Now let's solve the maze by allowing the solution to tunnel through walls. We can use it now to plot the shortest path. The binaryImageGraph function automatically put that auxiliary information into the graph. Notice that the graph's node table contains the x- and y- coordinates of the corresponding image pixels. These values can be used to index into the Nodes table of the graph. p = shortestpath(maze_graph,start_node,finish_node) The function shortestpath returns a list of graph node indices. Now we just call shortestpath to find our way from the start node to the finish node. The start and finish coordinates for the maze are (1,496) and (533,518), which we can use to find the corresponding graph nodes. These weights are the Euclidean distances between the centers of the neighboring foreground pixels. Notice that binaryImageGraph automatically added weights to the graph edges: maze_graph.Edges(1:5,:) (The background pixels form the maze walls.) maze_graph = binaryImageGraph(maze) The nodes of the graph will be the foreground pixels, which are the paths of the maze. The first step is to construct a graph from the binary image. Here's the maze we'll be tackling: url = '' Let's start with a maze solver that doesn't cheat.

Solver matlab