![recursive backtracking maze generator algorithm python recursive backtracking maze generator algorithm python](https://scipython.com/static/media/uploads/blog/df_maze/df_maze.png)
Choose randomly one of the unvisited neighbours.If the current cell has any neighbours which have not been visited.Make the initial cell the current cell and mark it as visited.The depth-first search algorithm of maze generation is frequently implemented using backtracking: Mazes generated with a depth-first search have a low branching factor and contain many long corridors, because the algorithm explores as far as possible along each branch before backtracking. This also provides a quick way to display a solution, by starting at any given point and backtracking to the beginning. The algorithm can be rearranged into a loop by storing backtracking information in the maze itself. We can be sure every cell is visited.Īs given above this algorithm involves deep recursion which may cause stack overflow issues on some computer architectures. This process continues until every cell has been visited, causing the computer to backtrack all the way back to the beginning cell. When at a dead-end it backtracks through the path until it reaches a cell with an unvisited neighbour, continuing the path generation by visiting this new, unvisited cell (creating a new junction). The computer continues this process, with a cell that has no unvisited neighbours being considered a dead-end. The computer removes the wall between the two cells and marks the new cell as visited, and adds it to the stack to facilitate backtracking. Starting from a random cell, the computer then selects a random neighbouring cell that has not yet been visited. Consider the space for a maze being a large grid of cells (like a large chess board), each cell starting with four walls. Frequently implemented with a stack, this approach is one of the simplest ways to generate a maze using a computer. This algorithm is a randomized version of the depth-first search algorithm. Finally, when all vertices of F have been visited, F is erased and two edges from G, one for the entrance and one for the exit, are removed. During the traversal, whenever a red edge crosses over a blue edge, the blue edge is removed.
![recursive backtracking maze generator algorithm python recursive backtracking maze generator algorithm python](https://www.laurentluce.com/images/blog/maze/a_star_1.png)
Second, computer traverses F using a chosen algorithm, such as a depth-first search, coloring the path red. First, the computer creates a random planar graph G shown in blue, and its dual F shown in yellow.
![recursive backtracking maze generator algorithm python recursive backtracking maze generator algorithm python](https://cdn-screenshots.comidoc.net/658286_4.png)
The animation shows the maze generation steps for a graph that is not on a rectangular grid. Loops, which can confound naive maze solvers, may be introduced by adding random edges to the result during the course of the algorithm. Because of this, maze generation is often approached as generating a random spanning tree. If the graph contains loops, then there may be multiple paths between the chosen nodes. If the subgraph is not connected, then there are regions of the graph that are wasted because they do not contribute to the search space. The purpose of the maze generation algorithm can then be considered to be making a subgraph in which it is challenging to find a route between two particular nodes. This predetermined arrangement can be considered as a connected graph with the edges representing possible wall sites and the nodes representing cells. A maze can be generated by starting with a predetermined arrangement of cells (most commonly a rectangular grid but other arrangements are possible) with wall sites between them.