In 2020
Path planning using many algorithms:1_ breadth-first-search: for unweighted edges
2_ depth-first-search: It has some advantages over other algorithms in rare cases like storage and completeness if there are infinity children for the node.
3_Dijkstra's: It has the advantage of finding the shortest path between the start node and all other nodes, not only the goal node
4_ cheapest-first-search: it has an advantage over Dijkstra's in-memory storage but has uniform contours.
5_ gready_best_search_algorithm: by using a heuristic, it focuses on the goal so, it is faster than cheapest-first-search
but has drawbacks if there are obstacles, it doesn't guarantee to find the shortest path.
6_ finally A*: it combines between efficiency and finding the optimal solution but depends on choosing admissible(for finding an optimal solution) and (consistent for efficiency optional) heuristic
Thanks for
professor: Peter Norvig, for his good lectures about problem-solving-technology.
professor: Sebastian Thrun for his support.
Have fun!
note:
Using a Fibonacci priority queue, it has a better performance
than any priority queue like binary because of insertion
https://lnkd.in/dQe2CRC
Have fun!
note:
Using a Fibonacci priority queue, it has a better performance
than any priority queue like binary because of insertion
https://lnkd.in/dQe2CRC
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