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On the theory side, this is related to the Nyquist Sampling Rate, which is how frequently you must measure a single to get an accurate reconstruction of it's peaks / valleys. Not suprisingly, Nyquist as a name appears all over some fundamental results in optimal control like the nyquist stability theorem. I suspect the insight you are looking for is right ...


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This seems like an interesting question! Read it once and haven't put a ton of thought into this, but my gut reaction was a disturbance model. Suppose you had some optimal path, $P$. Your path planning could be attempting to map that optimal path, but the calculated path might be constantly updating based on noise in sensor readings, localization estimates, ...


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You can set the angular velocity to whatever you want, that's the control input and depends on the algorithm. You have to set it to be something proportional to the difference in the desired angle (from the intercept trajectory) and the actual angle as measured. You then update the desired angle and velocity and re-calculate. Try this: https://robotics....


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