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 ...


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 ...


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.


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.


A first prototype for the snake game is working, see the figure below. It was realized with the RRT algorithm, or to be more specific with the “hierarchical RRT” variant. That was the easy part, because it is classical pathplanning. The second aspect in the OP is a bit more difficult to handle: heuristics and q-learning. Usually there are constraints given ...

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