vb.net barcode reader from image FAR-OBJECT DETECTION in Software


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Far-object detection focuses on objects that are outside the robot s primary area of interest but still within a detection range. A wall 50 ft away is not of critical importance to a robot (conversely, the same wall when it s 1 ft away is very important). Far-object detection is typically used for area and scene mapping to allow the robot to get a sense of its environment. Most hobby robots don t employ far-object detection because it requires fairly sophisticated sensors, such as narrow-beam radar or pulsed lasers. The difference between near- and far-object detection is relative. As the designer, builder, and master of your robot, you get to decide the threshold between near and far objects. Perhaps your robot is small and travels fairly slowly. In that case, far objects are those 4 to 5 ft away; anything closer is considered near. With such a robot, you can employ ordinary sonar distance systems for far-object detection, including area mapping. This chapter will concentrate on near-object detection methods since traditional farobject detection is beyond the reach and riches of most hobby robot makers (with the exception of sonar systems, which have a maximum range of about 30 ft). You may, if you wish, employ near-object techniques to detect objects that are far away relative to the world your robot lives in.
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Engineering texts like to tout the concept of KISS: Keep It Simple, Stupid. If the admonition is intentionally insulting it is to remind all of us that usually the simple techniques are the best. Of course, simplicity is relative. An ant is simple compared to a human being, but so far no scientist has ever created the equivalent of a living ant. KISS certainly applies to using robotic sensors for object detection. Your ultimate goal may be to put eyes on your robot to help it see the world the way that you do. In fact, such eyes already exist in the form of CCD and CMOS video imagers. They re relatively cheap, too less than $50 retail for camera modules or hackable webcams. What s missing in the case of vision systems is the ability to process the incoming signal ways to use the wealth of information provided by the sensor. How do you make a robot differentiate between a can of Dr. Pepper and Mrs. Johnson s slobbering two-year-old both of which are very wet when tipped over When you think about which object detection sensor or system to add to your robot, consider the system s relative complexity in relation to the rest of the project. If all your small bot needs is a bumper switch, then avoid going overboard with a $100 sonar system. Con-
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versely, if the context of the robot merits it, skimp with inadequate sensors. Larger, heavier robots require more effective object-detection systems if for no other reason than to prevent injuring you if it happens to start to run amok.
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Two heads are better than one Maybe. One thing is for sure: two eyes are definitely better than one. The same goes for ears and many other kinds of sensors. This is sensor redundancy at work (having two eyes and ears also provides stereo vision or hearing, which aids in perception). Sensor redundancy especially for object detection is not intended primarily to compensate for system failure, the way NASA builds backups into its space projects in case some key system fails 25,000 miles up in space. Rather, sensor redundancy is meant to provide a more complete picture and better situational awareness for the robot by using multiple sensors with different characteristics. If a long-range sensor says an object is 10 ft away and a mid-range sensor that is 8 ft away says it s a foot away, then the robot s control computer has dual conformation that there is an object present and can be confident that it is about 8 ft away. With only one sensor the robot must blindly (excuse the pun) trust that the sensor data is reliable. This is not a good idea because even for the best sensors data is not 100 percent reliable. There are two kinds of redundancy:
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Same-sensor redundancy relies on two or more sensors of an identical type. Each sensor more or less sees the same scene. You can use sensor data in either (or both) of two ways: through statistical analysis or interpolation. With statistical analysis, the robot s control circuitry combines the input from the sensors and uses a statistical formula to whittle the data to a most likely result. For example, sensors with wildly disparate results may be rejected out of hand, and the values of the remaining sensors may be averaged out. With interpolation, the data of two or more sensors is combined and crosscorrelated to triangulate on the object, just like having two eyes and two ears adds depth and direction to our visual and aural senses. Complementary-sensor redundancy relies on two or more sensors of different types. Since the sensors are fundamentally different for example, they use completely different collection methods the data from the sensors are always interpolated. For instance, if a robot has both a sonar and an infrared distance-measuring system, it uses both because it understands that for some kinds of objects the data from the infrared system will be more reliable, and for other objects the data from the sonar system will be more reliable. The previous example of the object 8 ft away is an example of complementary sensor redundancy.
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Budget and time constraints will likely be the limiting factors in whether you employ redundant sensor systems in your robots. So when combining sensors, do so logically: consider which sensors complement others well and if they can be reasonably added. For example, both sonar and infrared proximity sensors can use the same 40 kHz modulation system. If you have one, adding the other need not be difficult, expensive, or timeconsuming.
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