避障路径规划算法MATLAB

MATLAB是一种常用的科学计算软件，也可以用来实现避障路径规划算法。下面介绍两种常见的避障路径规划算法的MATLAB实现方法：

1. A*算法：

A*算法是一种基于启发式搜索的路径规划算法，可以在有障碍物的环境中规划出一条最优路径。实现A*算法的MATLAB代码如下：

function [path, cost] = Astar(start, goal, obstacles)
% start: 起点坐标
% goal: 终点坐标
% obstacles: 障碍物坐标

% 初始化起点和终点节点
start_node = Node(start, 0, 0, 0, 0);
goal_node = Node(goal, 0, 0, 0, 0);

% 初始化开放列表和关闭列表
open_list = [start_node];
close_list = [];

while ~isempty(open_list)
    % 找到f值最小的节点
    current_node = open_list(1);
    current_index = 1;
    for i = 1:length(open_list)
        if open_list(i).f < current_node.f
            current_node = open_list(i);
            current_index = i;
        end
    end

    % 将当前节点加入关闭列表，并从开放列表中删除
    open_list(current_index) = [];
    close_list = [close_list, current_node];

    % 到达终点
    if current_node.position == goal_node.position
        path = [];
        cost = current_node.g;
        while current_node.parent ~= 0
            path = [current_node.position, path];
            current_node = current_node.parent;
        end
        path = [start, path];
        return
    end

    % 扩展当前节点
    children = [];
    for i = -1:1
        for j = -1:1
            if i == 0 && j == 0
                continue
            end
            node_position = [current_node.position(1)+i, current_node.position(2)+j];
            if node_position(1) > size(obstacles, 1) || node_position(1) < 1 || node_position(2) > size(obstacles, 2) || node_position(2) < 1
                continue
            end
            if sum(ismember(obstacles, node_position, 'rows')) == 1
                continue
            end
            new_node = Node(node_position, current_node.g+1, heuristic(node_position, goal_node.position), current_node, 0);
            children = [children, new_node];
        end
    end

    % 对于每个子节点
    for i = 1:length(children)
        % 如果在关闭列表中
        if ~isempty(find(ismember(close_list, children(i), 'rows'), 1))
            continue
        end

        % 计算子节点的f、g、h值
        children(i).g = current_node.g + 1;
        children(i).h = heuristic(children(i).position, goal_node.position);
        children(i).f = children(i).g + children(i).h;

        % 如果在开放列表中
        index = find(ismember(open_list, children(i), 'rows'));
        if ~isempty(index)
            % 如果新的f值更小
            if children(i).f < open_list(index).f
                % 更新该节点
                open_list(index) = children(i);
            end
        else
            % 如果不在开放列表中，将该节点加入开放列表
            open_list = [open_list, children(i)];
        end
    end
end
end

function h = heuristic(position, goal_position)
% 启发函数：曼哈顿距离
h = abs(position(1)-goal_position(1)) + abs(position(2)-goal_position(2));
end

classdef Node
% 节点类
properties
    position % 坐标
    g % 到起点的距离
    h % 启发函数的值
    f % f = g + h
    parent % 父节点
end

methods
    function obj = Node(position, g, h, parent, f)
        obj.position = position;
        obj.g = g;
        obj.h = h;
        obj.parent = parent;
        obj.f = f;
    end
end
end

2. DWA算法：

DWA算法是一种基于动态窗口的路径规划算法，可以在实时环境中规划出一条安全的路径。实现DWA算法的MATLAB代码如下：

function [v, w] = DWA(x, goal, obstacles)
% x: 当前状态
% goal: 终点坐标
% obstacles: 障碍物坐标

% 设定参数
v_max = 0.5; % 最大速度
v_min = -0.5; % 最小速度
w_max = pi/4; % 最大角速度
w_min = -pi/4; % 最小角速度
v_res = 0.05; % 速度分辨率
w_res = pi/16; % 角速度分辨率
dt = 0.1; % 时间间隔
predict_time = 1; % 预测时间
obstacle_radius = 0.2; % 障碍物半径
goal_radius = 0.2; % 终点半径

% 计算速度空间
v_space = [v_min:v_res:v_max];
w_space = [w_min:w_res:w_max];

% 初始化最优速度和角速度
v_best = 0;
w_best = 0;

% 初始化最小代价
cost_best = inf;

% 对于每个速度和角速度
for i = 1:length(v_space)
    for j = 1:length(w_space)
        % 计算状态变化
        x_next = motion(x, v_space(i), w_space(j), dt, predict_time);

        % 计算代价
        cost = cost_function(x_next, goal, obstacles, obstacle_radius, goal_radius);

        % 如果代价更小
        if cost < cost_best
            % 更新最优速度和角速度
            v_best = v_space(i);
            w_best = w_space(j);
            % 更新最小代价
            cost_best = cost;
        end
    end
end

% 返回最优速度和角速度
v = v_best;
w = w_best;
end

function x_next = motion(x, v, w, dt, predict_time)
% 状态更新
x_next = x;
for t = 0:dt:predict_time
    x_next(3) = x_next(3) + w*dt;
    x_next(1) = x_next(1) + v*cos(x_next(3))*dt;
    x_next(2) = x_next(2) + v*sin(x_next(3))*dt;
end
end

function cost = cost_function(x, goal, obstacles, obstacle_radius, goal_radius)
% 计算代价
cost_distance = norm(x(1:2)-goal) / max(abs([x(1:2); goal])) * 100;
cost_collision = 0;
for i = 1:size(obstacles, 1)
    if norm(x(1:2)-obstacles(i,:)) < obstacle_radius
        cost_collision = inf;
        break
    end
end
if norm(x(1:2)-goal) < goal_radius
    cost_goal = 0;
else
    cost_goal = inf;
end
cost = cost_distance + cost_collision + cost_goal;
end

以上代码仅作为参考，具体实现还需要根据具体情况进行调整和完善。