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It demonstrates instantiating the class and filling a bucket (as it tries to leak out) until the bucket is full. A sample program is commented out at the bottom. If too much water is poured in, the bucket will eventually have the maximum amount in it and the rest is lost over the top edges. If the water stops pouring in, eventually the level will decrease to the level of the side drain. Water poured into the top fills the bucket if more water comes in than is let out the bottom. As the robot escapes, the memory of the corner fades to prevent drastic actions on bumps later in its journey when it may not be stuck in a corner.Īn analogy for the leaky integrator is a bucket with a drain in the side that can be opened to let some water out. This value could be used to adjust the spin angle for the robot so that the more times the robot bumps, the more it turns to get out of the corner. As the robot thrashes more the integrator is filled up. Using a Leaky Integrator allow the successive bumps to be summed in the short-term memory while at the same time leaking some bumps out of the bottom at a slower rate.
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The robot is essentially stuck in the corner, thrashing back and forth until something changes. This behavior is called canyoning and seems to happen often. This is useful, for instance, when a robot gets stuck in a corner, bumping left and them bumping right and repeating.
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I have been working to update my framework lately and I ran across and interesting concept that can be used by robots to track recurring events and store them in a short-term memory that eventually fades.
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You can download the code here: WeightedAverage.c This class can help filter sensor data to a robot, making it react more slowly to large value changes. Weighted Average = New Sample * %wt + (1-%wt)*Previous Average In other words, I don’t care what the actual average is I care that the trend of values over time reduces the spikes and provides usable information to the robot with less error. I am sure this method is not as accurate as averaging the last n values, but the goal is not so much precision, but reducing spikes and anomalies. I found a shortcut to all of that where an average value is maintained in the background, and a weighted percentage is applied to the newest input value and the previous average. It also means you have create some sort of queue so the values can shift such that the n most current samples are available for calculation. Creating a running average of a stream of numbers can be complicated, and consume many variables (depending on how many samples make up the average). Continuing on my current fascination with math and code to implement it, I decided to write a class for weighted average monitoring.