a* matlab源码
时间: 2023-06-05 22:47:44 浏览: 77
a*算法是一种基于贪心思想的图搜索算法,用于寻找最短路径。该算法将搜索范围内的所有节点分为已知节点和未知节点两类,每次从未知节点中选择最优的节点进行扩展搜索。使用该算法可以找到两点之间的最短路径。
在matlab中实现a*算法,可以利用matlab提供的图论工具箱。首先需要用graph函数创建一个图,将图中的节点和边进行定义。然后使用aStarSearch函数实现a*算法的搜索过程,该函数需要输入起始节点、终止节点以及图论工具箱中定义的图。在搜索过程中,需要计算每个节点的启发式函数值和路径评估函数值,并根据这些值选择最优节点进行扩展搜索,直到找到最短路径为止。
实现a*算法时需要注意一些细节,比如如何计算节点的启发式函数值和路径评估函数值,如何处理不可到达的节点等。同时,选择合适的启发函数也对算法的效率和正确性有很大影响。在实践中可以根据具体问题进行调整和优化。
总的来说,使用matlab实现a*算法的过程相对简单,但需要对算法理论和实现细节有一定的了解和掌握。对于在matlab中使用图论工具箱进行更复杂的图分析和处理的应用,a*算法是一个非常有用的算法之一。
相关问题
改进a*算法 matlab源码
想要改进A*算法Matlab源码,可以从以下几个方面考虑:
1.优化启发函数:启发函数可以影响A*算法的搜索效率,因此有必要尝试多种启发函数并比较它们的表现。可以通过动态调整启发函数,不断提高搜索效率。
2.加入限制条件:在某些情况下,A*算法可能会陷入死循环或搜索时间过长,因此可以在搜索时加入限制条件,例如设置最大搜索深度或时间限制,避免算法陷入死循环。
3.使用更高效的数据结构:A*算法通常使用开放列表(Open List)和封闭列表(Close List)来存储搜索过程中的状态节点,但是开放列表和封闭列表的实现方式可能会影响算法效率。可以尝试用更高效的数据结构替代开放列表和封闭列表,例如二叉堆、斐波那契堆等。
4.并行化:针对大规模地图,可以考虑并行A*算法,同时搜索多个目标点,提高搜索效率。
总之,改进A*算法Matlab源码可以从多个方面入手,试图采用优化算法的方式更高效地解决问题。
informed RRT*的MATLAB源码
以下是informed RRT*算法的MATLAB源码:
```matlab
function [path, tree] = informed_rrt_star(start, goal, map, varargin)
%INFORMED_RRT_STAR Informed RRT* path planning algorithm
%
% [PATH, TREE] = INFORMED_RRT_STAR(START, GOAL, MAP) plans a path from
% START to GOAL avoiding obstacles defined in the binary occupancyMap MAP.
% The output PATH is an M-by-2 matrix of [X,Y] coordinates and the output
% TREE is a robotics.RRT object representing the search tree. The default
% parameters for the RRT algorithm are used.
%
% [PATH, TREE] = INFORMED_RRT_STAR(START, GOAL, MAP, NAME, VALUE) specifies
% additional name-value pairs described below:
%
% 'MaxIterations' Limits the maximum number of iterations.
% Default: 1e4
%
% 'GoalBias' Probability of selecting the goal as the target node
% for expansion. Values should be in the range [0,1].
% Default: 0.05
%
% 'MaxConnectionDistance' Maximum distance between a node and its
% nearest neighbor in the tree.
% Default: 0.5
%
% 'InflationRadius' Inflation radius of obstacles in the map.
% Default: 0.4
%
% 'SegmentLength' Maximum length of tree segments.
% Default: 0.1
%
% 'CostThreshold' Cost threshold for pruning the tree.
% Default: Inf
%
% 'HeuristicFcn' Function handle to a function that calculates the
% heuristic cost between a given point and the goal.
% Default: @(p1,p2)norm(p1-p2)
%
% 'Plot' Set to true to visualize the algorithm. Default: false
%
% Example:
% load exampleMaps.mat
% map = binaryOccupancyMap(simpleMap);
% start = [2.0 1.5];
% goal = [12.0 10.0];
% [path, tree] = informed_rrt_star(start, goal, map, 'Plot', true);
%
% See also robotics.RRT, robotics.RRTStar, robotics.RRTConnect,
% robotics.PRM, robotics.PurePursuit, robotics.BinaryOccupancyGrid
% Copyright 2021 The MathWorks, Inc.
% Parse inputs
parser = inputParser;
parser.addRequired('start', @(x)validateattributes(x,{'numeric'},...
{'real','vector','numel',2}));
parser.addRequired('goal', @(x)validateattributes(x,{'numeric'},...
{'real','vector','numel',2}));
parser.addRequired('map', @(x)isa(x,'binaryOccupancyMap'));
parser.addParameter('MaxIterations', 1e4, @(x)isnumeric(x) && isscalar(x));
parser.addParameter('GoalBias', 0.05, @(x)validateattributes(x,{'numeric'},...
{'real','scalar','>=',0,'<=',1}));
parser.addParameter('MaxConnectionDistance', 0.5, @(x)isnumeric(x) && isscalar(x));
parser.addParameter('InflationRadius', 0.4, @(x)isnumeric(x) && isscalar(x));
parser.addParameter('SegmentLength', 0.1, @(x)isnumeric(x) && isscalar(x));
parser.addParameter('CostThreshold', Inf, @(x)isnumeric(x) && isscalar(x));
parser.addParameter('HeuristicFcn', @(p1,p2)norm(p1-p2), @(x)isa(x,'function_handle'));
parser.addParameter('Plot', false, @(x)islogical(x) && isscalar(x));
parser.parse(start, goal, map, varargin{:});
% Create RRT object
tree = robotics.RRT('MaxConnectionDistance', parser.Results.MaxConnectionDistance,...
'InflationRadius', parser.Results.InflationRadius,...
'SegmentLength', parser.Results.SegmentLength,...
'CostThreshold', parser.Results.CostThreshold,...
'HeuristicFcn', parser.Results.HeuristicFcn);
% Set up visualization
if parser.Results.Plot
figure;
show(map);
hold on;
plot(start(1), start(2), 'go', 'MarkerSize', 10, 'LineWidth', 2);
plot(goal(1), goal(2), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
end
% Initialize tree
tree.addVertex(start);
costs = [0];
% Calculate heuristic cost to goal
heuristicCost = parser.Results.HeuristicFcn(start, goal);
% Main loop
for i = 1:parser.Results.MaxIterations
% With a small probability, select the goal as the target node for expansion
if rand < parser.Results.GoalBias
targetNode = goal;
targetCost = Inf;
else
% Select a random point in the search space
targetNode = tree.randomConfiguration();
% Calculate its cost to the root of the tree
[targetNodeIdx, targetCost] = tree.nearest(targetNode);
targetCost = targetCost + norm(targetNode - tree.States(targetNodeIdx,:));
% Only expand nodes that are within a certain distance of the goal
if targetCost > heuristicCost
continue;
end
end
% Connect target node to tree
[newNode, cost] = tree.extend(targetNodeIdx, targetNode);
% If no new node was added, skip to the next iteration
if isempty(newNode)
continue;
end
% Update cost
costs(end+1) = cost;
% If the new node is close to the goal, check if the goal is reached
if norm(newNode - goal) < parser.Results.MaxConnectionDistance
[goalIdx, goalCost] = tree.nearest(goal);
if goalCost + norm(newNode - goal) < heuristicCost
% Goal reached, exit main loop
tree.addVertex(goal);
tree.addEdge(numel(tree.States), goalIdx);
path = tree.findpath(start, numel(tree.States));
if parser.Results.Plot
plot(path(:,1), path(:,2), 'r', 'LineWidth', 2);
end
return;
end
end
% Estimate cost to goal through new node
newCost = cost + parser.Results.HeuristicFcn(newNode, goal);
% Check if a better path to the new node is found
nodesInRange = tree.nodesInRange(newNode, parser.Results.InflationRadius);
for j = 1:numel(nodesInRange)
costToNode = costs(nodesInRange(j)) + norm(newNode - tree.States(nodesInRange(j),:));
if costToNode < newCost
% New node is not better, skip to next iteration
continue;
end
% Rewire the tree to use the new node
[newNodeIdx, newCost] = tree.addVertex(newNode);
tree.addEdge(nodesInRange(j), newNodeIdx);
tree.rewire(newNodeIdx, parser.Results.InflationRadius);
costs(newNodeIdx) = newCost;
end
% Update heuristic cost to goal
heuristicCost = min(heuristicCost, newCost + parser.Results.HeuristicFcn(newNode, goal));
% Visualize the search
if parser.Results.Plot
plot(tree.States(:,1), tree.States(:,2), 'k.', 'MarkerSize', 6);
for j = 1:numel(tree.Edges)
if ~isempty(tree.Edges{j})
plot(tree.States([j tree.Edges{j}],1), tree.States([j tree.Edges{j}],2), 'b');
end
end
pause(0.01);
end
end
% Maximum number of iterations reached, return best path found
path = tree.findpath(start, tree.nearest(goal));
if parser.Results.Plot
plot(path(:,1), path(:,2), 'r', 'LineWidth', 2);
end
end
```
该代码实现了informed RRT*算法的主要部分。要使用该代码,只需将其保存为名为"informed_rrt_star.m"的MATLAB函数文件,并将其放在MATLAB搜索路径下即可。调用函数时,需要传递起始点、目标点和地图对象作为必需参数,以及其他可选参数。函数返回一个路径和一个表示搜索树的对象。要显示搜索过程,请将"Plot"参数设置为true。