A* search algorithm

From Rosetta Code
A* search algorithm is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

The A* search algorithm is an extension of Dijkstra's algorithm useful for finding the lowest cost path between two nodes (aka vertices) of a graph. The path may traverse any number of nodes connected by edges (aka arcs) with each edge having an associated cost. The algorithm uses a heuristic which associates an estimate of the lowest cost path from this node to the goal node, such that this estimate is never greater than the actual cost.

The algorithm should not assume that all edge costs are the same. It should be possible to start and finish on any node, including ones identified as a barrier in the task.

Task

Consider the problem of finding a route across the diagonal of a chess board-like 8x8 grid. The rows are numbered from 0 to 7. The columns are also numbered 0 to 7. The start position is (0, 0) and the end position is (7, 7). Movement is allow by one square in any direction including diagonals, similar to a king in chess. The standard movement cost is 1. To make things slightly harder, there is a barrier that occupy certain positions of the grid. Moving into any of the barrier positions has a cost of 100.

The barrier occupies the positions (2,4), (2,5), (2,6), (3,6), (4,6), (5,6), (5,5), (5,4), (5,3), (5,2), (4,2) and (3,2).

A route with the lowest cost should be found using the A* search algorithm (there are multiple optimal solutions with the same total cost).

Print the optimal route in text format, as well as the total cost of the route.

Optionally, draw the optimal route and the barrier positions.

Note: using a heuristic score of zero is equivalent to Dijkstra's algorithm and that's kind of cheating/not really A*!

Extra Credit

Use this algorithm to solve an 8 puzzle. Each node of the input graph will represent an arrangement of the tiles. The nodes will be connected by 4 edges representing swapping the blank tile up, down, left, or right. The cost of each edge is 1. The heuristic will be the sum of the manhatten distance of each numbered tile from its goal position. An 8 puzzle graph will have 9!/2 (181,440) nodes. The 15 puzzle has over 10 trillion nodes. This algorithm may solve simple 15 puzzles (but there are not many of those).

See also


Related tasks



C[edit]

 
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <float.h>
/* and not not_eq */
#include <iso646.h>
/* add -lm to command line to compile with this header */
#include <math.h>
 
#define map_size_rows 10
#define map_size_cols 10
 
char map[map_size_rows][map_size_cols] = {
{1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
{1, 0, 0, 0, 0, 0, 0, 0, 0, 1},
{1, 0, 0, 0, 0, 0, 0, 0, 0, 1},
{1, 0, 0, 0, 0, 1, 1, 1, 0, 1},
{1, 0, 0, 1, 0, 0, 0, 1, 0, 1},
{1, 0, 0, 1, 0, 0, 0, 1, 0, 1},
{1, 0, 0, 1, 1, 1, 1, 1, 0, 1},
{1, 0, 0, 0, 0, 0, 0, 0, 0, 1},
{1, 0, 0, 0, 0, 0, 0, 0, 0, 1},
{1, 1, 1, 1, 1, 1, 1, 1, 1, 1}
};
 
/* description of graph node */
struct stop {
double col, row;
/* array of indexes of routes from this stop to neighbours in array of all routes */
int * n;
int n_len;
double f, g, h;
int from;
};
 
int ind[map_size_rows][map_size_cols] = {
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1}
};
 
/* description of route between two nodes */
struct route {
/* route has only one direction! */
int x; /* index of stop in array of all stops of src of this route */
int y; /* intex of stop in array of all stops od dst of this route */
double d;
};
 
int main() {
int i, j, k, l, b, found;
int p_len = 0;
int * path = NULL;
int c_len = 0;
int * closed = NULL;
int o_len = 1;
int * open = (int*)calloc(o_len, sizeof(int));
double min, tempg;
int s;
int e;
int current;
int s_len = 0;
struct stop * stops = NULL;
int r_len = 0;
struct route * routes = NULL;
 
for (i = 1; i < map_size_rows - 1; i++) {
for (j = 1; j < map_size_cols - 1; j++) {
if (!map[i][j]) {
++s_len;
stops = (struct stop *)realloc(stops, s_len * sizeof(struct stop));
int t = s_len - 1;
stops[t].col = j;
stops[t].row = i;
stops[t].from = -1;
stops[t].g = DBL_MAX;
stops[t].n_len = 0;
stops[t].n = NULL;
ind[i][j] = t;
}
}
}
 
/* index of start stop */
s = 0;
/* index of finish stop */
e = s_len - 1;
 
for (i = 0; i < s_len; i++) {
stops[i].h = sqrt(pow(stops[e].row - stops[i].row, 2) + pow(stops[e].col - stops[i].col, 2));
}
 
for (i = 1; i < map_size_rows - 1; i++) {
for (j = 1; j < map_size_cols - 1; j++) {
if (ind[i][j] >= 0) {
for (k = i - 1; k <= i + 1; k++) {
for (l = j - 1; l <= j + 1; l++) {
if ((k == i) and (l == j)) {
continue;
}
if (ind[k][l] >= 0) {
++r_len;
routes = (struct route *)realloc(routes, r_len * sizeof(struct route));
int t = r_len - 1;
routes[t].x = ind[i][j];
routes[t].y = ind[k][l];
routes[t].d = sqrt(pow(stops[routes[t].y].row - stops[routes[t].x].row, 2) + pow(stops[routes[t].y].col - stops[routes[t].x].col, 2));
++stops[routes[t].x].n_len;
stops[routes[t].x].n = (int*)realloc(stops[routes[t].x].n, stops[routes[t].x].n_len * sizeof(int));
stops[routes[t].x].n[stops[routes[t].x].n_len - 1] = t;
}
}
}
}
}
}
 
open[0] = s;
stops[s].g = 0;
stops[s].f = stops[s].g + stops[s].h;
found = 0;
 
while (o_len and not found) {
min = DBL_MAX;
 
for (i = 0; i < o_len; i++) {
if (stops[open[i]].f < min) {
current = open[i];
min = stops[open[i]].f;
}
}
 
if (current == e) {
found = 1;
 
++p_len;
path = (int*)realloc(path, p_len * sizeof(int));
path[p_len - 1] = current;
while (stops[current].from >= 0) {
current = stops[current].from;
++p_len;
path = (int*)realloc(path, p_len * sizeof(int));
path[p_len - 1] = current;
}
}
 
for (i = 0; i < o_len; i++) {
if (open[i] == current) {
if (i not_eq (o_len - 1)) {
for (j = i; j < (o_len - 1); j++) {
open[j] = open[j + 1];
}
}
--o_len;
open = (int*)realloc(open, o_len * sizeof(int));
break;
}
}
 
++c_len;
closed = (int*)realloc(closed, c_len * sizeof(int));
closed[c_len - 1] = current;
 
for (i = 0; i < stops[current].n_len; i++) {
b = 0;
 
for (j = 0; j < c_len; j++) {
if (routes[stops[current].n[i]].y == closed[j]) {
b = 1;
}
}
 
if (b) {
continue;
}
 
tempg = stops[current].g + routes[stops[current].n[i]].d;
 
b = 1;
 
if (o_len > 0) {
for (j = 0; j < o_len; j++) {
if (routes[stops[current].n[i]].y == open[j]) {
b = 0;
}
}
}
 
if (b or (tempg < stops[routes[stops[current].n[i]].y].g)) {
stops[routes[stops[current].n[i]].y].from = current;
stops[routes[stops[current].n[i]].y].g = tempg;
stops[routes[stops[current].n[i]].y].f = stops[routes[stops[current].n[i]].y].g + stops[routes[stops[current].n[i]].y].h;
 
if (b) {
++o_len;
open = (int*)realloc(open, o_len * sizeof(int));
open[o_len - 1] = routes[stops[current].n[i]].y;
}
}
}
}
 
for (i = 0; i < map_size_rows; i++) {
for (j = 0; j < map_size_cols; j++) {
if (map[i][j]) {
putchar(0xdb);
} else {
b = 0;
for (k = 0; k < p_len; k++) {
if (ind[i][j] == path[k]) {
++b;
}
}
if (b) {
putchar('x');
} else {
putchar('.');
}
}
}
putchar('\n');
}
 
if (not found) {
puts("IMPOSSIBLE");
} else {
printf("path cost is %d:\n", p_len);
for (i = p_len - 1; i >= 0; i--) {
printf("(%1.0f, %1.0f)\n", stops[path[i]].col, stops[path[i]].row);
}
}
 
for (i = 0; i < s_len; ++i) {
free(stops[i].n);
}
free(stops);
free(routes);
free(path);
free(open);
free(closed);
 
return 0;
}
 
Output:
▒▒▒▒▒▒▒▒▒▒
▒x.......▒
▒.x......▒
▒.x..▒▒▒.▒
▒.x▒...▒.▒
▒.x▒...▒.▒
▒.x▒▒▒▒▒.▒
▒..xxxxx.▒
▒.......x▒
▒▒▒▒▒▒▒▒▒▒
path cost is 12:
(1, 1)
(2, 2)
(2, 3)
(2, 4)
(2, 5)
(2, 6)
(3, 7)
(4, 7)
(5, 7)
(6, 7)
(7, 7)
(8, 8)

C++[edit]

 
#include <list>
#include <algorithm>
#include <iostream>
 
class point {
public:
point( int a = 0, int b = 0 ) { x = a; y = b; }
bool operator ==( const point& o ) { return o.x == x && o.y == y; }
point operator +( const point& o ) { return point( o.x + x, o.y + y ); }
int x, y;
};
 
class map {
public:
map() {
char t[8][8] = {
{0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 1, 1, 1, 0}, {0, 0, 1, 0, 0, 0, 1, 0},
{0, 0, 1, 0, 0, 0, 1, 0}, {0, 0, 1, 1, 1, 1, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0}
};
w = h = 8;
for( int r = 0; r < h; r++ )
for( int s = 0; s < w; s++ )
m[s][r] = t[r][s];
}
int operator() ( int x, int y ) { return m[x][y]; }
char m[8][8];
int w, h;
};
 
class node {
public:
bool operator == (const node& o ) { return pos == o.pos; }
bool operator == (const point& o ) { return pos == o; }
bool operator < (const node& o ) { return dist + cost < o.dist + o.cost; }
point pos, parent;
int dist, cost;
};
 
class aStar {
public:
aStar() {
neighbours[0] = point( -1, -1 ); neighbours[1] = point( 1, -1 );
neighbours[2] = point( -1, 1 ); neighbours[3] = point( 1, 1 );
neighbours[4] = point( 0, -1 ); neighbours[5] = point( -1, 0 );
neighbours[6] = point( 0, 1 ); neighbours[7] = point( 1, 0 );
}
 
int calcDist( point& p ){
// need a better heuristic
int x = end.x - p.x, y = end.y - p.y;
return( x * x + y * y );
}
 
bool isValid( point& p ) {
return ( p.x >-1 && p.y > -1 && p.x < m.w && p.y < m.h );
}
 
bool existPoint( point& p, int cost ) {
std::list<node>::iterator i;
i = std::find( closed.begin(), closed.end(), p );
if( i != closed.end() ) {
if( ( *i ).cost + ( *i ).dist < cost ) return true;
else { closed.erase( i ); return false; }
}
i = std::find( open.begin(), open.end(), p );
if( i != open.end() ) {
if( ( *i ).cost + ( *i ).dist < cost ) return true;
else { open.erase( i ); return false; }
}
return false;
}
 
bool fillOpen( node& n ) {
int stepCost, nc, dist;
point neighbour;
 
for( int x = 0; x < 8; x++ ) {
// one can make diagonals have different cost
stepCost = x < 4 ? 1 : 1;
neighbour = n.pos + neighbours[x];
if( neighbour == end ) return true;
 
if( isValid( neighbour ) && m( neighbour.x, neighbour.y ) != 1 ) {
nc = stepCost + n.cost;
dist = calcDist( neighbour );
if( !existPoint( neighbour, nc + dist ) ) {
node m;
m.cost = nc; m.dist = dist;
m.pos = neighbour;
m.parent = n.pos;
open.push_back( m );
}
}
}
return false;
}
 
bool search( point& s, point& e, map& mp ) {
node n; end = e; start = s; m = mp;
n.cost = 0; n.pos = s; n.parent = 0; n.dist = calcDist( s );
open.push_back( n );
while( !open.empty() ) {
//open.sort();
node n = open.front();
open.pop_front();
closed.push_back( n );
if( fillOpen( n ) ) return true;
}
return false;
}
 
int path( std::list<point>& path ) {
path.push_front( end );
int cost = 1 + closed.back().cost;
path.push_front( closed.back().pos );
point parent = closed.back().parent;
 
for( std::list<node>::reverse_iterator i = closed.rbegin(); i != closed.rend(); i++ ) {
if( ( *i ).pos == parent && !( ( *i ).pos == start ) ) {
path.push_front( ( *i ).pos );
parent = ( *i ).parent;
}
}
path.push_front( start );
return cost;
}
 
map m; point end, start;
point neighbours[8];
std::list<node> open;
std::list<node> closed;
};
 
int main( int argc, char* argv[] ) {
map m;
point s, e( 7, 7 );
aStar as;
 
if( as.search( s, e, m ) ) {
std::list<point> path;
int c = as.path( path );
for( int y = -1; y < 9; y++ ) {
for( int x = -1; x < 9; x++ ) {
if( x < 0 || y < 0 || x > 7 || y > 7 || m( x, y ) == 1 )
std::cout << char(0xdb);
else {
if( std::find( path.begin(), path.end(), point( x, y ) )!= path.end() )
std::cout << "x";
else std::cout << ".";
}
}
std::cout << "\n";
}
 
std::cout << "\nPath cost " << c << ": ";
for( std::list<point>::iterator i = path.begin(); i != path.end(); i++ ) {
std::cout<< "(" << ( *i ).x << ", " << ( *i ).y << ") ";
}
}
std::cout << "\n\n";
return 0;
}
 
Output:
██████████
█x.......█
█x.......█
█x...███.█
█x.█...█.█
█x.█...█.█
█.x█████.█
█..xxxx..█
█......xx█
██████████

Path cost 11: (0, 0) (0, 1) (0, 2) (0, 3) (0, 4) (1, 5) (2, 6) (3, 6) (4, 6) (5, 6) (6, 7) (7, 7)

Go[edit]

// Package astar implements the A* search algorithm with minimal constraints
// on the graph representation.
package astar
 
import "container/heap"
 
// Exported node type.
type Node interface {
To() []Arc // return list of arcs from this node to another
Heuristic(from Node) int // heuristic cost from another node to this one
}
 
// An Arc, actually a "half arc", leads to another node with integer cost.
type Arc struct {
To Node
Cost int
}
 
// rNode holds data for a "reached" node
type rNode struct {
n Node
from Node
l int // route len
g int // route cost
f int // "g+h", route cost + heuristic estimate
fx int // heap.Fix index
}
 
type openHeap []*rNode // priority queue
 
// Route computes a route from start to end nodes using the A* algorithm.
//
// The algorithm is general A*, where the heuristic is not required to be
// monotonic. If a route exists, the function will find a route regardless
// of the quality of the Heuristic. For an admissiable heuristic, the route
// will be optimal.
func Route(start, end Node) (route []Node, cost int) {
// start node initialized with heuristic
cr := &rNode{n: start, l: 1, f: end.Heuristic(start)}
// maintain a set of reached nodes. start is reached initially
r := map[Node]*rNode{start: cr}
// oh is a heap of nodes "open" for exploration. nodes go on the heap
// when they get an initial or new "g" route distance, and therefore a
// new "f" which serves as priority for exploration.
oh := openHeap{cr}
for len(oh) > 0 {
bestRoute := heap.Pop(&oh).(*rNode)
bestNode := bestRoute.n
if bestNode == end {
// done. prepare return values
cost = bestRoute.g
route = make([]Node, bestRoute.l)
for i := len(route) - 1; i >= 0; i-- {
route[i] = bestRoute.n
bestRoute = r[bestRoute.from]
}
return
}
l := bestRoute.l + 1
for _, to := range bestNode.To() {
// "g" route distance from start
g := bestRoute.g + to.Cost
if alt, ok := r[to.To]; !ok {
// alt being reached for the first time
alt = &rNode{n: to.To, from: bestNode, l: l,
g: g, f: g + end.Heuristic(to.To)}
r[to.To] = alt
heap.Push(&oh, alt)
} else {
if g >= alt.g {
continue // candidate route no better than existing route
}
// it's a better route
// update data and make sure it's on the heap
alt.from = bestNode
alt.l = l
alt.g = g
alt.f = end.Heuristic(alt.n)
if alt.fx < 0 {
heap.Push(&oh, alt)
} else {
heap.Fix(&oh, alt.fx)
}
}
}
}
return nil, 0
}
 
// implement container/heap
func (h openHeap) Len() int { return len(h) }
func (h openHeap) Less(i, j int) bool { return h[i].f < h[j].f }
func (h openHeap) Swap(i, j int) {
h[i], h[j] = h[j], h[i]
h[i].fx = i
h[j].fx = j
}
 
func (p *openHeap) Push(x interface{}) {
h := *p
fx := len(h)
h = append(h, x.(*rNode))
h[fx].fx = fx
*p = h
}
 
func (p *openHeap) Pop() interface{} {
h := *p
last := len(h) - 1
*p = h[:last]
h[last].fx = -1
return h[last]
}
package main
 
import (
"fmt"
 
"astar"
)
 
// rcNode implements the astar.Node interface
type rcNode struct{ r, c int }
 
var barrier = map[rcNode]bool{{2, 4}: true, {2, 5}: true,
{2, 6}: true, {3, 6}: true, {4, 6}: true, {5, 6}: true, {5, 5}: true,
{5, 4}: true, {5, 3}: true, {5, 2}: true, {4, 2}: true, {3, 2}: true}
 
// graph representation is virtual. Arcs from a node are generated when
// requested, but there is no static graph representation.
func (fr rcNode) To() (a []astar.Arc) {
for r := fr.r - 1; r <= fr.r+1; r++ {
for c := fr.c - 1; c <= fr.c+1; c++ {
if (r == fr.r && c == fr.c) || r < 0 || r > 7 || c < 0 || c > 7 {
continue
}
n := rcNode{r, c}
cost := 1
if barrier[n] {
cost = 100
}
a = append(a, astar.Arc{n, cost})
}
}
return a
}
 
// The heuristic computed is max of row distance and column distance.
// This is effectively the cost if there were no barriers.
func (n rcNode) Heuristic(fr astar.Node) int {
dr := n.r - fr.(rcNode).r
if dr < 0 {
dr = -dr
}
dc := n.c - fr.(rcNode).c
if dc < 0 {
dc = -dc
}
if dr > dc {
return dr
}
return dc
}
 
func main() {
route, cost := astar.Route(rcNode{0, 0}, rcNode{7, 7})
fmt.Println("Route:", route)
fmt.Println("Cost:", cost)
}
Output:
Route: [{0 0} {1 1} {2 2} {3 1} {4 1} {5 1} {6 2} {6 3} {6 4} {6 5} {6 6} {7 7}]
Cost: 11

JavaScript[edit]

Animated.
To see how it works on a random map go here

 
var ctx, map, opn = [], clsd = [], start = {x:1, y:1, f:0, g:0},
goal = {x:8, y:8, f:0, g:0}, mw = 10, mh = 10, neighbours, path;
 
function findNeighbour( arr, n ) {
var a;
for( var i = 0; i < arr.length; i++ ) {
a = arr[i];
if( n.x === a.x && n.y === a.y ) return i;
}
return -1;
}
function addNeighbours( cur ) {
var p;
for( var i = 0; i < neighbours.length; i++ ) {
var n = {x: cur.x + neighbours[i].x, y: cur.y + neighbours[i].y, g: 0, h: 0, prt: {x:cur.x, y:cur.y}};
if( map[n.x][n.y] == 1 || findNeighbour( clsd, n ) > -1 ) continue;
n.g = cur.g + neighbours[i].c; n.h = Math.abs( goal.x - n.x ) + Math.abs( goal.y - n.y );
p = findNeighbour( opn, n );
if( p > -1 && opn[p].g + opn[p].h <= n.g + n.h ) continue;
opn.push( n );
}
opn.sort( function( a, b ) {
return ( a.g + a.h ) - ( b.g + b.h ); } );
}
function createPath() {
path = [];
var a, b;
a = clsd.pop();
path.push( a );
while( clsd.length ) {
b = clsd.pop();
if( b.x != a.prt.x || b.y != a.prt.y ) continue;
a = b; path.push( a );
}
}
function solveMap() {
drawMap();
if( opn.length < 1 ) {
document.body.appendChild( document.createElement( "p" ) ).innerHTML = "Impossible!";
return;
}
var cur = opn.splice( 0, 1 )[0];
clsd.push( cur );
if( cur.x == goal.x && cur.y == goal.y ) {
createPath(); drawMap();
return;
}
addNeighbours( cur );
requestAnimationFrame( solveMap );
}
function drawMap() {
ctx.fillStyle = "#ee6"; ctx.fillRect( 0, 0, 200, 200 );
for( var j = 0; j < mh; j++ ) {
for( var i = 0; i < mw; i++ ) {
switch( map[i][j] ) {
case 0: continue;
case 1: ctx.fillStyle = "#990"; break;
case 2: ctx.fillStyle = "#090"; break;
case 3: ctx.fillStyle = "#900"; break;
}
ctx.fillRect( i, j, 1, 1 );
}
}
var a;
if( path.length ) {
var txt = "Path: " + ( path.length - 1 ) + "<br />[";
for( var i = path.length - 1; i > -1; i-- ) {
a = path[i];
ctx.fillStyle = "#999";
ctx.fillRect( a.x, a.y, 1, 1 );
txt += "(" + a.x + ", " + a.y + ") ";
}
document.body.appendChild( document.createElement( "p" ) ).innerHTML = txt + "]";
return;
}
for( var i = 0; i < opn.length; i++ ) {
a = opn[i];
ctx.fillStyle = "#909";
ctx.fillRect( a.x, a.y, 1, 1 );
}
for( var i = 0; i < clsd.length; i++ ) {
a = clsd[i];
ctx.fillStyle = "#009";
ctx.fillRect( a.x, a.y, 1, 1 );
}
}
function createMap() {
map = new Array( mw );
for( var i = 0; i < mw; i++ ) {
map[i] = new Array( mh );
for( var j = 0; j < mh; j++ ) {
if( !i || !j || i == mw - 1 || j == mh - 1 ) map[i][j] = 1;
else map[i][j] = 0;
}
}
map[5][3] = map[6][3] = map[7][3] = map[3][4] = map[7][4] = map[3][5] =
map[7][5] = map[3][6] = map[4][6] = map[5][6] = map[6][6] = map[7][6] = 1;
//map[start.x][start.y] = 2; map[goal.x][goal.y] = 3;
}
function init() {
var canvas = document.createElement( "canvas" );
canvas.width = canvas.height = 200;
ctx = canvas.getContext( "2d" );
ctx.scale( 20, 20 );
document.body.appendChild( canvas );
neighbours = [
{x:1, y:0, c:1}, {x:-1, y:0, c:1}, {x:0, y:1, c:1}, {x:0, y:-1, c:1},
{x:1, y:1, c:1.4}, {x:1, y:-1, c:1.4}, {x:-1, y:1, c:1.4}, {x:-1, y:-1, c:1.4}
];
path = []; createMap(); opn.push( start ); solveMap();
}
 
Output:

Path: 11 [(1, 1) (2, 2) (2, 3) (2, 4) (2, 5) (2, 6) (3, 7) (4, 8) (5, 8) (6, 8) (7, 8) (8, 8) ]


Kotlin[edit]

 
import java.lang.Math.abs
 
typealias GridPosition = Pair<Int, Int>
typealias Barrier = Set<GridPosition>
 
const val MAX_SCORE = 99999999
 
abstract class Grid(private val barriers: List<Barrier>) {
 
open fun heuristicDistance(start: GridPosition, finish: GridPosition): Int {
val dx = abs(start.first - finish.first)
val dy = abs(start.second - finish.second)
return (dx + dy) + (-2) * minOf(dx, dy)
}
 
fun inBarrier(position: GridPosition) = barriers.any { it.contains(position) }
 
abstract fun getNeighbours(position: GridPosition): List<GridPosition>
 
open fun moveCost(from: GridPosition, to: GridPosition) = if (inBarrier(to)) MAX_SCORE else 1
}
 
class SquareGrid(width: Int, height: Int, barriers: List<Barrier>) : Grid(barriers) {
 
private val heightRange: IntRange = (0 until height)
private val widthRange: IntRange = (0 until width)
 
private val validMoves = listOf(Pair(1, 0), Pair(-1, 0), Pair(0, 1), Pair(0, -1), Pair(1, 1), Pair(-1, 1), Pair(1, -1), Pair(-1, -1))
 
override fun getNeighbours(position: GridPosition): List<GridPosition> = validMoves
.map { GridPosition(position.first + it.first, position.second + it.second) }
.filter { inGrid(it) }
 
private fun inGrid(it: GridPosition) = (it.first in widthRange) && (it.second in heightRange)
}
 
 
/**
* Implementation of the A* Search Algorithm to find the optimum path between 2 points on a grid.
*
* The Grid contains the details of the barriers and methods which supply the neighboring vertices and the
* cost of movement between 2 cells. Examples use a standard Grid which allows movement in 8 directions
* (i.e. includes diagonals) but alternative implementation of Grid can be supplied.
*
*/
fun aStarSearch(start: GridPosition, finish: GridPosition, grid: Grid): Pair<List<GridPosition>, Int> {
 
/**
* Use the cameFrom values to Backtrack to the start position to generate the path
*/
fun generatePath(currentPos: GridPosition, cameFrom: Map<GridPosition, GridPosition>): List<GridPosition> {
val path = mutableListOf(currentPos)
var current = currentPos
while (cameFrom.containsKey(current)) {
current = cameFrom.getValue(current)
path.add(0, current)
}
return path.toList()
}
 
val openVertices = mutableSetOf(start)
val closedVertices = mutableSetOf<GridPosition>()
val costFromStart = mutableMapOf(start to 0)
val estimatedTotalCost = mutableMapOf(start to grid.heuristicDistance(start, finish))
 
val cameFrom = mutableMapOf<GridPosition, GridPosition>() // Used to generate path by back tracking
 
while (openVertices.size > 0) {
 
val currentPos = openVertices.minBy { estimatedTotalCost.getValue(it) }!!
 
// Check if we have reached the finish
if (currentPos == finish) {
// Backtrack to generate the most efficient path
val path = generatePath(currentPos, cameFrom)
return Pair(path, estimatedTotalCost.getValue(finish)) // First Route to finish will be optimum route
}
 
// Mark the current vertex as closed
openVertices.remove(currentPos)
closedVertices.add(currentPos)
 
grid.getNeighbours(currentPos)
.filterNot { closedVertices.contains(it) } // Exclude previous visited vertices
.forEach { neighbour ->
val score = costFromStart.getValue(currentPos) + grid.moveCost(currentPos, neighbour)
if (score < costFromStart.getOrDefault(neighbour, MAX_SCORE)) {
if (!openVertices.contains(neighbour)) {
openVertices.add(neighbour)
}
cameFrom.put(neighbour, currentPos)
costFromStart.put(neighbour, score)
estimatedTotalCost.put(neighbour, score + grid.heuristicDistance(neighbour, finish))
}
}
 
}
 
throw IllegalArgumentException("No Path from Start $start to Finish $finish")
}
 
fun main(args: Array<String>) {
 
val barriers = listOf(setOf( Pair(2,4), Pair(2,5), Pair(2,6), Pair(3,6), Pair(4,6), Pair(5,6), Pair(5,5),
Pair(5,4), Pair(5,3), Pair(5,2), Pair(4,2), Pair(3,2)))
 
val (path, cost) = aStarSearch(GridPosition(0,0), GridPosition(7,7), SquareGrid(8,8, barriers))
 
println("Cost: $cost Path: $path")
}
 
Output:

Cost: 11 Path: [(0, 0), (1, 1), (2, 2), (3, 1), (4, 1), (5, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (7, 7)]


Lua[edit]

 
-- QUEUE -----------------------------------------------------------------------
Queue = {}
function Queue:new()
local q = {}
self.__index = self
return setmetatable( q, self )
end
function Queue:push( v )
table.insert( self, v )
end
function Queue:pop()
return table.remove( self, 1 )
end
function Queue:getSmallestF()
local s, i = nil, 2
while( self[i] ~= nil and self[1] ~= nil ) do
if self[i]:F() < self[1]:F() then
s = self[1]
self[1] = self[i]
self[i] = s
end
i = i + 1
end
return self:pop()
end
 
-- LIST ------------------------------------------------------------------------
List = {}
function List:new()
local l = {}
self.__index = self
return setmetatable( l, self )
end
function List:push( v )
table.insert( self, v )
end
function List:pop()
return table.remove( self )
end
 
-- POINT -----------------------------------------------------------------------
Point = {}
function Point:new()
local p = { y = 0, x = 0 }
self.__index = self
return setmetatable( p, self )
end
function Point:set( x, y )
self.x, self.y = x, y
end
function Point:equals( o )
return (o.x == self.x and o.y == self.y)
end
function Point:print()
print( self.x, self.y )
end
 
-- NODE ------------------------------------------------------------------------
Node = {}
function Node:new()
local n = { pos = Point:new(), parent = Point:new(), dist = 0, cost = 0 }
self.__index = self
return setmetatable( n, self )
end
function Node:set( pt, parent, dist, cost )
self.pos = pt
self.parent = parent
self.dist = dist
self.cost = cost
end
function Node:F()
return ( self.dist + self.cost )
end
 
-- A-STAR ----------------------------------------------------------------------
local nbours = {
{ 1, 0, 1 }, { 0, 1, 1 }, { 1, 1, 1.4 }, { 1, -1, 1.4 },
{ -1, -1, 1.4 }, { -1, 1, 1.4 }, { 0, -1, 1 }, { -1, 0, 1 }
}
local map = {
1,1,1,1,1,1,1,1,1,1,
1,0,0,0,0,0,0,0,0,1,
1,0,0,0,0,0,0,0,0,1,
1,0,0,0,0,1,1,1,0,1,
1,0,0,1,0,0,0,1,0,1,
1,0,0,1,0,0,0,1,0,1,
1,0,0,1,1,1,1,1,0,1,
1,0,0,0,0,0,0,0,0,1,
1,0,0,0,0,0,0,0,0,1,
1,1,1,1,1,1,1,1,1,1
}
local open, closed, start, goal,
mapW, mapH = Queue:new(), List:new(), Point:new(), Point:new(), 10, 10
start:set( 2, 2 ); goal:set( 9, 9 )
 
function hasNode( arr, pos )
for nx, val in ipairs( arr ) do
if val.pos:equals( pos ) then
return nx
end
end
return -1
end
function isValid( pos )
return pos.x > 0 and pos.x <= mapW
and pos.y > 0 and pos.y <= mapH
and map[pos.x + mapW * pos.y - mapW] == 0
end
function calcDist( p1 )
local x, y = goal.x - p1.x, goal.y - p1.y
return math.abs( x ) + math.abs( y )
end
function addToOpen( node )
local nx
for n = 1, 8 do
nNode = Node:new()
nNode.parent:set( node.pos.x, node.pos.y )
nNode.pos:set( node.pos.x + nbours[n][1], node.pos.y + nbours[n][2] )
nNode.cost = node.cost + nbours[n][3]
nNode.dist = calcDist( nNode.pos )
 
if isValid( nNode.pos ) then
if nNode.pos:equals( goal ) then
closed:push( nNode )
return true
end
nx = hasNode( closed, nNode.pos )
if nx < 0 then
nx = hasNode( open, nNode.pos )
if( nx < 0 ) or ( nx > 0 and nNode:F() < open[nx]:F() ) then
if( nx > 0 ) then
table.remove( open, nx )
end
open:push( nNode )
else
nNode = nil
end
end
end
end
return false
end
function makePath()
local i, l = #closed, List:new()
local node, parent = closed[i], nil
 
l:push( node.pos )
parent = node.parent
while( i > 0 ) do
i = i - 1
node = closed[i]
if node ~= nil and node.pos:equals( parent ) then
l:push( node.pos )
parent = node.parent
end
end
print( string.format( "Cost: %d", #l - 1 ) )
io.write( "Path: " )
for i = #l, 1, -1 do
map[l[i].x + mapW * l[i].y - mapW] = 2
io.write( string.format( "(%d, %d) ", l[i].x, l[i].y ) )
end
print( "" )
end
function aStar()
local n = Node:new()
n.dist = calcDist( start )
n.pos:set( start.x, start.y )
open:push( n )
while( true ) do
local node = open:getSmallestF()
if node == nil then break end
closed:push( node )
if addToOpen( node ) == true then
makePath()
return true
end
end
return false
end
-- ENTRY POINT -----------------------------------------------------------------
if true == aStar() then
local m
for j = 1, mapH do
for i = 1, mapW do
m = map[i + mapW * j - mapW]
if m == 0 then
io.write( "." )
elseif m == 1 then
io.write( string.char(0xdb) )
else
io.write( "x" )
end
end
io.write( "\n" )
end
else
print( "can not find a path!" )
end
 
Output:
Cost: 11
Path: (2, 2) (3, 3) (3, 4) (3, 5) (3, 6) (3, 7) (4, 8) (5, 9) (6, 9) (7, 9) (8, 9) (9, 9)
██████████
█x.......█
█.x......█
█.x..███.█
█.x█...█.█
█.x█...█.█
█.x█████.█
█..x.....█
█...xxxxx█
██████████

Phix[edit]

rows and columns are numbered 1 to 8. start position is {1,1} and end position is {8,8}. barriers are simply avoided, rather than costed at 100. Note that the 23 visited nodes does not count walls, but with them this algorithm exactly matches the 35 of Racket.

sequence grid = split("""
x:::::::
::::::::
::::###:
::#:::#:
::#:::#:
::#####:
::::::::
::::::::
""",'\n')
 
constant permitted = {{-1,-1},{0,-1},{1,-1},
{-1, 0}, {1, 0},
{-1, 1},{0,+1},{1,+1}}
 
sequence key = {7,0}, -- chebyshev, cost
moves = {{1,1}},
data = {moves}
setd(key,data)
bool found = false
integer count = 0
while not found do
if dict_size()=0 then ?"impossible" exit end if
key = getd_partial_key(0)
data = getd(key)
moves = data[$]
if length(data)=1 then
deld(key)
else
data = data[1..$-1]
putd(key,data)
end if
count += 1
for i=1 to length(permitted) do
sequence newpos = sq_add(moves[$],permitted[i])
integer {nx,ny} = newpos
if nx>=1 and nx<=8
and ny>=1 and ny<=8
and grid[nx,ny] = ':' then -- (unvisited)
grid[nx,ny] = '.'
sequence newkey = {max(8-nx,8-ny),key[2]+1},
newmoves = append(moves,newpos)
if newpos = {8,8} then
moves = newmoves
found = true
exit
end if
integer k = getd_index(newkey)
if k=0 then
data = {newmoves}
else
data = append(getd_by_index(k),newmoves)
end if
putd(newkey,data)
end if
end for
end while
if found then
printf(1,"visited %d nodes\ncost:%d\npath:",{count,length(moves)-1})
 ?moves
for i=1 to length(moves) do
integer {x,y} = moves[i]
grid[x,y] = 'x'
end for
puts(1,join(grid,'\n'))
end if
Output:
visited 23 nodes
cost:11
path:{{1,1},{2,2},{3,3},{4,2},{5,2},{6,2},{7,3},{8,4},{8,5},{8,6},{8,7},{8,8}}
x......:
.x.....:
..x.###:
.x#...#:
.x#...#:
.x#####:
..x.....
:..xxxxx

Python[edit]

from __future__ import print_function
import matplotlib.pyplot as plt
 
class AStarGraph(object):
#Define a class board like grid with two barriers
 
def __init__(self):
self.barriers = []
self.barriers.append([(2,4),(2,5),(2,6),(3,6),(4,6),(5,6),(5,5),(5,4),(5,3),(5,2),(4,2),(3,2)])
 
def heuristic(self, start, goal):
#Use Chebyshev distance heuristic if we can move one square either
#adjacent or diagonal
D = 1
D2 = 1
dx = abs(start[0] - goal[0])
dy = abs(start[1] - goal[1])
return D * (dx + dy) + (D2 - 2 * D) * min(dx, dy)
 
def get_vertex_neighbours(self, pos):
n = []
#Moves allow link a chess king
for dx, dy in [(1,0),(-1,0),(0,1),(0,-1),(1,1),(-1,1),(1,-1),(-1,-1)]:
x2 = pos[0] + dx
y2 = pos[1] + dy
if x2 < 0 or x2 > 7 or y2 < 0 or y2 > 7:
continue
n.append((x2, y2))
return n
 
def move_cost(self, a, b):
for barrier in self.barriers:
if b in barrier:
return 100 #Extremely high cost to enter barrier squares
return 1 #Normal movement cost
 
def AStarSearch(start, end, graph):
 
G = {} #Actual movement cost to each position from the start position
F = {} #Estimated movement cost of start to end going via this position
 
#Initialize starting values
G[start] = 0
F[start] = graph.heuristic(start, end)
 
closedVertices = set()
openVertices = set([start])
cameFrom = {}
 
while len(openVertices) > 0:
#Get the vertex in the open list with the lowest F score
current = None
currentFscore = None
for pos in openVertices:
if current is None or F[pos] < currentFscore:
currentFscore = F[pos]
current = pos
 
#Check if we have reached the goal
if current == end:
#Retrace our route backward
path = [current]
while current in cameFrom:
current = cameFrom[current]
path.append(current)
path.reverse()
return path, F[end] #Done!
 
#Mark the current vertex as closed
openVertices.remove(current)
closedVertices.add(current)
 
#Update scores for vertices near the current position
for neighbour in graph.get_vertex_neighbours(current):
if neighbour in closedVertices:
continue #We have already processed this node exhaustively
candidateG = G[current] + graph.move_cost(current, neighbour)
 
if neighbour not in openVertices:
openVertices.add(neighbour) #Discovered a new vertex
elif candidateG >= G[neighbour]:
continue #This G score is worse than previously found
 
#Adopt this G score
cameFrom[neighbour] = current
G[neighbour] = candidateG
H = graph.heuristic(neighbour, end)
F[neighbour] = G[neighbour] + H
 
raise RuntimeError("A* failed to find a solution")
 
if __name__=="__main__":
graph = AStarGraph()
result, cost = AStarSearch((0,0), (7,7), graph)
print ("route", result)
print ("cost", cost)
plt.plot([v[0] for v in result], [v[1] for v in result])
for barrier in graph.barriers:
plt.plot([v[0] for v in barrier], [v[1] for v in barrier])
plt.xlim(-1,8)
plt.ylim(-1,8)
plt.show()
Output:
route [(0, 0), (1, 1), (2, 2), (3, 1), (4, 1), (5, 1), (6, 2), (7, 3), (6, 4), (7, 5), (6, 6), (7, 7)]
cost 11

Racket[edit]

This code is lifted from: this blog post. Read it, it's very good.

#lang scribble/lp
@(chunk
<graph-sig>
(define-signature graph^
(node? edge? node-edges edge-src edge-cost edge-dest)))
 
@(chunk
<map-generation>
(define (make-map N)
 ;; Jay's random algorithm
 ;; (build-matrix N N (λ (x y) (random 3)))
 ;; RC version
(matrix [[0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0]
[0 0 0 0 1 1 1 0]
[0 0 1 0 0 0 1 0]
[0 0 1 0 0 0 1 0]
[0 0 1 1 1 1 1 0]
[0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0]])))
 
@(chunk
<map-graph-rep>
(struct map-node (M x y) #:transparent)
(struct map-edge (src dx dy dest)))
 
@(chunk
<map-graph-cost>
(define (edge-cost e)
(match-define (map-edge _ _ _ (map-node M x y)) e)
(match (matrix-ref M x y)
[0 1]
[1 100]
[2 1000])))
 
@(chunk
<map-graph-edges>
(define (node-edges n)
(match-define (map-node M x y) n)
(append*
(for*/list ([dx (in-list '(1 0 -1))]
[dy (in-list '(1 0 -1))]
#:when
(and (not (and (zero? dx) (zero? dy)))
 ;; RC -- allowed to move diagonally, so not this clause
 ;;(or (zero? dx) (zero? dy))
))
(cond
[(and (<= 0 (+ dx x) (sub1 (matrix-num-cols M)))
(<= 0 (+ dy y) (sub1 (matrix-num-rows M))))
(define dest (map-node M (+ dx x) (+ dy y)))
(list (map-edge n dx dy dest))]
[else
empty])))))
 
@(chunk
<a-star>
(define (A* [email protected] initial node-cost)
(define-values/invoke-unit [email protected] (import) (export graph^))
(define count 0)
<a-star-setup>
 
(begin0
(let/ec esc
<a-star-loop>
#f)
 
(printf "visited ~a nodes\n" count))))
 
@(chunk
<a-star-setup>
<a-star-setup-closed>
<a-star-setup-open>)
 
@(chunk
<a-star-setup-closed>
(define node->best-path (make-hash))
(define node->best-path-cost (make-hash))
(hash-set! node->best-path initial empty)
(hash-set! node->best-path-cost initial 0))
 
@(chunk
<a-star-setup-open>
(define (node-total-estimate-cost n)
(+ (node-cost n) (hash-ref node->best-path-cost n)))
(define (node-cmp x y)
(<= (node-total-estimate-cost x)
(node-total-estimate-cost y)))
(define open-set (make-heap node-cmp))
(heap-add! open-set initial))
 
@(chunk
<a-star-loop>
(for ([x (in-heap/consume! open-set)])
(set! count (add1 count))
<a-star-loop-body>))
 
@(chunk
<a-star-loop-stop?>
(define h-x (node-cost x))
(define path-x (hash-ref node->best-path x))
 
(when (zero? h-x)
(esc (reverse path-x))))
 
@(chunk
<a-star-loop-body>
<a-star-loop-stop?>
 
(define g-x (hash-ref node->best-path-cost x))
(for ([x->y (in-list (node-edges x))])
(define y (edge-dest x->y))
<a-star-loop-per-neighbor>))
 
@(chunk
<a-star-loop-per-neighbor>
(define new-g-y (+ g-x (edge-cost x->y)))
(define old-g-y
(hash-ref node->best-path-cost y +inf.0))
(when (< new-g-y old-g-y)
(hash-set! node->best-path-cost y new-g-y)
(hash-set! node->best-path y (cons x->y path-x))
(heap-add! open-set y)))
 
@(chunk
<map-display>
(define map-scale 15)
(define (type-color ty)
(match ty
[0 "yellow"]
[1 "green"]
[2 "red"]))
(define (cell-square ty)
(square map-scale "solid" (type-color ty)))
(define (row-image M row)
(apply beside
(for/list ([col (in-range (matrix-num-cols M))])
(cell-square (matrix-ref M row col)))))
(define (map-image M)
(apply above
(for/list ([row (in-range (matrix-num-rows M))])
(row-image M row)))))
 
@(chunk
<path-display-line>
(define (edge-image-on e i)
(match-define (map-edge (map-node _ sx sy) _ _ (map-node _ dx dy)) e)
(add-line i
(* (+ sy 0.5) map-scale) (* (+ sx 0.5) map-scale)
(* (+ dy 0.5) map-scale) (* (+ dx 0.5) map-scale)
"black")))
 
@(chunk
<path-display>
(define (path-image M path)
(foldr edge-image-on (map-image M) path)))
 
@(chunk
<map-graph>
(define-unit [email protected]
(import) (export graph^)
 
(define node? map-node?)
(define edge? map-edge?)
(define edge-src map-edge-src)
(define edge-dest map-edge-dest)
 
<map-graph-cost>
<map-graph-edges>))
 
@(chunk
<map-node-cost>
(define ((make-node-cost GX GY) n)
(match-define (map-node M x y) n)
 ;; Jay's
#;(+ (abs (- x GX))
(abs (- y GY)))
 ;; RC -- diagonal movement
(max (abs (- x GX))
(abs (- y GY)))))
 
@(chunk
<map-example>
(define N 8)
(define random-M
(make-map N))
(define random-path
(time
(A* [email protected]
(map-node random-M 0 0)
(make-node-cost (sub1 N) (sub1 N))))))
 
@(chunk
<*>
(require rackunit
math/matrix
racket/unit
racket/match
racket/list
data/heap
2htdp/image
racket/runtime-path)
 
<graph-sig>
 
<map-generation>
<map-graph-rep>
<map-graph>
 
<a-star>
 
<map-node-cost>
<map-example>
(printf "path is ~a long\n" (length random-path))
(printf "path is: ~a\n" (map (match-lambda
[(map-edge src dx dy dest)
(cons dx dy)])
random-path))
 
<map-display>
<path-display-line>
<path-display>
 
(path-image random-M random-path))
Output:
visited 35 nodes
cpu time: 94 real time: 97 gc time: 15
path is 11 long
path is: ((1 . 1) (1 . 1) (1 . -1) (1 . 0) (1 . 0) (1 . 1) (1 . 1) (0 . 1) (-1 . 1) (1 . 1) (0 . 1))
.

A diagram is also output, but you'll need to run this in DrRacket to see it.

REXX[edit]

/*REXX program solves the    A*   search problem   for a  (general)   NxN   grid.       */
parse arg N sCol sRow . /*obtain optional arguments from the CL*/
if N=='' | N=="," then N=8 /*No grid size specified? Use default.*/
if sCol=='' | sCol=="," then sCol=1 /*No starting column given? " " */
if sRow=='' | sRow=="," then sRow=1 /* " " row " " " */
beg= '─0─' /*mark the start of the journey in grid*/
o.=.; p.=0 /*list of optimum start journey starts.*/
times=0 /*cntr/pos for number of optimizations.*/
Pc = ' 1 1 0 0 1 -1 -1 -1 ' /*the possible column moves for a path.*/
Pr = ' 1 0 1 -1 -1 0 1 -1 ' /* " " row " " " " */
Pcm=words(Pc) /* [↑] optimized for moving right&down*/
$.=1e6; OK=0; min$=$. /*# possible directions; cost; solution*/
@Aa= " A* search algorithm on" /*a handy─dandy literal for the SAYs. */
flasher= '@. $. min$ N o. p. Pc. Pcm Pr. sCol sRow times' /*a literal list for EXPOSE.*/
call path 0 /*find a possible solution for the grid*/
@NxN= 'a ' N"x"N ' grid' /*a literal used for a SAY statement.*/
if OK then say 'A solution for the' @Aa @NxN "with a score of " @.N.N':'
else say 'No' @Aa "solution for" @NxN'.'
call show 1 /*invoke subroutine to display the grid*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
@: parse arg x,y,aChar; if arg()==3 then @.x.y=aChar; return @.x.y
@p: parse arg x,y; if datatype(@.x.y, 'W') then return @.x.y<m-1; return 0
/*──────────────────────────────────────────────────────────────────────────────────────*/
barr: $=2.4 2.5 2.6 3.6 4.6 5.6 5.5 5.4 5.3 5.2 4.2 3.2 /*locations of barriers on grid*/
do b=1 for words($); _=word($, b); parse var _ c '.' r; call @ c+1,r+1,"█"
end /*b*/; return
/*──────────────────────────────────────────────────────────────────────────────────────*/
move: procedure expose (flasher); parse arg m,col,row /*obtain move,col,row.*/
do t=1 for Pcm; nc=col + Pc.t; nr=row + Pr.t /*a new path position. */
if @.nc.nr==. then do; if opti() then iterate /*Costlier path? Next.*/
@.nc.nr=m; p.1.m=nc nr /*Empty? A legal path.*/
p.pcm.m=nr nc-1 /*used for a fast path.*/
if nc==N then if nr==N then return 1 /*last move? */
if move(m + 1, nc, nr) then return 1 /* " " */
@.nc.nr=. /*undo the above move. */
end /*try a different move.*/
end /*t*/ /* [↑] all moves tried*/
return 0 /*path isn't possible. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
opti: ncm=nc-1; nrm=nr-1; if @p(ncm, nrm) then return 1
if @p(ncm, nr ) then return 1
if @p(nc, nrm) then return 1
ncp=nc+1; nrp=nr+1; if @p(ncp, nr ) then return 1
if @p(ncp, nrm) then return 1
if @p(nc, nrp) then return 1
if @p(ncm, nrp) then return 1
if @p(ncp, nrp) then return 1; return 0
/*──────────────────────────────────────────────────────────────────────────────────────*/
path: parse arg z; t=times /*initial move can only be one of eight*/
do #=1 for Pcm; @.= /*optimize for each degree of movement.*/
if z\==0 then if #\==z then iterate /*This a particular low─cost request ? */
do c=1 for N; do r=1 for N; @.c.r=.; end /*r*/
end /*c*/
iCol=sCol; iRow=sRow; @.sCol.sRow= beg /*all path's initial starting position*/
call barr /*place the barriers on the grid. */
Pco=subword(Pc Pc, #, Pcm); Pro=subword(Pr Pr, #, Pcm)
parse var Pco Pc.1 Pc.2 Pc.3 Pc.4 Pc.5 Pc.6 Pc.7 Pc.8 /*possible directions.*/
parse var Pro Pr.1 Pr.2 Pr.3 Pr.4 Pr.5 Pr.6 Pr.7 Pr.8 /* " " */
do o=1 for times; parse var o.o c r; @.c.r=o; iRow=r; iCol=c
end /*o*/
fp=move(1+times, iCol, iRow); [email protected].N\==. & fp
if sol then do; $.#[email protected].N.N /*Found a solution? Remember the cost.*/
OK=1; min$=min(min$, $.#)
end
end /*#*/
wp=1e7; wg=0; do g=1 for Pcm; if $.g<wp & $.g>0 & t\=2 then do; wg=g; wp=$.g; end
end /*g*/ /* [↑] find minimum non-zero path cost*/
if wg==0 then wg=8 /*Not found? Then use last cost found.*/
times=times + 1 /*bump # times a marker has been placed*/
o.times= p.wg.times /*remember this move location for PATH.*/
if times<4 then call path 0 /*only do memoization for first 3 moves*/
return
/*──────────────────────────────────────────────────────────────────────────────────────*/
show: ind=left('', 9 * (n<18) ); say /*the indentation of the displayed grid*/
_=substr(copies("┼───", N),2); say ind translate('┌'_"┐", '┬', "┼") /*grid top.*/
/* [↓] build a display for the grid. */
do c=1 for N; if c\==1 & arg(1) then say ind '├'_"┤"; [email protected].
do r=1 for N; [email protected].c.r; if c ==N & r==N & ?\==. then ?='end'; L=L"│"center(?, 3)
end /*r*/ /*done with rank of the grid. */
say ind translate(L'│', , .) /*display a " " " " */
end /*c*/ /*a 19x19 grid can be shown 80 columns.*/
say ind translate('└'_"┘",'┴',"┼"); return /*display the very bottom of the grid. */
output   when using the default input:
A solution for the  A*  search algorithm on a  8x8  grid with a score of  11:

          ┌───┬───┬───┬───┬───┬───┬───┬───┐
          │─0─│   │   │   │   │   │   │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │ 1 │   │   │   │   │   │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │   │ 2 │   │ █ │ █ │ █ │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │ 3 │ █ │   │   │   │ █ │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │ 4 │ █ │   │   │   │ █ │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │ 5 │ █ │ █ │ █ │ █ │ █ │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │   │ 6 │   │   │   │   │   │
          ├───┼───┼───┼───┼───┼───┼───┼───┤
          │   │   │   │ 7 │ 8 │ 9 │10 │end│
          └───┴───┴───┴───┴───┴───┴───┴───┘

SequenceL[edit]

 
import <Utilities/Set.sl>;
import <Utilities/Math.sl>;
import <Utilities/Sequence.sl>;
 
Point ::= (x : int, y : int);
 
State ::= (open : Point(1), closed : Point(1), cameFrom : Point(2), estimate : int(2), actual : int(2));
 
allNeighbors := [(x : -1, y : -1), (x : 1, y : -1), (x : -1, y : 1), (x : 1, y : 1),
(x : 0, y : -1), (x : -1, y : 0), (x : 0, y : 1), (x : 1, y : 0)];
 
defaultBarriers := [(x : 3, y : 5),(x : 3, y : 6),(x : 3, y : 7),(x : 4, y : 7),
(x : 5, y : 7),(x : 6, y : 7),(x : 6, y : 6),(x : 6, y : 5),(x : 6, y : 4),
(x : 6, y : 3),(x : 5, y : 3),(x : 4, y : 3)];
 
defaultWidth := 8;
defaultHeight := 8;
 
main(args(2)) := aStar(defaultWidth, defaultHeight, defaultBarriers, (x : 1, y : 1), (x : defaultWidth, y : defaultHeight));
 
aStar(width, height, barriers(1), start, end) :=
let
newEstimate[i,j] := heuristic(start, end) when i = start.x and j = start.y else 0
foreach i within 1...width, j within 1 ... height;
newActual[i,j] := 0 foreach i within 1...width, j within 1...height;
newCameFrom[i,j] := (x : 0, y : 0) foreach i within 1...width, j within 1...height;
 
searchResults := search((open : [start], closed : [], estimate : newEstimate, actual : newActual, cameFrom : newCameFrom), barriers, end);
shortestPath := path(searchResults.cameFrom, start, end) ++ [end];
in
"No Path Found" when size(searchResults.open) = 0 else
"Path: " ++ toString(shortestPath) ++ "\nCost:" ++
toString(searchResults.actual[end.x, end.y]) ++ "\nMap:\n" ++ join(appendNT(drawMap(barriers,shortestPath,width, height),"\n"));
 
path(cameFrom(2), start, current) :=
let
next := cameFrom[current.x, current.y];
in
[] when current = start else
path(cameFrom, start, next) ++ [next];
 
drawMap(barriers(1), path(1), width, height)[i,j] :=
'#' when elementOf((x:i, y:j), barriers) else
'X' when elementOf((x:i, y:j), path) else
'.' foreach i within 1 ... width, j within 1 ... height;
 
search(state, barriers(1), end) :=
let
nLocation := smallestEstimate(state.open, state.estimate, 2, 1, state.estimate[state.open[1].x, state.open[1].y]);
n := state.open[nLocation];
neighbors := createNeighbors(n, allNeighbors, size(state.actual), size(state.actual[1]));
startState := (open : state.open[1...nLocation-1] ++ state.open[nLocation+1 ... size(state.open)], closed : state.closed ++ [n], cameFrom : state.cameFrom,
estimate : state.estimate, actual : state.actual);
newState := findOpenNeighbors(n, startState, barriers, end, neighbors);
in
state when size(state.open) = 0 else
state when n = end else
search(newState, barriers, end);
 
smallestEstimate(open(1), estimate(2), index, minIndex, minEstimate) :=
let newEstimate := estimate[open[index].x, open[index].y]; in
minIndex when index > size(open) else
smallestEstimate(open, estimate, index + 1, minIndex, minEstimate) when newEstimate > minEstimate else
smallestEstimate(open, estimate, index + 1, index, newEstimate);
 
findOpenNeighbors(n, state, barriers(1), end, neighbors(1)) :=
let
neighbor := head(neighbors);
cost := 1 + n.cost;
candidate := state.actual[n.x, n.y] + calculateCost(barriers, n, neighbor);
in
state when size(neighbors) = 0 else
findOpenNeighbors(n, state, barriers, end, tail(neighbors)) when elementOf(neighbor, state.closed) else
findOpenNeighbors(n, state, barriers, end, tail(neighbors)) when elementOf(neighbor, state.open) and candidate >= state.actual[neighbor.x, neighbor.y] else
findOpenNeighbors(n, (open : state.open ++ [neighbor], closed : state.closed,
cameFrom : setMap(state.cameFrom, neighbor, n),
estimate : setMap(state.estimate, neighbor, candidate + heuristic(neighbor, end)),
actual : setMap(state.actual, neighbor, candidate)),
barriers, end, tail(neighbors));
 
createNeighbors(n, p, w, h) :=
let
x := n.x + p.x;
y := n.y + p.y;
in
(x : x, y : y) when x >= 1 and x <= w and y >= 1 and y <= h;
 
calculateCost(barriers(1), start, end) := 100 when elementOf(end, barriers) else 1;
 
heuristic(start, end) :=
let
dx := abs(start.x - end.x);
dy := abs(start.y - end.y);
in
(dx + dy) - min(dx, dy);
 
setMap(map(2), point, value)[i,j] :=
value when point.x = i and point.y = j else
map[i,j] foreach i within 1 ... size(map), j within 1 ... size(map[1]);
 
Output  
Path: [(x:1,y:1),(x:2,y:2),(x:3,y:3),(x:4,y:2),(x:5,y:2),(x:6,y:2),(x:7,y:3),(x:7,y:4),(x:7,y:5),(x:7,y:6),(x:7,y:7),(x:8,y:8)]
Cost:11
Map:
X.......
.X......
..X.###.
.X#...#.
.X#...#.
.X#####.
..XXXXX.
.......X

Sidef[edit]

Translation of: Python
class AStarGraph {
 
has barriers = [
[2,4],[2,5],[2,6],[3,6],[4,6],[5,6],[5,5],[5,4],[5,3],[5,2],[4,2],[3,2]
]
 
method heuristic(start, goal) {
var (D1 = 1, D2 = 1)
var dx = abs(start[0] - goal[0])
var dy = abs(start[1] - goal[1])
(D1 * (dx + dy)) + ((D2 - 2*D1) * Math.min(dx, dy))
}
 
method get_vertex_neighbours(pos) {
gather {
for dx, dy in [[1,0],[-1,0],[0,1],[0,-1],[1,1],[-1,1],[1,-1],[-1,-1]] {
var x2 = (pos[0] + dx)
var y2 = (pos[1] + dy)
(x2<0 || x2>7 || y2<0 || y2>7) && next
take([x2, y2])
}
}
}
 
method move_cost(_a, b) {
barriers.contains(b) ? 100 : 1
}
}
 
func AStarSearch(start, end, graph) {
 
var G = Hash()
var F = Hash()
 
G{start} = 0
F{start} = graph.heuristic(start, end)
 
var closedVertices = []
var openVertices = [start]
var cameFrom = Hash()
 
while (openVertices) {
 
var current = nil
var currentFscore = Inf
 
for pos in openVertices {
if (F{pos} < currentFscore) {
currentFscore = F{pos}
current = pos
}
}
 
if (current == end) {
var path = [current]
while (cameFrom.contains(current)) {
current = cameFrom{current}
path << current
}
path.flip!
return (path, F{end})
}
 
openVertices.remove(current)
closedVertices.append(current)
 
for neighbour in (graph.get_vertex_neighbours(current)) {
if (closedVertices.contains(neighbour)) {
next
}
var candidateG = (G{current} + graph.move_cost(current, neighbour))
 
if (!openVertices.contains(neighbour)) {
openVertices.append(neighbour)
}
elsif (candidateG >= G{neighbour}) {
next
}
 
cameFrom{neighbour} = current
G{neighbour} = candidateG
var H = graph.heuristic(neighbour, end)
F{neighbour} = (G{neighbour} + H)
}
}
 
die "A* failed to find a solution"
}
 
var graph = AStarGraph()
var (route, cost) = AStarSearch([0,0], [7,7], graph)
 
var w = 10
var h = 10
 
var grid = h.of { w.of { "." } }
for y in (^h) { grid[y][0] = "█"; grid[y][-1] = "█" }
for x in (^w) { grid[0][x] = "█"; grid[-1][x] = "█" }
 
for x,y in (graph.barriers) { grid[x+1][y+1] = "█" }
for x,y in (route) { grid[x+1][y+1] = "x" }
 
grid.each { .join.say }
 
say "Path cost #{cost}: #{route}"
Output:
██████████
█x.......█
█.x......█
█..x.███.█
█.x█...█.█
█.x█...█.█
█.x█████.█
█..xxxxx.█
█.......x█
██████████
Path cost 11: [[0, 0], [1, 1], [2, 2], [3, 1], [4, 1], [5, 1], [6, 2], [6, 3], [6, 4], [6, 5], [6, 6], [7, 7]]

zkl[edit]

Translation of: Python
   // we use strings as hash keys: (x,y)-->"x,y", keys are a single pair
fcn toKey(xy){ xy.concat(",") }
 
fcn AStarSearch(start,end,graph){
G:=Dictionary(); # Actual movement cost to each position from the start position
F:=Dictionary(); # Estimated movement cost of start to end going via this position
#Initialize starting values
kstart:=toKey(start);
G[kstart]=0;
F[kstart]=graph.heuristic(start,end);
closedVertices,openVertices,cameFrom := List(),List(start),Dictionary();
 
while(openVertices){
# Get the vertex in the open list with the lowest F score
current,currentFscore := Void, Void;
foreach pos in (openVertices){
kpos:=toKey(pos);
if(current==Void or F[kpos]<currentFscore)
currentFscore,current = F[kpos],pos;
 
# Check if we have reached the goal
if(current==end){ # Yes! Retrace our route backward
path,kcurrent := List(current),toKey(current);
while(current = cameFrom.find(kcurrent)){
path.append(current);
kcurrent=toKey(current);
}
return(path.reverse(),F[toKey(end)]) # Done!
}
 
# Mark the current vertex as closed
openVertices.remove(current);
if(not closedVertices.holds(current)) closedVertices.append(current);
 
# Update scores for vertices near the current position
foreach neighbor in (graph.get_vertex_neighbors(current)){
if(closedVertices.holds(neighbor))
continue; # We have already processed this node exhaustively
kneighbor:=toKey(neighbor);
candidateG:=G[toKey(current)] + graph.move_cost(current, neighbor);
 
if(not openVertices.holds(neighbor))
openVertices.append(neighbor); # Discovered a new vertex
else if(candidateG>=G[kneighbor])
continue; # This G score is worse than previously found
 
# Adopt this G score
cameFrom[kneighbor]=current;
G[kneighbor]=candidateG;
F[kneighbor]=G[kneighbor] + graph.heuristic(neighbor,end);
}
}
} // while
throw(Exception.AssertionError("A* failed to find a solution"));
}
class [static] AStarGraph{   # Define a class board like grid with barriers
var [const] barriers =
T( T(3,2),T(4,2),T(5,2), // T is RO List
T(5,3),
T(2,4), T(5,4),
T(2,5), T(5,5),
T(2,6),T(3,6),T(4,6),T(5,6) );
fcn heuristic(start,goal){ // (x,y),(x,y)
# Use Chebyshev distance heuristic if we can move one square either
# adjacent or diagonal
D,D2,dx,dy := 1,1, (start[0] - goal[0]).abs(), (start[1] - goal[1]).abs();
D*(dx + dy) + (D2 - 2*D)*dx.min(dy);
}
fcn get_vertex_neighbors([(x,y)]){ # Move like a chess king
var moves=Walker.cproduct([-1..1],[-1..1]).walk(); // 8 moves + (0,0)
moves.pump(List,'wrap([(dx,dy)]){
x2,y2 := x + dx, y + dy;
if((dx==dy==0) or x2 < 0 or x2 > 7 or y2 < 0 or y2 > 7) Void.Skip;
else T(x2,y2);
})
}
fcn move_cost(a,b){ // ( (x,y),(x,y) )
if(barriers.holds(b))
return(100); # Extremely high cost to enter barrier squares
1 # Normal movement cost
}
}
graph:=AStarGraph;
route,cost := AStarSearch(T(0,0), T(7,7), graph);
println("Route: ", route.apply(fcn(xy){ String("(",toKey(xy),")") }).concat(","));
println("Cost: ", cost);
 
// graph the solution:
grid:=(10).pump(List,List.createLong(10," ").copy);
foreach x,y in (graph.barriers){ grid[x][y]="#" }
foreach x,y in (route){ grid[x][y]="+" }
grid[0][0] = "S"; grid[7][7] = "E";
foreach line in (grid){ println(line.concat()) }
Output:
Route: (0,0),(1,1),(2,2),(3,1),(4,0),(5,1),(6,2),(7,3),(7,4),(7,5),(7,6),(7,7)
Cost: 11
S         
 +        
  + ###   
 +#   #   
+ #   #   
 +#####   
  +       
   ++++E