### MATLAB 中的地图路径规划算法示例
在 MATLAB 中实现地图路径规划通常涉及多种算法,例如 A* 算法、Dijkstra 算法以及 RRT(Rapidly-exploring Random Trees)。以下是基于这些常见方法的一个简单实例。
#### 使用 Dijkstra 算法进行路径规划
Dijkstra 是一种经典的最短路径算法,在网格环境中可以有效找到两点之间的最优路径。下面是一个简单的 MATLAB 实现:
```matlab
function path = dijkstra(map, start, goal)
% 初始化距离矩阵和前驱节点矩阵
[rows, cols] = size(map);
distance = inf(rows, cols);
predecessor = zeros(rows, cols);
% 设置起点的距离为0
distance(start(1), start(2)) = 0;
% 创建未访问集合并初始化优先队列
unvisited = true(size(map));
while any(unvisited(:))
% 找到当前最小距离的节点
current = find(distance .* unvisited == min(distance(unvisited)), 1);
% 如果目标已被访问,则退出循环
if isequal(current, sub2ind([rows,cols],goal(1),goal(2)))
break;
end
% 更新邻居节点的距离
neighbors = get_neighbors(current, rows, cols, map);
for i = 1:length(neighbors)
neighbor = neighbors(i,:);
alt_distance = distance(sub2ind([rows,cols],current(1),current(2))) + cost(map(neighbor(1), neighbor(2)));
if alt_distance < distance(neighbor(1), neighbor(2))
distance(neighbor(1), neighbor(2)) = alt_distance;
predecessor(neighbor(1), neighbor(2)) = sub2ind([rows,cols],current(1),current(2));
end
end
% 将当前节点标记为已访问
unvisited(current(1), current(2)) = false;
end
% 构建最终路径
path = reconstruct_path(predecessor, start, goal);
end
% 辅助函数:获取相邻节点
function neighbors = get_neighbors(node, rows, cols, map)
r = floor((node-1)/cols)+1; c = mod(node-1, cols)+1;
directions = [-1 0; 1 0; 0 -1; 0 1];
valid_neighbors = [];
for d = 1:size(directions,1)
nr = r + directions(d,1);
nc = c + directions(d,2);
if nr >=1 && nr <=rows && nc>=1 && nc<=cols && map(nr,nc)==0
valid_neighbors(end+1,:) = [nr, nc];
end
end
neighbors = valid_neighbors;
end
% 辅助函数:重建路径
function path = reconstruct_path(predecessor, start, goal)
path = [];
current = sub2ind(size(predecessor), goal(1), goal(2));
while ~isequal(current, [])
path = [ind2sub(size(predecessor), current); path];
current = predecessor(path(1,1),path(1,2));
end
end
% 辅助函数:计算代价
function c = cost(value)
if value == 0
c = 1;
else
c = Inf;
end
end
```
此代码实现了基本的 Dijkstra 算法来寻找二维栅格地图中的最短路径[^1]。用户可以通过定义 `map` 来指定障碍物的位置,并通过设置起始点和终点来进行测试。
#### 使用 A* 算法优化路径规划
A* 算法是一种启发式搜索算法,它结合了实际成本和估计剩余成本,从而更快地找到最佳路径。下面是其简化版本:
```matlab
function path = a_star(map, start, goal)
openList = struct('pos', {start}, 'gScore', {0}, 'fScore', {heuristic(start, goal)});
closedList = [];
while ~isempty(openList)
[~, idx] = min([openList.fScore]);
currentNode = openList(idx).pos;
if isequal(currentNode, goal)
returnPath = reconstruct_aStar_path(closedList, currentNode);
path = flipud(returnPath);
return;
end
openList(idx) = []; % Remove from open list
closedList{end+1} = struct('pos', currentNode, 'cameFrom', [], 'gScore', NaN, 'fScore', NaN);
neighbors = get_neighbors(currentNode, size(map,1), size(map,2), map);
for n = 1:numel(neighbors)
tentative_gScore = closedList{end}.gScore + heuristic(currentNode, neighbors(n,:));
if contains_pos(closedList, neighbors(n,:))
continue;
elseif ~contains_pos(openList, neighbors(n,:))
newEntry = struct('pos', neighbors(n,:), ...
'gScore', tentative_gScore, ...
'fScore', tentative_gScore + heuristic(neighbors(n,:), goal));
openList{end+1} = newEntry;
else
old_gScore = get_field_value(openList, 'gScore', neighbors(n,:));
if (tentative_gScore < old_gScore)
update_open_list(openList, neighbors(n,:), tentative_gScore, goal);
end
end
end
end
path = [];
end
function h = heuristic(a,b)
h = abs(b(1)-a(1)) + abs(b(2)-a(2)); % Manhattan Distance
end
function p = reconstruct_aStar_path(list,pos)
p = pos;
for k=numel(list):-1:1
if isequal(pos,list{k}.pos)
pos = list{k}.cameFrom;
p = [p; pos];
end
end
end
```
这段代码展示了如何利用曼哈顿距离作为启发函数完成更高效的路径查找过程[^2]。
#### 结合 Robotics System Toolbox 的功能
如果拥有 MATLAB 的 **Robotics System Toolbox**,可以直接调用内置工具箱的功能快速构建复杂环境下的路径规划解决方案。例如使用 `mobileRobotPRM` 或者 `navGraph` 对象创建导航图谱,并应用各种高级策略解决动态避障等问题。
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