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  2. Any-angle path planning - Wikipedia

    en.wikipedia.org/wiki/Any-angle_path_planning

    Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. [ 1 ]

  3. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    Dijkstra's algorithm, as another example of a uniform-cost search algorithm, can be viewed as a special case of A* where ⁠ = ⁠ for all x. [ 12 ] [ 13 ] General depth-first search can be implemented using A* by considering that there is a global counter C initialized with a very large value.

  4. Anytime A* - Wikipedia

    en.wikipedia.org/wiki/Anytime_A*

    In computer science, anytime A* is a family of variants of the A* search algorithm.Like other anytime algorithms, it has a flexible time cost, can return a valid solution to a pathfinding or graph traversal problem even if it is interrupted before it ends, by generating a fast, non-optimal solution before progressively optimizing it.

  5. Iterative deepening A* - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_A*

    Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...

  6. SMA* - Wikipedia

    en.wikipedia.org/wiki/SMA*

    SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory. All other characteristics of SMA* are inherited from A*.

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  8. Graphplan - Wikipedia

    en.wikipedia.org/wiki/Graphplan

    Graphplan is an algorithm for automated planning developed by Avrim Blum and Merrick Furst in 1995. Graphplan takes as input a planning problem expressed in STRIPS and produces, if one is possible, a sequence of operations for reaching a goal state.

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