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Return on robotics and Servo Mechanism

This definition implies that a device can only be called a "robot" if it contains mechanism of furniture, influenced by detection, and action planning and control components. This does not imply that a minimum number of these components must be implemented in software, or be modified by the consumer "using the device, for example, the behavior of movement may have been hard-wired the device by the manufacturer.

Therefore, the definition presented as well as the rest of the material in this part of the book covers not only "Pure" robotics or just "smart robots, but rather something broader domain of robotics and automation. This includes "Dumb" robots such as: metal and woodworking machines, "intelligent" washing machines, dishwashers and pool cleaning robots, etc. All These examples are detection, planning and control, but often not in the separate components individually. For example, detection and planning of behavior pool cleaning robot have been integrated into the mechanical design of the device, the intelligence of human development.

The robot is in a very large extent, all about systems integration, achieving a mission by a powered mechanical device, through an "intelligent" the integration of components, many of which are shared with other domains, such as systems and control, computer character animation, machine design, artificial vision, artificial intelligence, cognitive science, biomechanics, etc. Furthermore, the limits of robotics can not be clearly defined, and also its "core" ideas, concepts and algorithms are being applied in an increasing number of "external" applications, and vice versa, technology the basis of other domains (vision, biology, cognitive science or biomechanics, for example) are becoming increasingly vital components in modern robotic systems.

This part of the WEBook makes an effort to define exactly what is referred to the base material of the domain of robotics, and describe in a coherent and motivated. However, this structure chosen is only one of many possible "views" you may want to have in the domain of robotics.

Similarly, the above "definition" of robotics is not intended to be definitive or final, and only used as a rough framework for structuring the different chapters

Components of robotic systems

This figure shows the components that are part of all robotic systems. The purpose of this section is to describe the semantics of the terminology used to classify the chapters WEBook "sensing", "plan", "models", "control", etc.

The real robot is a mechanical device (mechanism) that moves in the environment and in doing so, physically interacts with this environment. This interaction involves the exchange of physical energy in one form or another. Both the mechanism of the robot and the environment can be the "cause" of physical interaction through of "Action" or the experience of "effect" of the interaction, which can be measured through "screening."

Robotics as an integrated control system of interaction with the physical world.

Detection and physical activity are the ports through which the Controller "of the robot determines the mechanical interaction of your body with the physical world. As mentioned before, the driver can, at one end consists of the program, but in the end everything else can also be implemented in hardware.

Inside the Controller component, several sub-activities often identified:

Modeling. The input-output relationships of all control components may (but need not) be derived from information that is stored in a model. This model can take many forms: analytical formulas, empirical look-up tables, fuzzy rules, neural networks, etc.

The name "model" often leads to heated discussions between different research "schools" and the WEBook not interested in adopting a position in this debate: the WEBook, "model" is to be understood with minimal semantics: "any information that is used to determine or influence in the input-output relationships of the components in the controller. "

The other components discussed below all models can be inside. A model of "system" can be used to tie multiple components together, but it is clear that not all robots use a system model. The model "Perception" and "role model" containing the information that transform raw data into information physics tasks dependent on the controller, and vice versa.

Planning. This is the activity that predicts the outcome of possible actions and select the "best" one. Almost by definition, planning can only be based on some kind of model.

Regulation. This component processes the results detection and planning components, to generate a reference point for action. Again, this activity regulation could or could not based on some kind of (system) model.

The term "control" is often used instead of "regulation" but it is impossible to determine clearly the domains using one term or another. The sense used in the WEBook is clear from context.

Scales in systems robotic

The aforementioned 'components' description of a robotic system is complemented by a "scale" description, ie, the scales of these systems have a great influence on the specific content of planning, testing, modeling and control of components on a scale particular, and therefore also in the relevant sections of the WEBook.

Mechanical scale. The physical volume of the robot determines to a large extent limits of what can be done with it. Overall, a large-scale robot (like a container crane or an autonomous space shuttle) has different capabilities and problems of macro control of a robot (for example, an industrial robot arm), a desktop robot (like "sumo" robots popular among fans), or military micro or nano robots.

Spatial scale. There are big differences between robots operating in 1D, 2D, 3D, or 6D (three positions and three orientations).

Timeline. There are great differences between robots that must react within hours, seconds, milliseconds or microseconds.

Density scale power. A robot must be operated in order to advance, actuators, but it needs more space and energy, so the relationship between the two determines some capabilities the robot.

System complexity scale. The increasing complexity of a robot system with the number of interactions between independent subsystems, and components should be adapted to this complexity.

Computational complexity scale. robot controller is inevitably running on hardware Computing in the real world, so are limited by the number of available estimates, the available communication bandwidth and memory storage available.

Obviously, these parameters are never applied independently scale the same system. For example, a system that must react microsecond time scale can not mechanical macro scale or involve a large number of interactions with communication subsystems.

Background sensitivity

Finally, no description even scientific material is ever fully objective or independent context, in the sense that it is very difficult for taxpayers to WEBook to "forget" their history during the writing of their contribution. In this regard, robotics has roughly twofold: (I) mathematics and engineering side, it's fairly "standardized" in the sense that there is broad consensus about the tools and theories for use ("systems theory"), and (ii) the face of AI, which is rather low standard, not by a lack of interest and research efforts, but due to the inherent complexity of behavior "smart." The terminology and the thinking of both systems origins are very different, hence the WEBook accommodate sections of the same material but written from different perspectives. This is not a "bug" but a "feature": have different opinions in the context of WEBook it can only lead to better understanding and mutual respect.

Robotics research engineering follows the bottom-up approach: systems and extend existing work and became more versatile. Research in artificial intelligence, robotics is from top to bottom: on the assumption that a set of low-level primitive is available, how they apply in order to increase the "intelligence" of a system. The border between the two approaches change continually as more and more "intelligence" is cast into algorithmic form, the theoretical system. For example, the response of a robot sensor input was considered "intelligent behavior" in the seventies and even eighties. Therefore belonged bird flu later showed that many of the sensor-based tasks such as monitoring the surface or visual tracking could be formulated as problems algorithmic control solutions. Since then, they did not belong to the IA more.

Robotics Technology

Most industrial robots have at least the following five parts:

Sensors, effectors, actuators, controllers and effectors commonly known as weapons.

Many other robots also have Artificial Intelligence and effectors that help you achieve mobility.

This section describes the basic technologies of a robot. Click one of the links above or use the menu on the navigation bar on the right.

Robotics Technology - Sensors

Most the robots of today are nearly deaf and blind. Sensors can provide some limited information to the robot so it can do its job. Compared to the senses and abilities of even the simplest things of life, the robots have a long way to go.

The sensor sends information in the form of signals e back to the cfontroller. sensors also control the robot information about its environment and let him know the exact position of the arm, or the state of the world around.

Sight, hearing, touch, taste and smell are the types of information we get from our world. The robots can be designed and programmed to get specific information that is beyond what our five senses can tell us. For example, a robot sensor can "see" in the dark detect small amounts of radiation as invisible or movement is too small or fast for the human eye can see.

These sensors are some of the things are used for:

Physical Property

Technology

BUMP contact switch

Distance ultrasound, radar, infrared

Light level cell photos, cameras

Sound level microphones

Strain gauges Strain

The rotation encoder

Compasses Magnetism

Chemical odor

Temperature Thermal infrared

Slope inclinometers, gyroscope

Pressure Gauges

Altimeters Altitude

The sensors can be simple and complex, depending on the amount of information needs to be stored. A switch is a simple on / off sensor used to rotate the robot on and off. A human retina is a complex sensor that uses more than one hundred million light-sensitive elements (rods and cones). sensors provide information the brain robots, which can be treated in several ways. For example, we simply react to the output of the sensor: if the switch is open, if the switch is closed leaves.

Levels of Processing

To find out if the switch is open or closed, will have to measure the voltage going through the circuit, which is electronics. Now let's say you have a microphone and would like to recognize a separate voice and noise, that is the signal processing. Now you have a camera, and you want to take pre-processed image and now need to find out what those objects are, perhaps, comparing with a large library of drawings, which is the computer. sensory data processing is a very complex thing to try to do, but the robot needs this to have a "brain." The brain has to be capable of processing analogue and digital cables to connect all support electronics to go with the team, and batteries to power the whole affair in order to process the sensory data. Perception requires that the robot has sensors (energy and electronics), computers (more power and electronics, and connectors (for connecting to all).

Switch Sensors

The switches are the simplest of all sensors. They work without processing, electronics (circuit) level. Their general underlying principle is that of a closed vs. open circuit. If a switch is open, no current can flow, if it is closed, current can flow and be detected. This simple principle can (and is) used in a wide variety of ways.

Change sensors can be used in a variety of ways:

contact sensors: detect when the sensor is contact with another object (for example, is activated when a robot hits a wall or take an object, which can even be whiskers)

Limit sensors: detect when a mechanism has been moved to the end of its range

shaft encoder sensors: detect how often a shaft rotates to have a click switch (Open / close) every time the shaft rotates (eg, triggers for each shift, thus providing rotations)

There are many common switches: Button switches, switches, mouse, key board keys, phone keys, and others. Depending on how a switch is connected, can be normally open or normally closed. Of course, this depends on your bot electronics, mechanics, and their task. The simple but extremely useful sensor for a robot obstacle is a switch that says when it is beaten into something, so you can back up and back. Even for a simple idea, there are many different ways of application.

Light Sensors

Switches as touch and light sensors measure the amount of light affecting the cell, which is basically a resistive sensor. The resistance of a photocell is low when lit, that is, when is very light, is high when it is night. In this regard, a light sensor is actually a "dark" of the sensor. In the creation of a photocell sensor, the result will end up using the equations we have learned before, because you will need to address the relationship of the photocell picture of resistance, and resistance and tension in its electronic sensor circuit. Of course, it will be the construction of electronics and write the program to measure and use the output light sensor, you can always be manipulated to make it easier and more intuitive. What surrounds a light sensor affects its properties. The sensor can be protected and the position in several ways. Multiple sensors can be arranged in configurations useful and isolated from each other with shields.

Just as switches, light sensors can be used in many different ways:

light sensors can measure:

intensity of light (as light / Dark s)

differential current (difference between photocells)

broken road (change / drop in intensity)

light sensors can be protected and focused in different ways

Its position and directionality a robot can make a big difference and impact

Polarized light

"Normal" which light emanating from a source non-polar, which means traveling to all orientations with respect to horizon. However, if there is a polarizing filter in front of a light source, only light waves of a certain orientation of the filter will pass through. This is useful because now we can manipulate the light remaining in other filters, and if we put another filter with the same feature level, almost everything you get through. But, if we use a filter perpendicular (one with a 90 degree angle feature relative) will block all light. Polarized light can be used to make specialized photoelectric sensors simple, if you put a filter in front of a light source and the same or a different filter in front of the cell, which can be manipulated skillfully what and how much light to be detected.

Sensors resistive position

We said earlier that a photocell is a resistive device. You may also feel the resistance in response to other physical properties such as bending. The resistance of the device increases with the amount doubles. These sensors were originally developed Dual Notice to control video games (eg Nintendo Powerglove), and in general are quite useful. Repeated bending sensor is spent. Not surprisingly, a bend sensor is much less robust than light sensors, despite using the same underlying principle of resistance.

Potentiometers

These devices are very common for manual tuning, you've probably seen some controls (such as volume and tone of equipment sound). pots usually called, allow the user to manually adjust the resistance. The general idea is that the device consists of a movable tap along two fixed ends. As the tap moves, the resistance changes. As you can imagine, the resistance between the two ends is fixed, but the resistance between the phone and either end varies as the part moves. In robotics, the pots are commonly used to sense and position tuning for sliding and rotating mechanisms.

Biological Analogues

All sensors that are described in biological systems

Tap / contact sensors with much greater precision and complexity in all species

Bend / receptors in muscle strength

Reflective optical sensors

We have mentioned that if we use a light bulb in combination with a photocell, we can make a road sensor break. This idea is the underlying principle of reflection optical sensors: the sensor consists of a transmitter and a detector. Depending on the available of the two for the other, we can obtain two types of sensors:

reflectance sensors (the emitter and detector are next to each other, separated by a barrier, the objects are detected when light is reflected off them and back into the detector)

road break sensors (the emitter and detector in front of other objects are detected if it interrupts the beam between the emitter and detector)

The transmitter is usually made out of a light emitting diode (LED) and is usually a photodiode detector / phototransistor.

Note that these are not the same technology as resistive photocells. photocells resistive are nice and simple, but its strength properties are slow photodiodes and photo transistors are much faster and therefore the preferred type of technology.

What can you do with this simple idea of the reflectivity of light? A whole bunch of useful things:

object presence detection

detection of distant objects

surface feature detection (finding / Following markers / tape)

wall / ceiling tracking

encoding the axis of rotation (with wheels encoder crested black and white or color)

barcode decoding

Note, however, that the reflectivity of light depends on the color (and other properties) of a surface. A light surface reflects light better than dark, and a black surface does not reflect at all with what appears invisible to a light sensor. Therefore, it can be harder (less reliable) to detect objects thus darker than lighter. In If the object distance, lighter objects that are further away appear closer than darker objects that are not that far. This gives an idea how the physical world is partially observable. Although the sensors are useful, we have no complete and accurate information in full.

Another source of noise in light sensors is ambient light. The best thing to do is subtract the ambient light sensor reading in order to detect real change in reflected light, not the ambient light. How does that happen? By taking two (or more, to be exact) readings of the detectors, one with the transmitter on, and with it off, and subtracting the two values of each other. The result is the level of ambient light, which can be subtracted from future readings. This process is called sensor calibration. Of course, remember that ambient light levels can change, so the sensors may need to be calibrated on several occasions.

Road Break Sensors

We talked about the idea of breaking road sensors. In general, any pair of devices compatible with emitter-detector can be used to produce such sensors:

an incandescent lamp and a photocell

Red LEDs and transistors visible photo-sensitive the light-

or infrared emitters and IR detectors

Axis coding

encoder to measure shaft rotation angle of a shaft position mediation and / or speed information. For example, a speedometer measures how fast vehicle wheels are turning, while an odometer measures the number of rotations of the wheels.

In order to detect a rotation complete or partial, we must somehow make the decisive element. This is usually done by setting a round disc on the shaft, and cut notches in it. A light emitter and detector are placed each side of the disc, so that the notch as it passes between them, light passes, and is found where there is no notch on the disk, the light does not pass.

If there is just a notch on the disk, and then a rotation is detected as is the case. This is not a very good idea, as it only allows a low level of resolution for the measurement Speed: the smallest unit that can be measured is a complete rotation. In addition, some rotations might be overlooked due to noise.

In general, many notches are cut on the disc, and the light strikes the impact detector counted. (You can see that it is important to have a speed sensor here, if the shaft rotates very quickly.)

An alternative to cutting notches in the hard disk is painted black (to absorb, reflect-) and black (highly reflective) spots, and measure the reflectance. In this case, the emitter and detector are on the same side of the disc.

In any case, the output of sensor will be a wave function of light intensity. This can be a process for producing the speed, counting the peaks of the waves.

Note that the encoding axis measures both the position and speed of rotation, by subtracting the difference in readings of position after each time interval. Speed, however, tells us how fast it is moving a robot, or if it moves at all. There are several ways of using this measure:

measure the speed of a motor (active) wheels

person using a wheel that is dragged by the robot (measuring progress forward)

We may combine the position and velocity information to make things more sophisticated:

move in a straight line

rotate by an exact amount

Note, however, to do such things is very difficult, because the wheels tend to slip (noise effector and error) and slide and there is usually a reaction in the slop and gear mechanism. Shaft encoders can provide information to correct errors, but some error is inevitable.

Quadrature Shaft Encoding

To date, we talked about Detection of position and velocity, but not to speak of direction of rotation. Assume the wheel suddenly changes the direction of rotation, it would be useful for the robot detect it.

An example of a common system that needs to measure the position, speed and direction is a computer mouse. Without a measure direction, a mouse is pretty useless. How to measure the direction of rotation?

quadrature shaft encoding is a development of the basic idea of balance beam, instead of using a single sensor, it takes two. The encoders are aligned so that its two data flows from detector and fourth cycle (90 degrees) out of phase, hence the name "square." When comparing the results of the two encoders at each time step with output of the previous time step, we can say if there is a change of address. When both are included in the sample at each time step, only one of them will change their state (Ie, switching from on to off) at a time, because they are out of phase. What is the address that determines the axis of rotation. Every time a tree is moving in one direction, a counter is incremented, and when it becomes the opposite direction, the counter is decremented, so do not lose sight of the overall situation.

Other uses quadrature encoding axis robot arms with complex joints (such as swivel joints /, Thinks about his knee or shoulder), Cartesian robots (and printers large) where an arm / rack moves back and forth along a shaft / gear.

Modulation and Demodulation of Light

We mentioned that the ambient light is a problem because it interferes with the light emitted by a light sensor. One way around this problem is the emission of light modulated, ie, to quickly turn the transmitter on and off. This signal is much easier and more reliably detected by a demodulator, which is tuned to the particular frequency modulated light. It is not surprising that a detector needs to detect various flashes in a row in order to detect a signal, ie, detecting frequency. This is a small point, but is important in writing code demodulator.

The idea of modulated IR light is commonly used for example in the home of the remotes.

Modulated light sensors are often more reliable than sensors light base. They can be used for the same purpose: to detect the presence of an object to measure the distance to a nearby object (requires intelligent electronics, see the course notes)

Infra Red (IR) sensors

Infrared sensors are a type of light sensors, which operate in the infrared sensors of the frequency spectrum. Infrared sensors are assets consist of: consisting of a transmitter and a receiver. Infrared sensors are used in the same way that visible light sensors is that we have discussed so far: as broken beams and reflectance as sensors. IR is preferable to visible light in robotics (and other) applications, and gets a little less interference environment because it can be easily modulated, and simply because it is not visible.

Communication IR

infrared modules can be used as a serial port for transmitting messages. This is a fact IR is how modems work. There are two basic methods:

bit frames (sampled at the center of each bit, assuming all the bits to make the same amount of time to transmit)

short intervals (more common in commercial use, sampled on the trailing edge, the length of the interval between sampling determines whether a 0 or 1)

Ultrasonic distance detection

As mentioned before ultrasound screening is based on the principle of flight times. The emitter produces a sonar "chirp" sound, away from the source, and if encounters obstacles, is reflected in them and returns to the receiver (microphone). The amount of time it takes the sound beam is tracked back (when you start a timer when the "chirp" is produced, and stop when the reflected sound returns), and is used to calculate the distance the sound travels. This is possible (and fairly easy) because we know how fast sound travels, which is a constant, which varies slightly depending on ambient temperature.

In temperature environment, sound travels to 1.12 meters per millisecond. Another way to say that sound travels in 0.89 milliseconds per foot. This is useful to recall a constant.

The process of finding a place based on the sonar is called echolocation. The inspiration for the ultrasound screening comes from the nature bats use ultrasound instead of vision (this makes sense, living in very dark caves where vision would be largely useless). Bat sonar is extremely unsophisticated in comparison with artificial sonar, which involve numerous different frequencies, used to find the smallest prey rapid flight, and to avoid hundreds of other bats, and communication to find a mate.

Specular Reflection

A major disadvantage of ultrasound screening is its susceptibility to specular reflection (specular reflection refers to the outer surface of the object). While the sonar detection principle is based on the sound wave reflecting surface and return to the receiver, it is important to remember that not necessarily the sound wave bouncing on the surface and "come back." In fact, the direction of reflection depends on the angle of incidence of sound beam and the surface. The smaller the angle, not the higher the probability that the sound is limited to "shepherd" to the surface and bounce back, thus returning to the issuer, in turn generate a false long / distant reading. This is called specular reflection, as smooth surfaces with specular properties, tend to aggravate this problem reflection. rough surfaces produce more irregular reflections, some of which are more likely to return to sender. (For example, in our robotics lab on campus, using sonar sensors, and have lined up a part of the test area with cardboard, because it has much better sound reflective properties of very soft to the wall behind him.)

In summary, the long reads may sound very inaccurate, because they can be false rather than accurate reflections. This should be taken into account when scheduling robots, or robot can produce very undesirable and dangerous behavior. For example, a robot approaches a wall at a steep angle can not see the wall at all, and hit him!

However, the sonar sensors have been successfully used in highly sophisticated robotics applications, including mapping the terrain and cover, and remain a popular choice in mobile robotic sensor.

The first commercial ultrasonic sensor produced by Polaroid, and is used to automatically measure the distance to the nearest object (presumably that is being photographed). These simple Polaroid sensors still the most popular sonar off-the-shelf (which come with a processor board that deals with analog electronics). Standard properties include:

rank 32 feet

30-degree beamwidth

sensitivity to specular

shorter return distance

Polaroid sensors can be combined into phased arrays to create more sophisticated and more accurate sensors.

One can find ultrasound used in a variety of other applications, the best known is that range from submarines. The sonars are much more focused and has longer beams scope. Simpler and more mundane applications involve automated "tape-measures, measures of height, burglar alarm, etc.

Vision Artificial

So far, we've talked about relatively simple sensors. They were simple in terms of information processing that return. We now turn to machine vision, ie the cameras as sensors.

Cameras, of course, the model biological eyes. Needless to say, all eyes biologics are more complex than any camera we know today, but as you can see, the cameras and machine vision systems that process sensory information, is not simple at all! In fact, the vision is a difficult issue that has historically been a separate branch of the Artificial Intelligence.

The general principle of a camera is to light scattered by objects in the environment (which are called scene) passes through an opening ("Iris" in the simplest case a hole, for a more sophisticated lens), and affect what is called the level of image. In biological systems, the image plane is the retina, which is attached to the rods and many cones (photosensitive elements) which, in turn, bind to the nerves conducting the "first look", and then passing the information through the brain to do "higher level" vision transformation. As we mentioned earlier, a very high percentage of human (and animal) of the brain devoted to visual processing, so this is a task very complex.

In the chambers, instead of photosensitive rhodopsin and the rods and cones, we use silver halide photographic film, or circuits silicon charge-coupled device (CCD) cameras. In all cases, some information about the incoming light (eg intensity, color) is detected by these photosensitive elements in the image plane.

In vision, the computer must make sense out of the information place the image plane. If the camera is very simple, and uses a small hole, then it requires some calculation to calculate the projection of objects in the environment in the image plane (note will be reversed). If a target is involved (as in the eyes of vertebrates and real cameras), then you can get more light in, but at the price of being focused, objects only a special range of distances from the lens is in focus. This range of distances is called chamber depth field.

The image plane is usually divided into equal parts, called pixels, typically arranged in a rectangular grid. In a typical camera is 512 by 512 pixels in the image plane (for comparison, there are 120 x 10 ^ 6 rods and 6 x 10 ^ 6 cones in the eye, willing hexagonal). Let's call projection onto the image plane of the image.

The brightness of each pixel of the image is proportional to the amount of light directed towards the camera the revision of the object's surface that projects of that pixel. (This of course depends on the reflectance properties of the surface of the patch, the position and distribution of light sources in the environment, and the amount of light reflected by other objects in the scene where the patch surface.) As a result, the brightness of a patch depends two types of reflections, one being mirror (not the surface, as we saw before), and the other is diffuse (light entering the object, is absorbed and then re-issued). To properly light reflection model and reconstruct the scene, all these properties are necessary.

Suppose it is a black and white camera with 512 x 512 pixel image plane. Now we have an image, which is a collection of pixels, each intensity between white and black. To find an object in the image (if there is one, which of course we do not know a priori), the typical first step ("first window") is to detect of edges, ie, find all the edges. How do you recognize? We define the edges as curves in the plane of the image through which there is a significant change in brightness.

A simple approach would be to seek changes to differentiate strong brightness of the image and look for areas where the magnitude of the derivative is large. This almost works, but unfortunately there is all sorts of spurious peaks, ie the noise. Also, do not inherently able to distinguish the changes in intensity due to shadows from those due to physical objects. But let's leave that for now and think about the noise. What about noise?

We relaxed, ie, applies a mathematical procedure called convolution, which finds and removes isolated peaks. Convolution, in effect, applies a filter to the image. In fact, to find arbitrary edges in the image, we must convolve the image with many filters with different orientations. Fortunately, relatively complicated mathematics involved in edge detection has been well studied, and for now, preferring non-standard approaches to edge detection.

Once we have the edges the next thing to do is try to find objects from all sides. Segmentation is the process of division or organization of the image into parts that correspond continuous object. But how do we know that the lines correspond to objects, and what makes an object? There are several signs that can be used to detect objects:

We have stored models of line drawings of objects (from many angles, and in many different scales possible!), Then compare these with all possible combinations of the edges of the image. Note that this is a very intensive and computationally expensive. This general approach, which has been studied broadly-based model is called vision.

We can take advantage of movement. If you look at a picture in two consecutive time steps, and move the camera in the middle, each continuous solid objects (that obeys physical laws) will move as one, ie brightness properties will be retained. This will hives a hint to find objects by subtracting two images together. But note that this also depends on knowing how the camera moved about the scene (direction, distance), and that nothing moved on the scene at that time. This general approach, which has also been extensively studied, is called vision of movement.

We can use stereo (ie, binocular stereopsis, two eyes / cameras / views). As with the vision of the movement, but without having to actually move, we get two images, we can subtract one another if we know what the differences between them should be, that is, if we know how the two cameras are arranged / positioned relative to each other.

We can use the texture. The patches are consistent with uniform texture, and have almost identical brightness, so we can assume that from the same object. When removing it can get a hint about which parts may belong to the same object on the scene.

We may also use shading and contours in a similar manner. And there are many other methods, with the participation and how projective invariant object, etc.

Note that all the above strategies are used in biological vision. It is difficult to recognize unexpected objects or completely new (they do not have models at all or not in the list.) The movement helps to get our attention. Stereo, ie two eyes, is critical, and all carnivores use it (have two eyes pointing in the same direction, unlike the herbivores). The brain does an excellent job rapid extraction of the information we need for the scene.

Machine vision has the same task of making real-time vision. But this is, as we have seen, a very difficult task. Often, an alternative to trying to do all the steps above to make object recognition, is possible to simplify the vision problem in several ways:

Use color, find specific items and unique colors, and recognize that way (for example as stop signs, for example)

Use a small image plane, rather than a total of 512 x 512 pixel array, we can reduce our view at least, for example just one line (called a linear CCD). Of course, there is much less of the image, but if we intelligent, and know what to expect, what we see we can process quickly and useful.

Using other sensors, easier and faster, and combine those with vision. For example, infrared cameras isolate people by body temperature. Tweezers allows us to touch and move objects, after which we can be sure they exist.

Use the information on the environment, and if you know you will be driving on the highway, which has lines white and look specifically for these lines in the right places in the image. This is how the first and still the fastest road and highway driving robot is done.

These and many other intelligent techniques must be employed when considering how important it is to "see" in real time. Consider the road the driver, as an important and growing application of robotics and AI. Everything moves so fast that the system must perceive and act in time to react and safe protective and intelligent.

Now that you know how complex it is the vision, You can see why it was not used in the first robots, and still not used to all applications, and does not hesitate simple robots. A robot can be extremely helpful without vision, but some of the tasks required. As always, it is essential to devise a proper match between the sensors of the robot and the task.

About the Author

Assistant professor in lord venkateswara engineering college.I am doing phd in sathyabama university, Tamil Nadu,India.