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The exact same algorithm works beautifully for planning the shortest path
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to a single mission goal from any possible start location,
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and the only difference here is in this graph over here of an actual road graph
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we are also incorporating the heading direction as measure of distance.
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Green corresponds to nearby to large values, red to far away.
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The reason why the area below the mission goal is green is because we expect
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the car to point up, to point north, at the time it reached the mission.
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So if it came from the north, it would point in the wrong direction.
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The state space is augmented correspondingly.
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Where if it comes from over here, it points in the correct direction.
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If you look at the circle over here, it's interesting.
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If we came from the left side over here, it could do a right turn,
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but over here it's forced on this one-way circle to do the entire loop to go around,
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and that increases the value as it comes over here.
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This is value iteration applied to the road graph where we keep track of heading
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and where the circle over here is a one-way circle.
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