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See Article History Robot, any automatically operated machine that replaces human effort, though it may not resemble human beings in appearance or perform functions in a humanlike manner. By extension, robotics is the engineering discipline dealing with the design, construction, and operation of robots. American Honda Motor Co. A robot may not injure a human being , or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
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A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. This article traces the development of robots and robotics. For further information on industrial applications, see the article automation. Industrial robots Though not humanoid in form, machines with flexible behaviour and a few humanlike physical attributes have been developed for industry. The first stationary industrial robot was the programmable Unimate, an electronically controlled hydraulic heavy-lifting arm that could repeat arbitrary sequences of motions.
In Condec Corp. Unimate arms continue to be developed and sold by licensees around the world, with the automobile industry remaining the largest buyer. Industrial robot at a factory. Called PUMA Programmable Universal Machine for Assembly , they have been used since to assemble automobile subcomponents such as dash panels and lights.
PUMA was widely imitated, and its descendants, large and small, are still used for light assembly in electronics and other industries. Since the s small electric arms have become important in molecular biology laboratories, precisely handling test-tube arrays and pipetting intricate sequences of reagents.
University College Cork, Ireland Mobile industrial robots also first appeared in In that year a driverless electric cart, made by Barrett Electronics Corporation, began pulling loads around a South Carolina grocery warehouse. Such machines, dubbed AGVs Automatic Guided Vehicles , commonly navigate by following signal-emitting wires entrenched in concrete floors.
In the s AGVs acquired microprocessor controllers that allowed more complex behaviours than those afforded by simple electronic controls. In the s a new navigation method became popular for use in warehouses: AGVs equipped with a scanning laser triangulate their position by measuring reflections from fixed retro-reflectors at least three of which must be visible from any location. Although industrial robots first appeared in the United States, the business did not thrive there.
Unimation was acquired by Westinghouse Electric Corporation in and shut down a few years later. Foreign licensees of Unimation, notably in Japan and Sweden, continue to operate, and in the s other companies in Japan and Europe began to vigorously enter the field.
The prospect of an aging population and consequent worker shortage induced Japanese manufacturers to experiment with advanced automation even before it gave a clear return, opening a market for robot makers.
By the late s Japan—led by the robotics divisions of Fanuc Ltd. High labour costs in Europe similarly encouraged the adoption of robot substitutes, with industrial robot installations in the European Union exceeding Japanese installations for the first time in Robot toys Lack of reliable functionality has limited the market for industrial and service robots built to work in office and home environments.
Toy robots, on the other hand, can entertain without performing tasks very reliably, and mechanical varieties have existed for thousands of years.
In the s microprocessor-controlled toys appeared that could speak or move in response to sounds or light. More advanced ones in the s recognized voices and words. In the Sony Corporation introduced a doglike robot named AIBO , with two dozen motors to activate its legs, head, and tail, two microphones, and a colour camera all coordinated by a powerful microprocessor.
More lifelike than anything before, AIBOs chased coloured balls and learned to recognize their owners and to explore and adapt. Courtesy of Sony Electronics Inc. Robotics research Dexterous industrial manipulators and industrial vision have roots in advanced robotics work conducted in artificial intelligence AI laboratories since the late s.
Yet, even more than with AI itself, these accomplishments fall far short of the motivating vision of machines with broad human abilities. Techniques for recognizing and manipulating objects, reliably navigating spaces, and planning actions have worked in some narrow, constrained contexts , but they have failed in more general circumstances. Further geometric formulas related object positions to the necessary joint angles needed to allow a robot arm to grasp them, or the steering and drive motions to get a mobile robot around or to the object.
This approach was tedious to program and frequently failed when unplanned image complexities misled the first steps. An attempt in the late s to overcome these limitations by adding an expert system component for visual analysis mainly made the programs more unwieldy—substituting complex new confusions for simpler failures. Courtesy of Northwestern University In the mids Rodney Brooks of the MIT AI lab used this impasse to launch a highly visible new movement that rejected the effort to have machines create internal models of their surroundings.
Instead, Brooks and his followers wrote computer programs with simple subprograms that connected sensor inputs to motor outputs, each subprogram encoding a behaviour such as avoiding a sensed obstacle or heading toward a detected goal. There is evidence that many insects function largely this way, as do parts of larger nervous systems. The approach resulted in some very engaging insectlike robots, but—as with real insects—their behaviour was erratic, as their sensors were momentarily misled, and the approach proved unsuitable for larger robots.
To see a larger image and obtain information on each robot, click on the individual photograph. One prominent example involves semiautonomous mobile robots for exploration of the Martian surface.
With its arm attached, Pebbles can collect samples or handle dangerous objects. In an international community of researchers organized a long-term program to develop robots capable of playing this sport, with progress tested in annual machine tournaments.
The first RoboCup games were held in in Nagoya, Japan, with teams entered in three competition categories: Merely finding and pushing the ball was a major accomplishment, but the event encouraged participants to share research, and play improved dramatically in subsequent years.
In Sony began providing researchers with programmable AIBOs for a new competition category; this gave teams a standard reliable prebuilt hardware platform for software experimentation. While robot football has helped to coordinate and focus research in some specialized skills, research involving broader abilities is fragmented.
Faster microprocessors developed in the s have enabled new, broadly effective techniques. For example, by statistically weighing large quantities of sensor measurements, computers can mitigate individually confusing readings caused by reflections, blockages, bad illumination, or other complications.
Connectionist neural networks containing thousands of adjustable-strength connections are the most famous learners, but smaller, more-specialized frameworks usually learn faster and better. Another kind of learning remembers a large number of input instances and their correct responses and interpolates between them to deal with new inputs.
Such techniques are already in broad use for computer software that converts speech into text. With anticipated increases in computing power, by second-generation robots with trainable mouselike minds may become possible.
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Such robots would learn from mental rehearsals in simulations that would model physical, cultural, and psychological factors. Physical properties would include shape, weight, strength, texture, and appearance of things and knowledge of how to handle them. Psychological factors, applied to humans and other robots, would include goals, beliefs, feelings, and preferences.
The simulation would track external events and would tune its models to keep them faithful to reality. This should let a robot learn by imitation and afford it a kind of consciousness. By the middle of the 21st century, fourth-generation robots may exist with humanlike mental power able to abstract and generalize.
Researchers hope that such machines will result from melding powerful reasoning programs to third-generation machines. Properly educated, fourth-generation robots are likely to become intellectually formidable.