is also known as a driverless car, robot car, self-driving car or autonomous vehicle
For autonomous agents, it is essential to have a clear view and understanding of their surroundings to be able to navigate safely. To generate this view many different types of sensors including vision, lidar, radar, ultrasound, hyperspectral, infrared and other sensors can be used. The fusion can be based on a time series of data from the same type or from several different sensing modalities, the latter called multi-modal sensor fusion. Multi-modal sensor fusion offers advantages in terms of being able to sense various complementary aspects of an object or scene with the different modalities, i.e. increasing information gain. Due to the uncertain nature of sensor measurements, the fusion of the (time series) data is non-trivial. And as soon as the sensors are mounted on a moving agent the issue of synchronization, drift, blur and others make the fusion yet more difficult. Fusing sensors with different modalities is even more challenging because the sensors perceive different aspects of the environment. One sensor might sense an object while the other sensor might not see it at all because of the material of the object, for instance seeing through glass with a visual camera and getting a return with an ultrasound sensor. Oftentimes the sizes and the distances to the objects are also reported differently. These aspects make multi-modal sensor fusion non-trivial.
Deep learning is the science of training large artificial neural networks. Deep neural networks (DNNs) can have hundreds of millions of parameters allowing them to model complex functions such as nonlinear dynamics. They form compact representations of state from raw, high-dimensional, multimodal sensor data commonly found in robotic systems and unlike many machine learning methods, they do not require a human expert to hand-engineer feature vectors from sensor data at design time. DNNs can, however, present particular challenges in physical robotic systems, where generating training data is generally expensive, and sub-optimal performance in training poses a danger in some applications. Yet, despite such challenges, robotcists are finding creative alternatives, such as leveraging training data via digital manipulation, automating training, and employing multiple DNNs to improve performance and reduce training time.
As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. A significant part of artificial intelligence deals with planning or deliberation for system which can perform mechanical actions such as moving a robot through some environment. This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Other parts which sometimes are described as belonging to artificial intelligence and which are used in relation to computer vision is pattern recognition and learning techniques. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is considered an example of an intelligent agent. Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents.
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is considered an example of an intelligent agent. Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents to distinguish them from their real-world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents.
An intelligent transportation system (ITS) is an advanced application which, without embodying intelligence as such, aims to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. An intelligent Transportation system is an emerging transportation system which is comprised of an advanced information and telecommunications network for users, roads and vehicles. An intelligent Transportation system is the integrated application of advanced Technologies using electronics, computers, communications, and advanced sensors. These applications provide travellers with important information while improving the safety and efficiency of the transportation system.
Artificial intelligence is a behaviour based-system concept in robot. Artificial Intelligence brings intelligent behaviour to the robot to be able to provide services to humans in unpredictable and changing environments, such as homes, hospitals, the work place, and all around us. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. Artificial intelligence is accomplished by studying how human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. In the real world, the knowledge has some unwelcomed properties
The abundance of increasingly affordable computing power lends itself to new and demanding applications. In the environmental field, one of the most demanding problem areas is that of environmental systems analysis, subsuming the areas of policy design, planning and management. These areas and their problems are characterized by their multi- and inter-disciplinary nature, as well as the often dominating importance of political and judgemental elements, as opposed to purely technical, scientific problems. Thus, since the classical, formal approaches to technical problem solving are not strictly applicable, and the people involved are not necessarily technically trained experts but will include elected representatives, interest groups, and the general public, new methods of problem-solving, or applied systems analysis, and new methods of communicating scientific and technical information have to be developed.
Big data is a term for a large data set. Big data sets are those that outgrow the simple kind of database and data handling architectures that were used in earlier times, when big data was more expensive and less feasible. For example, sets of data that are too large to be easily handled in a Microsoft Excel spread sheet could be referred to as big data sets. Data mining refers to the activity of going through big data sets to look for relevant or pertinent information. This type of activity is really a good example of the old axiom "looking for a needle in a haystack." The idea is that businesses collect massive sets of data that may be homogeneous or automatically collected. Decision-makers need access to smaller, more specific pieces of data from those large sets. They use data mining to uncover the pieces of information that will inform leadership and help chart the course for a business.
Technology is embedded in everything we do, improving the ways we live, work, and experience the world. But there’s a larger transformation at play–a shift beyond digital into an era where tech is built into every single interaction. Disruption courtesy of emerging technologies is nothing new, in fact, we predicted it. But this latest transformation is unique. For the first time, the change is a two-way street. People aren’t just using products and services, but feeding information and access back to them. To deliver integrated innovation, companies need a profound level of insight into people’s lives. For intelligent enterprise, this level of connection—and this degree of trust—require a new type of relationship. It’s not just business. It’s personal. And it’s how leaders will redefine their company based on the company they keep.
Augmented reality is defined as "an enhanced version of reality created by the use of technology to add digital information on an image of something”. AR is used in apps for smartphones and tablets. AR apps use your phone's camera to show you a view of the real world in front of you, then put a layer of information, including text and/or images, on top of that view.
Virtual Reality is defined as "the use of computer technology to create a simulated environment”. When you view VR, you are viewing a completely different reality than the one in front of you. Virtual reality may be artificial, such as an animated scene, or an actual place that has been photographed and included in a virtual reality app.With virtual reality, you can move around and look in every direction -- up, down, sideways and behind you, as if you were physically there. You can view virtual reality through a special VR viewer, such as the Oculus Rift. Other virtual reality viewers use your phone and VR apps, such as Google Cardboard or Daydream View.
Cyber security refers to the body of technologies, processes, and practices designed to protect networks, devices, programs, and data from attack, damage, or unauthorized access. Cyber security may also be referred to as information technology security. Cybersecurity is important because the government, military, corporate, financial, and medical organizations collect process and store unprecedented amounts of data on computers and other devices. A significant portion of that data can be sensitive information, whether that is intellectual property, financial data, personal information, or other types of data for which unauthorized access or exposure could have negative consequences. An organization transmit sensitive data across networks and to other devices in the course of doing businesses and cybersecurity describes the discipline dedicated to protecting that information and the systems used to process or store it.
The Internet of things (IoT) is the network of physical devices, vehicles, home appliances and other terms embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing Internet infrastructure The Internet of things allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. When Internet of things is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, virtual power plants, smart homes, intelligent transportation and smart cities.