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As you say, GPS is not precise enough for the purpose (until recently it was only accurate within 5m or so, since 2018 there are receivers that have an accuracy of about 30cm). Instead, autonomous vehicles have a multitude of sensors, mostly cameras and radar, which record the surrounding area and monitor the road ahead. Due to them being flat, mostly one ...


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Let's say I have a white image, a green blob in the middle, and points $A$ and $B$ at either side of the blob, I need to get from $A$ to $B$. I don't have points to traverse. I just have this image. If your image is as "simple" as you describe here, with very easily distinguishable colours, the easiest solution would likely be to construct a graph ...


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The usual way to solve this kind of problem is to construct a configuration space: extruding all the polygonal obstacles by sliding the polygon corresponding to the robot around them (some slides). The exterior vertices of the configuration space can then be used as input to a path-planning algorithm, such as A*.


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A Hamiltonian path in a graph is a path that visits all the nodes/vertices exactly once, a hamiltonian cycle is a cyclic path, i.e. all nodes visited once and the start and the endpoint are the same. If we want to solve the snake game using this, we could divide the playable space in a grid and then try to just keep traversing on a hamiltonian cycle, this ...


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Should I have all paths as the population, No, this is not usually possible for more realistic problems where a population that covered all possibilities would be far too large to manage. or should I create a population same size as the nodes(17). No, there is no need to link the population size to other properties of the problem so directly. If your path ...


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Defining Path You are right to be confused if your professors did not clarify (see warning at end). The term "path" can mean a few things: "Concrete" Path: Recall, a graph is a collection of vertices and edges. A path on the graph is then: $$v_1\xrightarrow{e_1} v_2\xrightarrow{e_2} \cdots \xrightarrow{e_{n-1}} v_n$$ Where $v_i$ are vertices on the graph ...


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Yes, this is easily solved using the A* algorithm. Once your agent has visited a particular node, increase the cost of that node to infinity and recalculate the path.


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The idea is to apply a well known pathfinding algorithm (such as Dijkstra) on the graph given the rule: "only black and red edges" Remove all the non-black-and-red edges from the graph first, then run it through any off-the-shelf pathfinding implementation. Or implement your own, and have it completely ignore the non-black-and-red edges.


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