Prezi

ROBOTICA

Robotics is the study of robots. Robots are machines that can be used to do jobs. Some robots can do work by themselves. Other robots must always have a person telling them what to do.

How Does NASA Use Robots? 
NASA uses robots in many different ways. Robotic arms on spacecraft can move large objects in space. Robotic spacecraft can visit other worlds. Robotic airplanes can fly without a pilot aboard. NASA is studying new types of robots. These will work with people and help them.

What Are Robotic Arms?
NASA uses robotic arms to move large objects in space. The "Canadarm" robotic arm is on the space shuttle. The International Space Station has the larger Canadarm2. The space shuttle uses its arm for many jobs. The Canadarm can release or recover satellites. Astronauts have used it to grab the Hubble Space Telescope. This let them fix the Hubble. The shuttle and space station arms work together to help build the station. The robotic arms have added new parts to the space station. The arms also can move astronauts around on spacewalks. The space station's arm can move to different parts of the station. It moves along the outside of the station like an inchworm, attached at one end at a time. It has a robotic "hand" named Dextre that can do smaller jobs. An astronaut or someone in Mission Control must control these robotic arms. The astronaut uses controllers like joysticks used to play video games.

How Do Robots Explore Other Worlds?
Robots help explore space. Spacecraft that explore other worlds, like the moon or Mars, are robots. These include orbiters, landers and rovers on other planets. The Mars rovers Spirit and Opportunity are robots. Other robotic spacecraft fly by or orbit other worlds. These robots study planets from space. The Cassini spacecraft is this type of robot. Cassini studies Saturn and its moons and rings. The Voyager and Pioneer spacecraft are now traveling beyond our solar system. They are also robots. People use computers to send messages to the spacecraft. The robots have antennas that pick up the message commands. Then the robot does what the person has told it to do.  

How Does NASA Use Robotic Airplanes?
NASA uses many airplanes that do not carry pilots aboard. Some of these airplanes are flown by remote control. Others can fly themselves, with only simple directions. Robotic planes help in many ways. They can study dangerous places. For example, they might be used to take pictures of a volcano. They let NASA try new ideas for aircraft. These planes can fly for a long time without the need to land. They also can be smaller than a plane flown by a pilot. They may not have room for a person to be on board.

How Can Robots Help Astronauts?
NASA is developing new robots to help people in space. One of these ideas is called Robonaut. Robonaut looks like the upper body of a person. It has a chest, head and arms. Robonaut could work outside a spacecraft. It could do work like an astronaut on a spacewalk. With wheels or another way of moving, Robonaut could work on another world. Robonaut could help astronauts on the moon or Mars.

Another robot idea is called SPHERES. These small robots look a little like soccer balls. SPHERES are being used on the space station to test how well they can move there. Someday, robots could fly around the station helping astronauts.

NASA is studying other ideas for robots. A small robotic arm could be used inside the station. A robot like that might help in an emergency. If an astronaut were seriously hurt, a doctor on Earth could use the arm to perform surgery. This technology could help on Earth, as well. Doctors could help people in faraway places where there are no doctors.

Robots also can be used as scouts to check out new areas to be explored. Scout robots can take photographs and measure the terrain. This helps scientists and engineers make better plans for exploring. Scout robots can be used to look for dangers and to find the best places to walk, drive or stop. This helps astronauts work more safely and quickly. Having humans and robots work together makes it easier to study other worlds.

https://www.nasa.gov/audience/forstudents/k-4/stories/nasa-knows/what_is_robotics_k4.html

DOMOTICA

WHAT IS DOMOTICA?

 For one domotica is a home that life course is installed. For others, it is in every room speakers, blinds / shades / blinds and screens with automatic light when you enter the room. To us, domotica is installation and integration of the needs of the user, so that the facilities are nice and easy to operate and optimally come into their own. This helps the installation to the comfort of the user. Actually it is about lifestyle and not about the installation itself. You just live better. If you go to bed, with your touch panel remote you can close all the curtains or blinds, turn down the heat, turn down the lights except for the way to your bedroom and security. You can see at a glance that everything is in order and that gives a pleasant feeling. Another service we often is offer the 24-hour monitoring. All modules in the house be called once per hour from a central MR Domotica at to see if it is still functioning. If something is not working we can take preventative action. Sometimes we can reset things online, but we will contact you to make an appointment if that is not possible. It even goes so far that we can warn the customer that the lamp in his home cinema projector is nearing the end of its life. Each system is once broken, that's a given, but with proper monitoring, you can avoid a lot. One of the reasons why we can call ourselves a leader is that very high service level. Our unique overarching vision is another reason.

Home automation (also called domotics) is a field within building automation, specializing in the specific automation requirements of private homes and in the application of automation techniques for the comfort and security of its residents. Although many techniques used in building automation (such as light and climate control, control of doors and window shutters, security and surveillance systems, etc.) are also used in home automation, additional functions in home automation include the control of multi-media home entertainment systems, automatic plant watering and pet feeding, and automatic scenes for dinners and parties.

The main difference between building automation and home automation is, however, the human interface. In home automation, ergonomics is of particular importance: the control should be largely image-based and self-explanatory.

When home automation is installed during construction of a new home, usually control wires are added before the drywall is installed. These control wires run to a controller, which will then control the environment.

http://future.wikia.com/wiki/Domotics

Expectations of AI and Robotics

Artificial intelligence is defined as that intelligence exhibited by artifacts created by humans, often hypothetically applied to computers. The study of AI is one of the oldest in the field of science but although this study is not as resent the most fascinating advances have been made in recent years, after each advance is always looking at the next and this has done That this type of studies advance rapidly. The expectations with respect to the subject are too many since it is a very ample and interesting subject that gives us many questions and makes the imagination fly.

Artificial Inteligence and robotics together promise more than anything we have imagined, if in this present they have already achieved amazing things like language simulation, physical reactions and logical responses, understanding; The future of these research and projects is fascinating and motivating for new scientists to take care of projects that could revolutionize our daily lives.

http://iaunetrajn.blogspot.pe/2011/1         0/inteligencia-artificial-desarrolladores.html

Artificial Neural Network

Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry a unidirectional signal with an activating strength that is proportional to the strength of the connection between those neurons.^{[further explanation needed]} If the combined incoming signals are strong enough, the "postsynaptic" neuron becomes activated and a signal propagates to downstream neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.

Typically, neurons are connected in layers, and signals travel from the first (input), to the last (output) layer. Modern neural network projects typically have a few thousand to a few million neural units and millions of connections; their computing power is similar to a worm brain, several orders of magnitude simpler than a human brain. The signals and state of artificial neurons are real numbers, typically between 0 and 1. There may be a threshold function or limiting function on each connection and on the unit itself, such that the signal must surpass the limit before propagating. Back propagation is the use of forward stimulation to modify connection weights, and is sometimes done to train the network using known correct outputs.^{[further explanation needed]} However, the success is unpredictable: after training, some systems are good at solving problems while others are not. Training typically requires several thousand cycles of interaction.^{[citation needed]}

The goal of the neural network is to solve problems in the same way that a human would, although several neural network categories are more abstract. New brain research often stimulates new patterns in neural networks. One new approach is use of connections which span further to connect processing layers rather than adjacent neurons. Other research being explored with the different types of signal over time that axons propagate, such as deep learning, interpolates greater complexity than a set of boolean variables being simply on or off. Newer types of network are more free flowing in terms of stimulation and inhibition, with connections interacting in more chaotic and complex ways. ^{[clarification needed]} Dynamic neural networks are the most advanced, in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.^{[citation needed]}

Historically, the use of neural network models marked a directional shift in the late 1980s from high-level (symbolic) artificial intelligence, characterized by expert systems with knowledge embodied in if-then rules, to low-level (sub-symbolic) machine learning, characterized by knowledge embodied in the parameters of a cognitive model with some dynamical system.

Genetic Algorithms

A genetic algorithm (GA) is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm randomly selects individuals from the current population and uses them as parents to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution.

You can apply the genetic algorithm to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear.

The genetic algorithm differs from a classical, derivative-based, optimization algorithm in two main ways, as summarized in the following table.

https://www.mathworks.com/discovery/genetic-algorithm.html

Artificial Intelligence

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.^[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".^[2]

As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology.^[3] Capabilities currently classified as AI include successfully understanding human speech,^[4] competing at a high level in strategic game systems (such as chess and Go^[5]), self-driving cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

AI research is divided into subfields^[6] that focus on specific problems, approaches, the use of a particular tool, or towards satisfying particular applications.

https://en.wikipedia.org/wiki/Artificial_intelligence