A* Search algorithm implementation. A* Search algorithm is one of the best and popular technique used in path-finding and graph traversals. Using Vanilla JS and p5.js for visualization. (Random generate map ) Practise.
https://en.wikipedia.org/wiki/A*_search_algorithm
https://www.geeksforgeeks.org/a-search-algorithm/
https://github.com/NemesLaszlo/A-Pathfinding-Visualization
function reconstruct_path(cameFrom, current)
total_path := {current}
while current in cameFrom.Keys:
total_path.prepend(current)
current := cameFrom[current]
return total_path
// A* finds a path from start to goal.
// h is the heuristic function. h(n) estimates the cost to reach goal from node n.
function A_Star(start, goal, h)
// The set of discovered nodes that may need to be (re-)expanded.
// Initially, only the start node is known.
openSet := {start}
// For node n, cameFrom[n] is the node immediately preceding it on the cheapest path from start to n currently known.
cameFrom := an empty map
// For node n, gScore[n] is the cost of the cheapest path from start to n currently known.
gScore := map with default value of Infinity
gScore[start] := 0
// For node n, fScore[n] := gScore[n] + h(n).
fScore := map with default value of Infinity
fScore[start] := h(start)
while openSet is not empty
current := the node in openSet having the lowest fScore[] value
if current = goal
return reconstruct_path(cameFrom, current)
openSet.Remove(current)
for each neighbor of current
// d(current,neighbor) is the weight of the edge from current to neighbor
// tentative_gScore is the distance from start to the neighbor through current
tentative_gScore := gScore[current] + d(current, neighbor)
if tentative_gScore < gScore[neighbor]
// This path to neighbor is better than any previous one. Record it!
cameFrom[neighbor] := current
gScore[neighbor] := tentative_gScore
fScore[neighbor] := gScore[neighbor] + h(neighbor)
if neighbor not in openSet
openSet.add(neighbor)
// Open set is empty but goal was never reached
return failure