A星算法
Posted maxwell_xu
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了A星算法相关的知识,希望对你有一定的参考价值。
A星算法,egret中使用例子
var list = [ [1,1,1,1,0,1,0,0,0,1,0,0,1,1,0], [0,1,1,0,1,1,0,1,0,1,1,1,1,0,0], [0,0,1,1,1,0,0,1,1,1,1,0,1,0,1], [0,0,0,1,1,0,0,1,1,0,1,0,1,1,1], [0,0,0,1,0,0,1,1,1,0,1,0,1,1,1], [0,0,1,1,1,1,1,1,0,0,0,0,1,0,1], [0,0,1,1,1,0,0,1,0,1,1,0,1,0,1], ]; var graph = new AStar.Graph(list,{}); var start = graph.grid[0][0]; var end = graph.grid[3][14]; var result = AStar.astar.search(graph, start, end,{closest:true, heuristic:AStar.astar.heuristics.diagonal});
源代码
module AStar { export class AStar { public constructor() { } } export function pathTo(node) { var curr = node; var path = []; while (curr.parent) { path.unshift(curr); curr = curr.parent; } return path; } export function getHeap() { return new BinaryHeap(function(node) { return node.f; }); } export var astar = { /** * Perform an A* Search on a graph given a start and end node. * @param {Graph} graph * @param {GridNode} start * @param {GridNode} end * @param {Object} [options] * @param {bool} [options.closest] Specifies whether to return the path to the closest node if the target is unreachable. * @param {Function} [options.heuristic] Heuristic function (see * astar.heuristics). */ search: function(graph, start, end, options) { graph.cleanDirty(); options = options || {}; var heuristic = options.heuristic || astar.heuristics.manhattan; var closest = options.closest || false; var openHeap = getHeap(); var closestNode = start; // set the start node to be the closest if required start.h = heuristic(start, end); graph.markDirty(start); openHeap.push(start); while (openHeap.size() > 0) { // Grab the lowest f(x) to process next. Heap keeps this sorted for us. var currentNode = openHeap.pop(); // End case -- result has been found, return the traced path. if (currentNode === end) { return pathTo(currentNode); } // Normal case -- move currentNode from open to closed, process each of its neighbors. currentNode.closed = true; // Find all neighbors for the current node. var neighbors = graph.neighbors(currentNode); for (var i = 0, il = neighbors.length; i < il; ++i) { var neighbor = neighbors[i]; if (neighbor.closed || neighbor.isWall()) { // Not a valid node to process, skip to next neighbor. continue; } // The g score is the shortest distance from start to current node. // We need to check if the path we have arrived at this neighbor is the shortest one we have seen yet. var gScore = currentNode.g + neighbor.getCost(currentNode); var beenVisited = neighbor.visited; if (!beenVisited || gScore < neighbor.g) { // Found an optimal (so far) path to this node. Take score for node to see how good it is. neighbor.visited = true; neighbor.parent = currentNode; neighbor.h = neighbor.h || heuristic(neighbor, end); neighbor.g = gScore; neighbor.f = neighbor.g + neighbor.h; graph.markDirty(neighbor); if (closest) { // If the neighbour is closer than the current closestNode or if it‘s equally close but has // a cheaper path than the current closest node then it becomes the closest node if (neighbor.h < closestNode.h || (neighbor.h === closestNode.h && neighbor.g < closestNode.g)) { closestNode = neighbor; } } if (!beenVisited) { // Pushing to heap will put it in proper place based on the ‘f‘ value. openHeap.push(neighbor); } else { // Already seen the node, but since it has been rescored we need to reorder it in the heap openHeap.rescoreElement(neighbor); } } } } if (closest) { return pathTo(closestNode); } // No result was found - empty array signifies failure to find path. return []; }, // See list of heuristics: http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html heuristics: { manhattan: function(pos0, pos1) { var d1 = Math.abs(pos1.x - pos0.x); var d2 = Math.abs(pos1.y - pos0.y); return d1 + d2; }, diagonal: function(pos0, pos1) { var D = 1; var D2 = Math.sqrt(2); var d1 = Math.abs(pos1.x - pos0.x); var d2 = Math.abs(pos1.y - pos0.y); return (D * (d1 + d2)) + ((D2 - (2 * D)) * Math.min(d1, d2)); } }, cleanNode: function(node) { node.f = 0; node.g = 0; node.h = 0; node.visited = false; node.closed = false; node.parent = null; } }; /** * A graph memory structure * @param {Array} gridIn 2D array of input weights * @param {Object} [options] * @param {bool} [options.diagonal] Specifies whether diagonal moves are allowed */ export function Graph(gridIn, options) { options = options || {}; this.nodes = []; this.diagonal = !!options.diagonal; this.grid = []; for (var x = 0; x < gridIn.length; x++) { this.grid[x] = []; for (var y = 0, row = gridIn[x]; y < row.length; y++) { var node = new GridNode(x, y, row[y]); this.grid[x][y] = node; this.nodes.push(node); } } this.init(); } Graph.prototype.init = function() { this.dirtyNodes = []; for (var i = 0; i < this.nodes.length; i++) { astar.cleanNode(this.nodes[i]); } }; Graph.prototype.cleanDirty = function() { for (var i = 0; i < this.dirtyNodes.length; i++) { astar.cleanNode(this.dirtyNodes[i]); } this.dirtyNodes = []; }; Graph.prototype.markDirty = function(node) { this.dirtyNodes.push(node); }; Graph.prototype.neighbors = function(node) { var ret = []; var x = node.x; var y = node.y; var grid = this.grid; // West if (grid[x - 1] && grid[x - 1][y]) { ret.push(grid[x - 1][y]); } // East if (grid[x + 1] && grid[x + 1][y]) { ret.push(grid[x + 1][y]); } // South if (grid[x] && grid[x][y - 1]) { ret.push(grid[x][y - 1]); } // North if (grid[x] && grid[x][y + 1]) { ret.push(grid[x][y + 1]); } if (this.diagonal) { // Southwest if (grid[x - 1] && grid[x - 1][y - 1]) { ret.push(grid[x - 1][y - 1]); } // Southeast if (grid[x + 1] && grid[x + 1][y - 1]) { ret.push(grid[x + 1][y - 1]); } // Northwest if (grid[x - 1] && grid[x - 1][y + 1]) { ret.push(grid[x - 1][y + 1]); } // Northeast if (grid[x + 1] && grid[x + 1][y + 1]) { ret.push(grid[x + 1][y + 1]); } } return ret; }; Graph.prototype.toString = function() { var graphString = []; var nodes = this.grid; for (var x = 0; x < nodes.length; x++) { var rowDebug = []; var row = nodes[x]; for (var y = 0; y < row.length; y++) { rowDebug.push(row[y].weight); } graphString.push(rowDebug.join(" ")); } return graphString.join("\n"); }; function GridNode(x, y, weight) { this.x = x; this.y = y; this.weight = weight; } GridNode.prototype.toString = function() { return "[" + this.x + " " + this.y + "]"; }; GridNode.prototype.getCost = function(fromNeighbor) { // Take diagonal weight into consideration. if (fromNeighbor && fromNeighbor.x != this.x && fromNeighbor.y != this.y) { return this.weight * 1.41421; } return this.weight; }; GridNode.prototype.isWall = function() { return this.weight === 0; }; function BinaryHeap(scoreFunction) { this.content = []; this.scoreFunction = scoreFunction; } BinaryHeap.prototype = { push: function(element) { // Add the new element to the end of the array. this.content.push(element); // Allow it to sink down. this.sinkDown(this.content.length - 1); }, pop: function() { // Store the first element so we can return it later. var result = this.content[0]; // Get the element at the end of the array. var end = this.content.pop(); // If there are any elements left, put the end element at the // start, and let it bubble up. if (this.content.length > 0) { this.content[0] = end; this.bubbleUp(0); } return result; }, remove: function(node) { var i = this.content.indexOf(node); // When it is found, the process seen in ‘pop‘ is repeated // to fill up the hole. var end = this.content.pop(); if (i !== this.content.length - 1) { this.content[i] = end; if (this.scoreFunction(end) < this.scoreFunction(node)) { this.sinkDown(i); } else { this.bubbleUp(i); } } }, size: function() { return this.content.length; }, rescoreElement: function(node) { this.sinkDown(this.content.indexOf(node)); }, sinkDown: function(n) { // Fetch the element that has to be sunk. var element = this.content[n]; // When at 0, an element can not sink any further. while (n > 0) { // Compute the parent element‘s index, and fetch it. var parentN = ((n + 1) >> 1) - 1; var parent = this.content[parentN]; // Swap the elements if the parent is greater. if (this.scoreFunction(element) < this.scoreFunction(parent)) { this.content[parentN] = element; this.content[n] = parent; // Update ‘n‘ to continue at the new position. n = parentN; } // Found a parent that is less, no need to sink any further. else { break; } } }, bubbleUp: function(n) { // Look up the target element and its score. var length = this.content.length; var element = this.content[n]; var elemScore = this.scoreFunction(element); while (true) { // Compute the indices of the child elements. var child2N = (n + 1) << 1; var child1N = child2N - 1; // This is used to store the new position of the element, if any. var swap = null; var child1Score; // If the first child exists (is inside the array)... if (child1N < length) { // Look it up and compute its score. var child1 = this.content[child1N]; child1Score = this.scoreFunction(child1); // If the score is less than our element‘s, we need to swap. if (child1Score < elemScore) { swap = child1N; } } // Do the same checks for the other child. if (child2N < length) { var child2 = this.content[child2N]; var child2Score = this.scoreFunction(child2); if (child2Score < (swap === null ? elemScore : child1Score)) { swap = child2N; } } // If the element needs to be moved, swap it, and continue. if (swap !== null) { this.content[n] = this.content[swap]; this.content[swap] = element; n = swap; } // Otherwise, we are done. else { break; } } } }; }
以上是关于A星算法的主要内容,如果未能解决你的问题,请参考以下文章