RRT.m

x_max=1000;
y_max=1000;
obstacle=[400,175,250,250];
EPS=20;
numNodes=3000;
q_start.coord=[0 0];
q_start.cost=0;
q_start.parent=0;
q_goal.coord=[999 999];
q_goal.cost=0;
nodes(1) = q_start;
figure(1)
axis([0 x_max 0 y_max])
rectangle('Position',obstacle,'FaceColor',[0 .5 .5])
hold on

for i = 1:1:numNodes
    q_rand = [floor(rand(1)*x_max) floor(rand(1)*y_max)];
    plot(q_rand(1), q_rand(2), 'x', 'Color',  [0 0.4470 0.7410])
    
    % Break if goal node is already reached
    for j = 1:1:length(nodes)
        if nodes(j).coord == q_goal.coord
            break
        end
    end
    
    % Pick the closest node from existing list to branch out from
    ndist = [];
    for j = 1:1:length(nodes)
        n = nodes(j);
        tmp = dist(n.coord, q_rand);
        ndist = [ndist tmp];
    end
    [val, idx] = min(ndist);
    q_near = nodes(idx);
    
    q_new.coord = steer(q_rand, q_near.coord, val, EPS);
    if noCollision(q_rand, q_near.coord, obstacle)
        line([q_near.coord(1), q_new.coord(1)], [q_near.coord(2), q_new.coord(2)], 'Color', 'k', 'LineWidth', 2);
        drawnow
        hold on
        q_new.cost = dist(q_new.coord, q_near.coord) + q_near.cost;
        
        % Within a radius of r, find all existing nodes
        q_nearest = [];
        r = 60;
        neighbor_count = 1;
        for j = 1:1:length(nodes)
            if noCollision(nodes(j).coord, q_new.coord, obstacle) && dist(nodes(j).coord, q_new.coord) <= r
                q_nearest(neighbor_count).coord = nodes(j).coord;
                q_nearest(neighbor_count).cost = nodes(j).cost;
                neighbor_count = neighbor_count+1;
            end
        end
        
        % Initialize cost to currently known value
        q_min = q_near;
        C_min = q_new.cost;
        
        % Iterate through all nearest neighbors to find alternate lower
        % cost paths
        
        for k = 1:1:length(q_nearest)
            if noCollision(q_nearest(k).coord, q_new.coord, obstacle) && q_nearest(k).cost + dist(q_nearest(k).coord, q_new.coord) < C_min
                q_min = q_nearest(k);
                C_min = q_nearest(k).cost + dist(q_nearest(k).coord, q_new.coord);
                line([q_min.coord(1), q_new.coord(1)], [q_min.coord(2), q_new.coord(2)], 'Color', 'g');                
                hold on
            end
        end
        
        % Update parent to least cost-from node
        for j = 1:1:length(nodes)
            if nodes(j).coord == q_min.coord
                q_new.parent = j;
            end
        end
        
        % Append to nodes
        nodes = [nodes q_new];
    end
end

D = [];
for j = 1:1:length(nodes)
    tmpdist = dist(nodes(j).coord, q_goal.coord);
    D = [D tmpdist];
end

% Search backwards from goal to start to find the optimal least cost path
[val, idx] = min(D);
q_final = nodes(idx);
q_goal.parent = idx;
q_end = q_goal;
nodes = [nodes q_goal];
while q_end.parent ~= 0
    start = q_end.parent;
    line([q_end.coord(1), nodes(start).coord(1)], [q_end.coord(2), nodes(start).coord(2)], 'Color', 'r', 'LineWidth', 2);
    hold on
    q_end = nodes(start);
end