[matlab] 7.快速搜索随机树（RRT---Rapidly-exploring Random Trees) 路径规划

Posted 2021-01-01 Clemente

RRT是一种多维空间中有效率的规划方法。它以一个初始点作为根节点，通过随机采样增加叶子节点的方式，生成一个随机扩展树，当随机树中的叶子节点包含了目标点或进入了目标区域，便可以在随机树中找到一条由从初始点到目标点的路径。RRT方法是概率完备且不最优的。

function BuildRRT(qinit, K, Δq)
    T.init(qinit)
    for k = 1 to K
        qrand = Sample()  -- chooses a random configuration
        qnearest = Nearest(T, qrand) -- selects the node in the RRT tree that is closest to qrand
        if  Distance(qnearest, qgoal) < Threshold then
            return true
        qnew = Extend(qnearest, qrand, Δq)  -- moving from qnearest an incremental distance in the direction of qrand
        if qnew ≠ NULL then
            T.AddNode(qnew)
    return false


function Sample() -- Alternatively,one could replace Sample with SampleFree(by using a collision detection algorithm to reject samples in C_obstacle
    p = Random(0, 1.0)
    if 0 < p < Prob then
        return qgoal
    elseif Prob < p < 1.0 then
        return RandomNode()

RRT 伪代码

　　初始化时随机树T只包含一个节点：根节点q_init。首先Sample函数从状态空间中随机选择一个采样点q_rand（4行）；然后Nearest函数从随机树中选择一个距离q_rand最近的节点q_nearest（5行）；最后Extend函数通过从q_nearest向q_rand扩展一段距离，得到一个新的节点q_new（8行）。如果q_new与障碍物发生碰撞，则Extend函数返回空，放弃这次生长，否则将q_new加入到随机树中。重复上述步骤直到q_nearest和目标点q_gaol距离小于一个阈值，则代表随机树到达了目标点，算法返回成功（6~7行）。为了使算法可控，可以设定运行时间上限或搜索次数上限（3行）。如果在限制次数内无法到达目标点，则算法返回失败。
　　为了加快随机树到达目标点的速度，简单的改进方法是：在随机树每次的生长过程中，根据随机概率来决定q_rand是目标点还是随机点。在Sample函数中设定参数Prob，每次得到一个0到1.0的随机值p，当0<p<Prob的时候，随机树朝目标点生长行；当Prob<p<1.0时，随机树朝一个随机方向生长。

　　上述算法的效果是随机采样点会“拉着”树向外生长，这样能更快、更有效地探索空间（The effect is that the nearly uniformly distributed samples “pull” the tree toward them, causing the tree to rapidly explore C-Space）。随机探索也讲究策略，如果我们从树中随机取一个点，然后向着随机的方向生长，那么结果是什么样的呢？见下图（Left: A tree generated by applying a uniformly-distributed random motion from a randomly chosen tree node does not explore very far. Right: A tree generated by the RRT algorithm using samples drawn randomly from a uniform distribution. Both trees have 2000 nodes ）。可以看到，同样是随机树，但是这棵树并没很好地探索空间。

 

 

下面是具体的代码

%% RRT parameters
map=im2bw(imread(\'map.bmp\')); % input map read from a bmp file. for new maps write the file name here
source=[490 490]; % source position in Y, X format
goal=[10 900]; % goal position in Y, X format
stepsize = 20;  % size of each step of the RRT
threshold = 20; % nodes closer than this threshold are taken as almost the same
maxFailedAttempts = 10000;
display = true; % display of RRT

if ~feasiblePoint(source,map), error(\'source lies on an obstacle or outside map\'); end
if ~feasiblePoint(goal,map), error(\'goal lies on an obstacle or outside map\'); end
if display,imshow(map);rectangle(\'position\',[1 1 size(map)-1],\'edgecolor\',\'k\'); end

tic;  % tic-toc: Functions for Elapsed Time

RRTree = double([source -1]); % RRT rooted at the source, representation node and parent index
failedAttempts = 0;
counter = 0;
pathFound = false;

while failedAttempts <= maxFailedAttempts  % loop to grow RRTs
%% chooses a random configuration
    if rand < 0.5
        sample = rand(1,2) .* size(map);   % random sample
    else
        sample = goal; % sample taken as goal to bias tree generation to goal
    end
    
%% selects the node in the RRT tree that is closest to qrand
    [A, I] = min( distanceCost(RRTree(:,1:2),sample) ,[],1); % find the minimum value of each column
    closestNode = RRTree(I(1),1:2);
    
%% moving from qnearest an incremental distance in the direction of qrand
    theta = atan2(sample(1)-closestNode(1),sample(2)-closestNode(2));  % direction to extend sample to produce new node
    newPoint = double(int32(closestNode(1:2) + stepsize * [sin(theta)  cos(theta)]));
    if ~checkPath(closestNode(1:2), newPoint, map) % if extension of closest node in tree to the new point is feasible
        failedAttempts = failedAttempts + 1;
        continue;
    end
    
    if distanceCost(newPoint,goal) < threshold, pathFound = true; break; end % goal reached
    
    [A, I2] = min( distanceCost(RRTree(:,1:2),newPoint) ,[],1); % check if new node is not already pre-existing in the tree
    if distanceCost(newPoint,RRTree(I2(1),1:2)) < threshold, failedAttempts = failedAttempts + 1; continue; end 
    
    RRTree = [RRTree; newPoint I(1)]; % add node
    failedAttempts = 0;
    if display, line([closestNode(2);newPoint(2)],[closestNode(1);newPoint(1)]);counter = counter + 1; M(counter) = getframe; end % Capture movie frame 
end

% getframe returns a movie frame, which is a structure having two fields
if display && pathFound, line([closestNode(2);goal(2)],[closestNode(1);goal(1)]); counter = counter+1;M(counter) = getframe; end

if display, disp(\'click/press any key\'); waitforbuttonpress; end
if ~pathFound, error(\'no path found. maximum attempts reached\'); end

%% retrieve path from parent information
path = [goal];
prev = I(1);
while prev > 0
    path = [RRTree(prev,1:2); path];
    prev = RRTree(prev,3);
end

pathLength = 0;
for i=1:length(path)-1, pathLength = pathLength + distanceCost(path(i,1:2),path(i+1,1:2)); end % calculate path length
fprintf(\'processing time=%d \\nPath Length=%d \\n\\n\', toc, pathLength); 
imshow(map);rectangle(\'position\',[1 1 size(map)-1],\'edgecolor\',\'r\');
line(path(:,2),path(:,1));

rrt_main.m

%% distanceCost.m
function h=distanceCost(a,b)
    h = sqrt(sum((a-b).^2, 2));

distanceCost.m

%% checkPath.m    
function feasible=checkPath(n,newPos,map)
feasible=true;
dir=atan2(newPos(1)-n(1),newPos(2)-n(2));
for r=0:0.5:sqrt(sum((n-newPos).^2))
    posCheck=n+r.*[sin(dir) cos(dir)];
    if ~(feasiblePoint(ceil(posCheck),map) && feasiblePoint(floor(posCheck),map) && ... 
            feasiblePoint([ceil(posCheck(1)) floor(posCheck(2))],map) && feasiblePoint([floor(posCheck(1)) ceil(posCheck(2))],map))
        feasible=false;break;
    end
    if ~feasiblePoint(newPos,map), feasible=false; end
end

checkPath.m

%% feasiblePoint.m
function feasible=feasiblePoint(point,map)
feasible=true;
% check if collission-free spot and inside maps
if ~(point(1)>=1 && point(1)<=size(map,1) && point(2)>=1 && point(2)<=size(map,2) && map(point(1),point(2))==1)
    feasible=false;
end

feasiblePoint.m

 

结果如图