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This website was really helpful for me when learning all about robotics: https://robotacademy.net.au It has lots of info on motion planning / path planning too. I guess this isn't a direct answer to your question but for anyone coming here looking for educational resources, this is a good one.

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Assuming RRT is done in two steps: Grow your tree from start state until one node is near enough to the goal state Output path from start state to goal state along tree I'm assuming based on your question that you're asking if part 2 is a graph search? Usually in RRT, each node has exactly one parent (since it's a tree) and you can just walk back from the ...

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I would argue, that it is not graph search. In the implementation, you keep all the nodes in a flat list and check which of the nodes is closest to the sampled point. As all nodes are checked, this might be seen as a brute-force graph search, but in the implementation is just for loop iterating though all the points in a list. The goal check is done for the ...

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1- No it is not. This example is the beginning of RL, while Self-driving cars are way much complex. In a simplified view, there are two main differences. state and action in the shown example are discrete, while for a real robotic application are continuous. 2- Despite the promising results, still RL is far from global path planning -planning to long ahead- ...

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