基于matlab平台，给出代码：RRT*与DWA融合在动态环境下避障

由于RRT*和DWA算法都比较复杂，这里只能给出大致的代码框架，具体实现还需要根据具体情况进行调整。

1. RRT*算法：

function [path, tree] = RRT_star(start, goal, obstacles, max_iter, step_size, goal_tol, connect_prob)
% 初始化树
tree = struct('vertex', {}, 'edge', {});
tree(1).vertex = start;
tree(1).edge = [];

% 开始迭代
for i = 1:max_iter
    % 随机采样点
    if rand() < connect_prob
        q_rand = goal;
    else
        q_rand = sample_free_space(obstacles);
    end
    
    % 找到最近点
    q_near_idx = nearest_neighbor(tree, q_rand);
    q_near = tree(q_near_idx).vertex;
    
    % 扩展树
    q_new = steer(q_near, q_rand, step_size);
    if ~collision_check(q_near, q_new, obstacles)
        % 找到最优父节点
        [q_min_idx, c_min] = find_best_parent(tree, q_new, q_near_idx, step_size);
        q_min = tree(q_min_idx).vertex;
        
        % 添加节点
        tree(end+1).vertex = q_new;
        tree(end).edge = [q_min_idx, length(tree)];
        
        % 重新连接父节点
        rewire(tree, q_new, q_near_idx, c_min, step_size);
    end
    
    % 判断是否到达终点
    if norm(q_new - goal) < goal_tol
        % 找到最优路径
        path = find_path(tree, length(tree));
        return;
    end
end

% 没有找到路径
path = [];
end

2. DWA算法：

function [v, w] = DWA(x, goal, obstacles, v_min, v_max, w_min, w_max, dt, goal_tol, max_speed, max_yawrate, max_accel, max_dyawrate, v_reso, yawrate_reso, delta_t, predict_time)
% 生成动态窗口
dw = calc_dynamic_window(x, v_min, v_max, w_min, w_max, dt, max_speed, max_yawrate, max_accel, max_dyawrate);

% 计算目标函数
u, traj = calc_control_and_trajectory(dw, x, goal, obstacles, v_reso, yawrate_reso, delta_t, predict_time);

% 选择最优控制输入
v, w = select_best_input(u, dw);

% 判断是否到达终点
if norm(x(1:2) - goal) < goal_tol
    v = 0;
    w = 0;
end
end

3. RRT*和DWA融合：

function [path, tree] = RRT_star_DWA(start, goal, obstacles, max_iter, step_size, goal_tol, connect_prob, v_min, v_max, w_min, w_max, dt, max_speed, max_yawrate, max_accel, max_dyawrate, v_reso, yawrate_reso, delta_t, predict_time)
% 初始化树
tree = struct('vertex', {}, 'edge', {});
tree(1).vertex = start;
tree(1).edge = [];

x = start;
for i = 1:max_iter
    % 使用DWA算法生成控制输入
    v, w = DWA(x, goal, obstacles, v_min, v_max, w_min, w_max, dt, goal_tol, max_speed, max_yawrate, max_accel, max_dyawrate, v_reso, yawrate_reso, delta_t, predict_time);
    
    % 计算下一个状态
    x_next = motion(x, v, w, dt);
    
    % 判断是否碰撞
    if collision_check(x, x_next, obstacles)
        continue;
    end
    
    % 将下一个状态加入树中
    q_new = x_next(1:2);
    q_near_idx = nearest_neighbor(tree, q_new);
    q_near = tree(q_near_idx).vertex;
    
    % 找到最优父节点
    [q_min_idx, c_min] = find_best_parent(tree, q_new, q_near_idx, step_size);
    q_min = tree(q_min_idx).vertex;
    
    % 添加节点
    tree(end+1).vertex = q_new;
    tree(end).edge = [q_min_idx, length(tree)];
    
    % 重新连接父节点
    rewire(tree, q_new, q_near_idx, c_min, step_size);
    
    % 判断是否到达终点
    if norm(q_new - goal) < goal_tol
        % 找到最优路径
        path = find_path(tree, length(tree));
        return;
    end
    
    % 更新状态
    x = x_next;
end

% 没有找到路径
path = [];
end

需要注意的是，在动态环境下，RRT*算法可能会找不到路径，因此需要在一定时间内尽可能多地搜索，并在超时后返回最优路径。同时，需要根据具体情况调整参数，如树的扩展步长、动态窗口的参数等。