迷宫寻路A*算法matlab代码实现

A*算法是一种基于启发式搜索的寻路算法，常用于解决迷宫寻路问题。下面是一个简单的迷宫寻路A*算法Matlab代码实现：

function [path,visited] = A_star(maze,start,goal)

% Initialize the open and closed lists
open_list = start;
closed_list = [];

% Initialize the g and h scores of the start node
start.g = 0;
start.h = heuristic(start,goal);

while ~isempty(open_list)
    % Find the node with the lowest f score in the open list
    current_node = open_list(1);
    for i = 2:length(open_list)
        if open_list(i).f < current_node.f
            current_node = open_list(i);
        end
    end
    
    % If the goal is reached, return the path and visited nodes
    if isequal(current_node,goal)
        path = reconstruct_path(current_node);
        visited = closed_list;
        return;
    end
    
    % Move the current node from open to closed list
    open_list(open_list == current_node) = [];
    closed_list(end+1) = current_node;
    
    % Generate successor nodes
    successors = generate_successors(current_node,maze);
    
    for i = 1:length(successors)
        successor = successors(i);
        
        % If the successor is already in the closed list, skip it
        if any(ismember(closed_list,successor,'rows'))
            continue;
        end
        
        % Calculate the tentative g score
        tentative_g = current_node.g + 1;
        
        % If the successor is not in the open list, add it and calculate its f and g scores
        if ~any(ismember(open_list,successor,'rows'))
            successor.g = tentative_g;
            successor.h = heuristic(successor,goal);
            successor.f = successor.g + successor.h;
            successor.parent = current_node;
            open_list(end+1) = successor;
        else % If the successor is already in the open list, update its g score if the tentative g score is lower
            index = find(ismember(open_list,successor,'rows'));
            if tentative_g < open_list(index).g
                open_list(index).g = tentative_g;
                open_list(index).f = open_list(index).g + open_list(index).h;
                open_list(index).parent = current_node;
            end
        end
    end
end

% If no path is found, return an empty path and visited list
path = [];
visited = [];

end

function successors = generate_successors(node,maze)

[row,col] = size(maze);
successors = [];

% Generate all possible successor nodes
for i = -1:1
    for j = -1:1
        if abs(i) == abs(j) || node.x+i < 1 || node.x+i > row || node.y+j < 1 || node.y+j > col || maze(node.x+i,node.y+j) == 1
            continue;
        end
        successors(end+1,:) = [node.x+i,node.y+j];
    end
end

% Convert the successor nodes to node objects and set their parent nodes
for i = 1:size(successors,1)
    successors(i) = Node(successors(i,1),successors(i,2));
    successors(i).parent = node;
end

end

function h = heuristic(node,goal)

% Calculate the Manhattan distance between the node and the goal
h = abs(node.x - goal.x) + abs(node.y - goal.y);

end

function path = reconstruct_path(node)

% Reconstruct the path from the goal node to the start node
path = [node.x,node.y];
while ~isempty(node.parent)
    node = node.parent;
    path(end+1,:) = [node.x,node.y];
end

% Reverse the path to get it from start to goal
path = flipud(path);

end

classdef Node
% Node class to represent a node in the A* search algorithm

properties
    x % X-coordinate of the node in the maze
    y % Y-coordinate of the node in the maze
    g % Cost of the path from the start node to this node
    h % Heuristic cost of this node to the goal node
    f % Estimated total cost of this node (f = g + h)
    parent % Parent node of this node in the search tree
end

methods
    
    function obj = Node(x,y)
        obj.x = x;
        obj.y = y;
    end
    
end

end

使用该代码实现A*算法的迷宫寻路，需要提供一个二维的maze矩阵，其中0表示可通过的空间，1表示障碍物。同时还需要提供起点start和终点goal。函数会返回最短路径path和访问过的节点visited。你可以根据需要对其进行修改和优化。