peak height velocity definition

May 15
2009

Return on Robotics and Servo Mechanism

This definition implies that a product can only be called a â € € œrobotâ if it contains a movable mechanism, influenced by the detection, planning and command and control components. This does not imply that a minimum number of these components must be implemented in software, or be modified by the â € € œconsumerâ using the device, for example, the behavior of motion can be printed on the device by the manufacturer.

Thus the definition presented as well as the rest of the material in this part of the Reserve, encompasses not only â € or â € œpureâ robotics € € œintelligentâ only robots, but rather something broader domain of robotics and automation. This includes € â € œdumbâ robots such as metal and woodworking machines, € â € œintelligentâ washers, dishwasher and pool cleaning robots, etc. All these examples have sensing, planning and control, but often not in the components individually and separately. For example, detection and behavior planning pool cleaning robot have been integrated into the mechanical design of the device, by the intelligence of the human programmer.

Robotics is largely all about systems integration, achievement of a task by a mechanical device operated at through a â € € œintelligentâ the integration of components, many of which it shares with other domains, such as systems and control, computing, character animation, machine design, computer vision, artificial intelligence, cognitive science, biomechanics, etc. Furthermore, the limits robotics can not be clearly defined, as also its € â € œcoreâ ideas, concepts and algorithms are being implemented in a growing number increasing applications œexternalâ € â €, and, conversely, the core technology in other domains (vision, biology, science cognitive or biomechanics, for example) are becoming vital components in robotic systems more and more modern.

This part of the WEBook makes an effort to define what exactly the material above the base of the domain of robotics, and describing in a coherent and reasoned. No However, this preferred structure is just one of many possible â € € œviewsâ you may want to have in the field of robotics.

Similarly, the aforementioned â € € œdefinitionâ of robotics is not intended to be definitive or final, and only used as a general framework for the structure of 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 to chapters of WEBook: œsensingâ € â €, â € œplanningâ €, â € œmodelingâ €, â € œcontrolâ €, etc.

The real robot is a mechanical device (œmechanismâ € â €) that moves in the middle 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 robot mechanism and the environment may be the â € € œcauseâ of physical interaction through a œActuationâ € €, or the experience of â € € œeffectâ interaction as measured through a œSensingâ € €.

Robotics as an integrated system for monitoring the interaction with the physical world.

Detection and performance all through the physical ports of which € â € œControllerâ the robot determines its mechanical body interaction with the physical world. As mentioned before, the controller may, at one end, consisting of software, but across the other side can also be implemented in hardware.

Within the component controller several sub-activities are often identified:

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

The name â € € œmodelâ often leads to heated discussions between different research â € € œschoolsâ, and WEBook not interested in taking a position on this debate within the WEBook, € â € œmodelâ means with their semantics Minimum â € œany information used to determine or influence the input-output relationships of the components on the € Controller.â

The other components discussed below can have all the models inside. A € â € œSystem models can be used to bind several components together, but it is clear that not all robots use a system model. The â € and â € œSensing models œActuation € € models contain information with which to transform raw physical data information tasks by the controller, and vice versa.

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

Regulation. This component processes the results of the screening and planning components to generate a reference point for action. Again, this regulatory activity may or may not rely on some kind of (system) model.

The term â € œcontrolâ € is often used instead of œregulationâ € â €, but it is impossible clearly identify the domains that use either term. The sense used in the WEBook is clear from the context.

Scales in robotic systems

The above description œcomponentsâ € â € a robotic system is complemented by a description œscaleâ € â €, ie, the scales of these systems have a large influence on the specific content of the planning, detection, modeling and control components to a particular scale, and therefore also in the relevant sections of the WEBook.

The scale mechanics. The physical volume of the robot largely determines the limits of what can be done with it. Overall, large-scale robot (like a Self container crane or a space shuttle) has different capabilities and problems of robot control macro (such as an industrial robot arm), a robot desktop (such as â € € œsumoâ robots popular among fans), or milli micro or nano robots.

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

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

Scale of energy density. A robot should be operated to advance, but actuators need space and energy, so their relationship determines some of the capabilities of the robot.

Scale system complexity. The complexity of a robot system increases with the number of interactions between independent subsystems, and components control must adapt to this complexity.

Computational complexity scale. The robot controllers are inevitably running on real hardware computer world, so are limited by the available number of calculations, the communication bandwidth available and the memory storage available.

Obviously, these parameters are not implemented at the same system fully independent. For example, a system that must react in microsecond time scale can not mechanical macro scale or involve a large number of communication interactions of the subsystems.

Background sensitivity

Finally, no description even of scientific material is always completely objective or independent the context, that is very difficult for taxpayers to € â € WEBook to œforgetâ background when writing your contribution. In this regard the robotics has broadly twofold: (i) mathematics and engineering side, it's pretty â € œstandardizedâ € in the sense that there is broad consensus about the tools and theories for use (€ â € œsystems theory), and (ii) the face of AI, which is rather standard, not because of lack of interest or research efforts, but due to the inherent complexity of â € œintelligent behaviour.â € The terminology and thought systems of the two funds are significantly different, hence the WEBook take into account the sections on the same material but Written from various perspectives. This is not a â € € œbugâ, but a â € € œfeatureâ: to have different opinions in the context WEBook of it can only lead to better understanding and mutual respect.

Robotics Research follows the bottom-up approach: existing and working systems are extended and more versatile. Robotics research in artificial intelligence is top to bottom: in the event that a primitive set of low level is available, how it could be applied in order to increase the € â € œintelligenceâ of a system. The boundary changes continuously between the two approaches, as more and more â € € œintelligenceâ thrown into algorithms, the system theoretically. For example, the response of a robot sensor input was considered â € € behavior œintelligent in the seventies and even eighties. Therefore, belonged to avian flu later shown that the sensor of many basic tasks such as monitoring the surface or visual tracking could be formulated as problems of control algorithmic solutions. Thereafter, no more belong to the IA.

Robotic 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 the effectors that help achieve mobility.

This section discusses the core technologies of a robot. Click click one of the links above or use the navigation menu bar at the far right.

Robotics Technology – Sensors

Most robots today are sensors blind.Â almost deaf and can provide some limited information to the robot so it can perform its job.Â compared with the senses and abilities of even the simplest living things, robots have a long way to go.

The sensor sends information in the form of electronic signals back to cfontroller.Â sensors also control the robot information about its environment and lets them know the position accurate arm or the state of the world around him.

Sight, hearing, touch, taste and smell is the kind of information we receive from our world. The robots can be designed and programmed for specific information that is beyond what our 5 senses can tell us. For example, a sensor robot can "see" in the dark, detect small amounts of invisible radiation or movement as being too small or fast for the human eye to see.

Here are some of the sensors are used to things:

Physical Property

Â Technology

Contact Bump Switch

Distance from ultrasound, radar, infrared

Photo Light Level cells, chambers of

Microphone Sound Level

Strain Gauges

Rotate encoder

Magnetism Compasses

Chemical odor

Temperature Thermal Infrared

Slope inclinometers, gyroscope

Manometers

Altimeters Altitude

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

Levels of processing

Â Â Â To find out if the switch is open or closed, will have to measure the voltage passing through the circuit, which is electronics.Â Now suppose you have a microphone and a voice you want to recognize and separate the noise signal that is processing.Â Now you have a camera, and you want to take the pre-image processing and now must find out what objects are, perhaps comparing them with a large library of drawings, it is processing sensory computation.Â data is a very complex thing to try to do, but the robot needs to have this for a 'brain'. Â The brain must be capable of processing analog or digital cables to connect all support electronics to go with the team, and batteries to power the whole thing in order to process the perception sensory data.Â requires that the robot has sensors (supply and electronics), calculated (more energy and electronics, and connectors (for connect everything). Â

Sensor Switch

Switches are the simplest sensors of all.Â work without further processing, electronics (circuits) level.Â The overarching principle is that of an open front circuit.Â If a switch closed is open, no current can flow, if closed, current can flow and detected.Â This simple principle can (and is) used in a wide variety of forms.

Switch sensors can be used in a variety of ways:

contact sensors: detect when the sensor has contact with another object (for example, triggered when an occurrence of a robot grasps an object or wall, which can even be whiskers)

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

shaft encoder sensors: detects how many times becomes a shaft with a click of the switch (open / close) every time the shaft rotates (eg, the triggers for each shift, taking into account rotations)

Â Â There are many common switches: switches, buttons, switches, mouse, keyboard keys, phone keys, and others.Â Depending on how connected a switch may be normally open or normally closed.Â This, of course, depend on the electronics of their robot, mechanics, and task.Â The simplest but very useful sensor for a robot is a "package switch" that tells you when it bumped into something, so that can support and give back. Even for a simple idea, there are many different ways of application.

Light Sensors

Contact physical measurement switches and light sensors measure the amount of light impacting a photocell, which is basically sensor.Â one resistance The resistance of a photocell is low when lit, that is, when is lightweight, is high when Dark.E In this regard, a light sensor is actually a "dark" sensor.Â In the creation of a sensor cell, may end up using the equations we have learned previously, because you will need to address the relationship of the photoelectric picture of resistance, and resistance and tension in the sensor electronics circuit.Â Of course, It is to be the construction of electronics and write the software to measure and use the light sensor output, one can always manipulate to get around What else intuitive.Â simpler and a light sensor affects the sensor can bea properties.Â protected and placed in several ways.Â multiple sensors can be arranged in useful configurations and isolate them from each other with shields.

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

Light sensors can measure:

light intensity (how light / dark en)

intensity differential (difference between photocells)

off 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" light from a source is not polarized, meaning that travels all orientations with respect to horizon.Â However, if there is a polarizing filter in front of a light, only light waves of a specific orientation of the filter will through.Â This is useful because it can now manipulate the remaining light with other filters, if put through another filter with the same feature level, almost all of it will get through.Â But if we use a filter perpendicular (one with an angle of 90 degrees relative property), we will block all light.Â polarized light can be used to manufacture specialized sensors out of simple photocells, 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 ably handle what and how much light detect.Â

Resistive Position Sensors

Â Â Â We said before that a photocell device.Â is a resistive We can also sense the resistance in response to other physical properties such as bending.Â resistance device increases with double the amount bent.Â These sensors were originally developed for video game control (eg, Nintendo Powerglove), and are usually very notice useful.Â repeated bending sensor.Â is spent is not surprising that a curve of the sensor is much less robust than the light sensors, although using the same principle underlying resistance.

Potentiometers

Â Â Â These devices are common to manual tuning, you've probably seen in some controls (such as volume and tone of the stereo). Called Â pots, which allow the user to manually adjust the resistance.Â The general idea is that the device consists of a movable tap along two fixed ends.Â As the key moves, resistance changes.Â As you can imagine, the resistance between the two ends is fixed, but the resistance between the moving part, and varies at each end as the part is moved.Â In robotics, pots are commonly used to detect and adjust the position of sliding and rotating mechanisms.

Biological Analogues

All sensors that are described in biological systems

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

Bend / receivers musclesÂ resistance

Reflective Opto Sensors

Â Â Â We have mentioned that if we use a light bulb in combination with a photodetector, we can take a break beam sensor. This idea is the underlying principle in reflexive optosensors: The sensor consists of a transmitter and a detector. Depending the willingness of those two to each other, we can obtain two types of sensors:

reflectance sensors (the emitter and detector are one side by side, separated by a barrier, objects are detected when the light reflects off them and back into the detector)

break sensors barrier (the emitter and detector facing each other, objects are detected if they interrupt the beam between the emitter and detector)

Â Â Â The issuer is usually a light emitting diode (LED) and photodiode detector is usually a / phototransistor.

Â Â Â Note that these are not the same technology as photocells resistance. Photocells resistance are nice and simple, but their resistance 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:

detecting the presence of objects

detection distance of the object

detection of surface characteristics (finding / following markers and tape)

/ Tracking Wall

encoding the axis of rotation (with an encoder wheel or color edges black and white)

barcode decoding

Â Â Â Note, however, that the reflectivity of the light depends on the color (and other properties) of a surface. An area of light to reflect light better than a dark one, and a black surface can not reflect in all, which is invisible to a light sensor. Therefore, it can be harder (less reliable) to detect objects thus darker than lighter. In the case of the lighter object distance, objects that are further away it seems closer than darker objects that are not that far. This gives an idea of how the physical world is partially visible. Although sensors have useful information that does not have complete and totally accurate.

Â Â Â Another source of noise in light sensors is ambient light. The best thing to do is to subtract the ambient light level sensor reading, in order to detect real change in reflected light, no ambient light. How does that happen? By taking two (or more, to be exact) detector readings, one with the issuer, and one with it off, and subtracting the two values of each other. The result is the level of ambient light, which can then be subtracted from future readings. This process is called sensor calibration. Of course, remember that the levels of ambient light can change, so that the sensor calibration may be required repeatedly.

Break-beam sensors

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

an incandescent bulb flashlight and a photocell

Visible red LED light-sensitive photo-transistors

or infrared emitters Red and IR detectors

Shaft Encoding

Encoders measure the spin of a position provide shaft and / or velocity information. For example, a speedometer that measures how quickly the wheels of a vehicle are becoming, while a odometer measures the number of rotations of the wheels.

To detect partial or complete rotation, we have to somehow mark the item decisive. This is usually done by attaching a round disc on the shaft, and cut notches in it. A light emitter and detector are placed on each side of the disc, so that as the notch passes between them, light passes, is detected and where there is no notch on the disk, no light passes.

If only one notch on the disk, then a rotation is detected as it happens. This is not a very good idea because it allows a low level of resolution for the measurement of speed: the smallest unit that can be measured is a complete rotation. In addition, some rotations may be lost due to noise.

Generally, notches are cut on the disc, and the light affects the impact detector counted. (You can see that it is important to have a sensor quick here, if the shaft becomes very quickly.)

An alternative to the reduction of notches in the hard disk is painted with black (absorbing, non-reflective) and white (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 sensor output will be a wave function of the intensity of light. This may be intended to have speed, counting the peaks of the waves.

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

measure the speed of an RBI (active) wheel

passive use a wheel that is dragged by the robot (measured with interest the progress made)

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

move in line straight

rotate by an exact amount

Note, however, that doing these things is quite difficult, as the wheels tend to slip (sound effector and error) and slide and there is usually some settling and reaction gear mechanism. Encoders can provide feedback to correct errors, but with some error is inevitable.

Quadrature Shaft Encoding

So far we have talked about the position and speed detection, but not talk about the direction of rotation. Suppose suddenly the wheel changes the direction of rotation, it would be useful for the robot to detect that.

An example of a common you need to measure the position, speed and direction is a computer mouse. Without a measure of direction, a mouse is pretty useless. How is the direction measured rotation?

Coding quadrature axis is an elaboration of the basic split beam idea, instead of using a single sensor, it takes two. The encoders are aligned so that the two streams of data from the detector and the fourth cycle (90-degrees) out of phase, hence the name squareness. By comparing the results of the two coders in each time step to time step output, we can say if there is an address change. When the two are sampled at each time step, only one of them will change their state (ie switch from on to off) at a time, because they are outside phase. That it is determined that the direction of the axis of rotation is. Every time a tree is moving in one direction, a counter is incremented, and when it turns to the contrary, the counter is decremented, so keep track of the overall position.

Other uses of encryption quadrature axis are in the arms of the robots with complex joints (such as Rotary ball joints; Think of your knee or shoulder), the Cartesian robot (and big printers), where 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 solution to this problem is the emission modulated light, ie, quickly turn the transmitter on and off. This signal is much easier and more reliably detected by a demodulator, which is tuned a particular frequency modulated light. Not surprisingly, a detector to detect the needs of multiple flashes in a row in order to detect a signal, ie for detect frequency. This is a small point, but is important in writing code demodulator.

The idea is modulated infrared light commonly used, for example in household remote controls.

Modulated light sensors are generally more reliable than those based sensors light. They can be used for the same purpose: the detection of the presence of an object by measuring the distance to a nearby object (electronic intelligence necessary, consult the course notes)

Infrared (IR) sensors

Infra red sensors are a type of light sensors, operating in the infrared part of the frequency of the infrared sensors are active sensors consist spectrum.Â: consist of a transmitter and a sensor receiver.Â Infrared is used in the same way that visible light sensors are discussed so far: as broken beams and as sensors.Â IR reflection is preferable to visible light in robotics (and other) applications because it suffers a little less environmental interference, which can be easily modulated, and simply because it is not visible.

IR Communication

Infrared modulation can be used as a serial line for message transmission. It is a fact as infrared modem work. There are two basic methods:

bit frames (including in the sample in the center of each bit, assuming all the bits to make the same amount of time for transmission)

bit intervals (more common commercial use, the sample on the falling edge, the time interval between sampling determines whether a 0 or 1)

Distance Ultrasonic detection

As mentioned earlier, ultrasound screening is based on the principle of flight time. The emitter produces a sonar "chirp" sound, away from the source, and if it encounters obstacles, is reflected in them and returns to the receiver (microphone). The amount of time it takes the sound beam to return are tracked (using a timer when the "chirp" is produced, and stop when the sound becomes reflected), and is used to calculate the distance the sound. This is possible (and easy), because we know that sound travels at the speed, this is a constant, varies slightly depending on ambient temperature.

At room temperature, sound travels at 1.12 meters per millisecond. Another way of saying that sound travels at 0.89 milliseconds per foot. This is a useful reminder constant.

The process of finding rather than one based on sonar is called echolocation. The inspiration for ultrasound screening comes from nature, bats use ultrasound instead of vision (this has sense, living in dark caves where vision would be virtually useless). Bat sonar are very sophisticated compared to artificial sonar, consisting on many different frequencies, used to find the smallest fast-flying prey, and for preventing hundreds of bats, and communication to find the couple.

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â

Specular Reflection

A major disadvantage of ultrasound screening is their susceptibility to specular reflection (specular reflection by the reflection of the outer surface of the object). While the sonar detection is based on the principle of sound wave reflecting surface and back 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 incident sound beam angle and the surface. The smaller the angle, the greater the probability of the sound is limited to "touch" the surface and bounce, so do not go back to the issuer, in turn generating a false long / distant reading. This is often called the specular reflection, because the 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 the sender. (For example, in our Robotics Laboratory on campus, sonar sensors are used, and are aligned part of the test area with cardboard, as it has reflective properties of sound much better than the very smooth wall behind him.)

In short, time sonar readings can be very inaccurate, as may result from the false in place of reflection. This should be taken into account when programming robot or a robot can produce very adverse and unsafe behavior. For example, a robot approaches a wall at a steep angle, you can not see the wall at all, and collide with it!

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

The first ultrasonic sensor commercial was produced by Polaroid, and is used to automatically measure the distance to the nearest object (presumably being photographed). These sensors simple Polaroid remains the most popular outside platform sonars (which come with a card processor that handles analog electronics). Their level properties include:

32-foot range

30-degree beam width

sensitivity to specular reflection

shortest return

Polaroid sensors can be combined in arrays phase to create more sophisticated sensors and more accurate.

One can find ultrasound used in a variety of other applications, the best known is that they go underwater. Sonars do not have much more focused and longer term has beams. Simplification and common applications involve automatic "tape-measures", height measurement, burglar alarms, etc.

Vision

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

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

The general principle of the camera light is scattered by objects in the environment (which are called scene), passes through an opening ( "Iris" in the simplest case, a pinhole, in the most sophisticated of a lens), and that influence what is called image plane. In biological systems, the image plane is the retina, which is attached to many rods and cones (photosensitive elements) which, in turn, bind to the nerves that carry out the "first vision", and then pass information on the whole brain to make "higher level" process of vision. As mentioned before, a large percentage of humans (and other) of animal brains devoted to visual processing, so this is a very complex task.

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

In the view of the machine, the computer must make sense of the information obtained in the image plane. If the camera is very simple, and uses a small pinhole, then some calculations are needed to calculate the projection of the objects of the environment in the image plane (note that will be reversed). If this is a goal (as in the eyes of vertebrates and real cameras), then more light can enter, but the price of being focused; objects only a range of distances from the lens is in focus. This range of distances is called depth of field camera.

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, are 120 x 10 ^ 6 bars and 6 x 10 ^ 6 cones in the eye, willing hexagonal). Let call the projection onto the image plane of the image.

The brightness of each pixel of the image is proportional to the amount of light directed into the chamber through the revision of the object surface that projects of that pixel. (This of course depends on the reflective properties of the surface of the patch, position and distribution of light sources in the environment, and the amount of reflected light from other objects in the scene in the review of the surface.) As a result, the brightness of a patch depends on two types of reflections, one to speculate (not the surface, as shown above) and the other is diffuse (light that penetrates the object, absorbed, and then re-emitted). To model correctly reflected light and reconstruct the scene, all these properties are necessary.

Suppose we are dealing with a black and white camera with a plane of 512 x 512 pixel image. Now we have an image, which is a collection of pixels, each which is an intensity between white and black. To find an object in that picture (if there is one, of course, we know a priori), the typical first step ( "first vision ") is to make the edge detection, ie, find all edges. How do we recognize them? edges are defined as curves in the image plane through which there is a significant change in brightness.

A simple approach would be to look for sharp changes in brightness by image differentiation and look for areas where the magnitude of the derivative is large. This almost works, but unfortunately it produces all sorts of spurious peaks, ie the noise. Also, do not per se can distinguish changes in intensity due to the shadows of those due to physical objects. But let's forget that for now and think about noise. How can we deal 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 convolution of image with many filters with different guidelines. Fortunately, relatively complicated mathematics involved in edge detection is well studied, and there are now standard and preferred criteria the edge detection.

Once we have the edge, the next step is to try to find objects from all sides. Segmentation is the process of division or organization of the image into parts that correspond to continuous objects. 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 the line models of the drawings of objects (from several possible angles, and in many different scales as possible!) and then compare these with all possible combinations of the edges of the image. Note note that this is a very computationally intensive and costly. This general approach, which has been extensively studied, is called the vision-based model.

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

We can use stereo (ie, binocular stereopsis, two eyes / cameras / viewpoints). As with the vision of moving up, but without moving, we have two images that may undermine each other, if we know what the differences between them must be, that is, if we know how the two chambers are organized / Position over others.

We can use the texture. The uniform texture patches that are consistent, and have almost identical brightness, so that we can assume that originate from the same object. The removal of those who may get a clue about which parts may belong to the same object in the scene.

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

Note that using all the strategies mentioned in biological vision. It is difficult to recognize objects or unexpected brand new (because we have no models at all, or not in the list). Movement helps to get our attention. Stereo, ie two eyes, is crucial, and all the carnivores use it (have two eyes pointing in the same direction, unlike the herbivores). The brain does an excellent job of quickly extracting of the information we need for the stage.

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

Use color, look specifically and uniquely colored objects, and recognizing that way (as the stop signs, for example)

Use a small plane of the image instead of a matrix of 512 x 512 pixels, we can reduce our vision much less, for example, just a line (called a CCD linear). Of course, there is much less information than the image, but if we're smart, and know what to expect, we can process what we see through a quick and useful.

Using other, simpler and faster, sensors, and combine those with vision. For example, infrared cameras to isolate people by body temperature. The clamp allows us to touch and move objects, after we can ensure that there is.

Use the information on the environment, if you know you'll be driving on the highway, which has lines white, look specifically for the lines in the places indicated in the image. This is the first and even faster driving highways and byways of robotic is done.

These and many other intelligent techniques must be employed when considering how important it is to "see" in real time. Consider the highway driving like a large and growing application of robotics and artificial intelligence. Everything is moving so rapidly that the system must perceive and act in time to react and safe protective and intelligent.

Now that you know how complex which is the vision, you can see why it was not used in the first robots, and still not used for all applications, and definitely not mere robots. A robot can be very useful, without vision, but some tasks require it. As always, it is essential to think about the appropriate match between the sensors of the robot and the task.

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

Lec 17 | MIT 3.091 Introduction to Solid State Chemistry

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