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A* (pronounced "A-star") is a graph traversal and pathfinding algorithm that is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. [1] Given a weighted graph, a source node and a goal node, the algorithm finds the shortest path (with respect to the given weights) from source to goal.
In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, [1] eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.
It is a generalization of pathfinding. Many multi-agent pathfinding algorithms are generalized from A*, or based on reduction to other well studied problems such as integer linear programming. [11] However, such algorithms are typically incomplete; in other words, not proven to produce a solution within polynomial time.
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.
For the simplest version of Theta*, the main loop is much the same as that of A*. The only difference is the _ function. Compared to A*, the parent of a node in Theta* does not have to be a neighbor of the node as long as there is a line-of-sight between the two nodes.
So far, five main any-angle path planning algorithms that are based on the heuristic search algorithm A* [3] have been developed, all of which propagate information along grid edges: Field D* [ 4 ] [ 5 ] (FD* [ 6 ] ) and 3D Field D* [ 7 ] [ 8 ] - Dynamic pathfinding algorithms based on D* that use interpolation during each vertex expansion and ...
LPA* maintains two estimates of the start distance g*(n) for each node: . g(n), the previously calculated g-value (start distance) as in A*; rhs(n), a lookahead value based on the g-values of the node's predecessors (the minimum of all g(n' ) + d(n' , n), where n' is a predecessor of n and d(x, y) is the cost of the edge connecting x and y)
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 ...