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Brushless DC motors exhibit the same linear speed torque characteristics as the brush-type PM DC motors, but they are electronically commutated. The construction of these motors, as shown in Figure 1-22, differs from that of a typical brush-type DC motor in that they are inside-out. In other words, they have permanent magnet rotors instead of stators, and the stators rather than the rotors are wound. Although this geometry is required for brushless DC motors, some manufacturers have adapted this design for brush-type DC motors. The mechanical brush and bar commutator of the brushless DC motor is replaced by electronic sensors, typically Hall-effect devices (HEDs). They are located within the stator windings and wired to solidstate transistor switching circuitry located either on circuit cards mounted within the motor housings or in external packages. Generally, only fractional horsepower brushless motors have switching circuitry within their housings. The cylindrical magnet rotors of brushless DC motors are magnetized laterally to form opposing north and south poles across the rotor s diameter. These rotors are typically made from neodymium iron boron or samarium cobalt rare-earth magnetic materials, which offer higher flux densities than alnico magnets. These materials permit motors offering higher performance to be packaged in the same frame sizes as earlier motor designs or those with the same ratings to be packaged in smaller frames than the earlier designs. Moreover, rare-earth or ceramic magnet

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Supervision can be applied to the mapping problem. The robot is given a map that is already lled in and it tries to keep track of its position on this map. The odometry methods place the robot on the map as it wanders across the center. Then, when the robot senses a wall it can compare its sensory input to the map and adjust its concept of where it is on the map. This combines the data from the odometry system with sensory data and applies it against the template of the built-in map. If the robot reaches a corner, it can perform an even more accurate realignment of its internal map, since the corner constrains the robot s position along two axes of motion. Many arti cial intelligence technologies use a form of supervised learning. Supervised learning provides both the question and an answer, and the system learns the connection between them. We apply a training pattern to patternmatching neurons in Fig. 18-7. In this type of supervised training, the programmer sends an input pattern xn to the neurons as well as the desired output Tm, or training pattern, upstream to the outputs ym. The input is processed and the actual results are compared to the desired output. The di erence, or error, is propagated

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17.11.1 4.3 V Zener Battery Monitor 17.11.2 Zener/Comparator Battery Monitor 17.11.3 Using a Battery Monitor with a Microprocessor

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Fig. 18-7.

17.12 A Robot Testing Power Supply 17.13 From Here 18 Principles of Robot Locomotion 18.1 First Things First: Weight 18.2 Tips for Reducing Weight 18.3 Beware of the Heavy Frame 18.4 Construction Robots with Multiple Decks 18.5 Frame Sagging Caused by Weight 18.6 Horizontal Center of Balance 18.7 Vertical Center of Gravity 18.8 Locomotion Issues

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rotors can be made with smaller diameters than those earlier models with alnico magnets, thus reducing their inertia. A simplified diagram of a DC brushless motor control with one Halleffect device (HED) for the electronic commutator is shown in Figure 1-23. The HED is a Hall-effect sensor integrated with an ampli-

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