DataProcessing.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet href="documentation.xsl" type="text/xsl"?>
<section title="Data Processing" linked-project="data manipulation">
	<content>
		There are many application within the robot that require a simple
		input-output object, where a sensor is used as an input, and a
		calculated value is output. For this usage, we defined a basic
		<class-name>DataProcessor</class-name>
		class:
		<code src="technobotts.util.DataProcessor" />
		From this, we derived a class for a PID controller, and a class to
		exponentially smooth data inputs
	</content>
	<section title="PID Controller">
		<content>
			A PID controller
			<code src="technobotts.util.SimplePID" />
		</content>
	</section>
	<section title="Exponential smoothers">
		<content>There are a couple of noisy inputs which our robot needs to
			smooth before they are useful. These are the output angle of the
			goal-detecting camera, and the angle of the ball as received by the
			infra-red detectors. In order to smooth these, we decided to use an
			exponential smoother.</content>
		<section title="The math">
			<content>
				The basic equation for an exponential smoother is as follows, where α
				is a constant:
				<math>\text{average} = \alpha \times \text{data} + (1-\alpha) \times
					\text{average}</math>
				This can be rearranged to:
				<math>\text{average} = \text{average} + \alpha \times (\text{data} -
					\text{average})</math>
				Which in turn, can be written simply in java code as:
				<code>
					average += alpha * data;
				</code>
			</content>
			<content>
				However, this assumes a consistent iteration rate: the time between
				successive applications of the formula must remain constant. This
				isn't really feasible with the robot, as the sensors will be polled
				irregularly, the delay depending on the robot's circumstance.
				Therefore, α
				cannot be constant, and must be adjusted to reflect the poll time. A
				detailed explanation can be found at <link>http://stackoverflow.com/questions/1023860/exponential-moving-average-sampled-at-varying-times
				</link>. In short, α can be calulated using:
				<math>\alpha \times \text{data} + (1-\alpha) \times
					\text{average}
				</math>
			</content>
		</section>
	</section>
</section>