By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Code Review Stack Exchange is a question and answer site for peer programmer code reviews. It only takes a minute to sign up. After watching Computerphile's video I decided to create my own maze solver and generator in Python. I've never written anything in Python so I'd like to get some feedback about my code, specifically about:.

This is my first mini-project and as such I have no idea what is the correct way of doing things e. There are no docstrings. What do these functions do? What arguments do they take? What do they return? There's repetitive code for printing progress messages and measuring the time taken. I would avoid this repetition using a context manager, like this:. This keeps the timing code out of the individual functions, making them shorter, simpler, and easier to test.

Lists and tuples are very convenient for building simple data structures, but code that accesses them is hard to understand. For example, what does this line test? By reading the comment, we can figure out that graph[v][i] is the vertex number of the i-1 -th neighbour of vertex number v. But it takes considerable effort to figure this out — first you have to find the comment explaining the data structure, and then you have to figure out that the comment isn't telling the whole truth because there can be multiple connected vertices, not just one as in the comment.

So let's see if we can simplify this data structure. The first thing to observe is that vertices are represented by integers, not by their coordinates. Why is that? There are two places where this fact is used. First, in the visited list:. After making these two changes, the vertices can be represented by any hashable objects, in particular by tuples posx, posy.

The other redundant piece of information in the graph data structure is the position of the wall between a vertex and its neighbour. A convenient way to construct such a data structure is to use a collections. This code iterates over the logical coordinates x, y and maintaining separate real coordinates posx, posy :. Now you can use itertools. There's repetitive code for adding the neighbours. The repetition can be avoided by making a list of cardinal directions:.

But the only thing we use visited for is to get a list of unvisited neighbours, so we could use path for this, and avoid the need for visited at all. Since the image is black-and-white, it's wasteful to use format 'RGB' which has 24 bits per pixel. Format '1' uses 1 bit per pixel.The purpose of this Python challenge is to demonstrate the use of a backtracking algorithm to find the exit path of Maze.

Backtracking Algorithm A backtracking algorithm is a recursive algorithm that attempts to solve a given problem by testing all possible paths towards a solution until a solution is found.

Each time a path is tested, if a solution is not found, the algorithm backtracks to test another possible path and so on till a solution is found or all paths have been tested.

The typical scenario where a backtracking algorithm is when you try to find your way out in a maze. Every time you reach a dead-end, you backtrack to try another path until you find the exit or all path have been explored. Backtracking algorithms can be used for other types of problems such as solving a Magic Square Puzzle or a Sudoku grid. Backtracking algorithms rely on the use of a recursive function. A recursive function is a function that calls itself until a condition is met.

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View all books. Follow this blog. Email Address. Related Posts.Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa. Rectangular maze solving using image processing with OpenCV and Numpy libraries.

Usage of an open sourced algorithm BFS concept. Solves Mazes taking an image input, along with the starting and ending points.

There are two Algorithms here which solve mazes. Theta maze solving using image processing with OpenCV and Numpy libraries. Used a variant of the Breadth First Search algorithm to solve the maze, and concept of the polar coordinate system.

Generates a maze, as well as its solution. The maze is generated using recursive backtracker, and the solution is found using DFS. Solves Newspaper Like Mazes, while traversing provided markers checkpoints along the path.

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A maze generator, solver and visualizer for Python. Updated Feb 10, Python. Updated Jul 19, Python. Updated Jan 15, Python. Star 8. Maze generator, solver and interactive game written in Python. Updated Oct 2, Python. Star 4. Updated Apr 11, Python. Updated Oct 8, Python. Star 3.

## A maze generator and solver written in Python

Updated Oct 18, Python. Introduction to AI assignment. Updated May 21, Python.Let us discuss Rat in a Maze as another example problem that can be solved using Backtracking. A rat starts from source and has to reach the destination. The rat can move only in two directions: forward and down. In the maze matrix, 0 means the block is a dead end and 1 means the block can be used in the path from source to destination. Note that this is a simple version of the typical Maze problem.

For example, a more complex version can be that the rat can move in 4 directions and a more complex version can be with a limited number of moves. Please refer complete article on Rat in a Maze Backtracking-2 for more details!

Writing code in comment? Please use ide. Following is an example maze. Python3 program to solve Rat in a Maze. A utility function to print solution matrix sol. A utility function to check if x, y is valid. It mainly uses solveMazeUtil to solve the problem. Please note. A recursive utility function to solve Maze problem. Check if maze[x][y] is valid.

Move forward in x direction. If moving in x direction doesn't give solution. If none of the above movements work then.

Driver program to test above function. Initialising the maze.There are a number of different maze solving algorithmsthat is, automated methods for the solving of mazes. Mazes containing no loops are known as "simply connected", or "perfect" mazes, and are equivalent to a tree in graph theory. Thus many maze solving algorithms are closely related to graph theory.

Intuitively, if one pulled and stretched out the paths in the maze in the proper way, the result could be made to resemble a tree. This is a trivial method that can be implemented by a very unintelligent robot or perhaps a mouse. It is simply to proceed following the current passage until a junction is reached, and then to make a random decision about the next direction to follow. Although such a method would always eventually find the right solutionthis algorithm can be extremely slow.

The best-known rule for traversing mazes is the wall followeralso known as either the left-hand rule or the right-hand rule.

If the maze is simply connectedthat is, all its walls are connected together or to the maze's outer boundary, then by keeping one hand in contact with one wall of the maze the solver is guaranteed not to get lost and will reach a different exit if there is one; otherwise, the algorithm will return to the entrance having traversed every corridor next to that connected section of walls at least once.

Another perspective into why wall following works is topological. If the walls are connected, then they may be deformed into a loop or circle.

To further this idea, notice that by grouping together connected components of the maze walls, the boundaries between these are precisely the solutions, even if there is more than one solution see figures on the right. If the maze is not simply-connected i. Another concern is that care should be taken to begin wall-following at the entrance to the maze. If the maze is not simply-connected and one begins wall-following at an arbitrary point inside the maze, one could find themselves trapped along a separate wall that loops around on itself and containing no entrances or exits.

Should it be the case that wall-following begins late, attempt to mark the position in which wall-following began. Because wall-following will always lead you back to where you started, if you come across your starting point a second time, you can conclude the maze is not simply-connected, and you should switch to an alternative wall not yet followed. See the Pledge Algorithmbelow, for an alternative methodology. Wall-following can be done in 3D or higher-dimensional mazes if its higher-dimensional passages can be projected onto the 2D plane in a deterministic manner.

For example, if in a 3D maze "up" passages can be assumed to lead Northwest, and "down" passages can be assumed to lead southeast, then standard wall following rules can apply.

However, unlike in 2D, this requires that the current orientation is known, to determine which direction is the first on the left or right. Disjoint [ clarification needed ] mazes can be solved with the wall follower method, so long as the entrance and exit to the maze are on the outer walls of the maze.

If however, the solver starts inside the maze, it might be on a section disjoint from the exit, and wall followers will continually go around their ring. The Pledge algorithm named after Jon Pledge of Exeter can solve this problem. The Pledge algorithm, designed to circumvent obstacles, requires an arbitrarily chosen direction to go toward, which will be preferential.

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When an obstacle is met, one hand say the right hand is kept along the obstacle while the angles turned are counted clockwise turn is positive, counter-clockwise turn is negative. When the solver is facing the original preferential direction again, and the angular sum of the turns made is 0, the solver leaves the obstacle and continues moving in its original direction.

The hand is removed from the wall only when both "sum of turns made" and "current heading" are at zero. This allows the algorithm to avoid traps shaped like an upper case letter "G". Assuming the algorithm turns left at the first wall, one gets turned around a full degrees by the walls.Rat in a maze is also one popular problem that utilizes backtracking. If you want to brush up your concepts of backtracking, then you can read this post here.

You can also see this post related to solving a Sudoku using backtracking. A maze is a 2D matrix in which some cells are blocked. One of the cells is the source cell, from where we have to start. And another one of them is the destination, where we have to reach.

Python Path Finding Tutorial - Breadth First Search Algorithm

We have to find a path from the source to the destination without moving into any of the blocked cells. A picture of an unsolved maze is shown below. To solve this puzzle, we first start with the source cell and move in a direction where the path is not blocked. If taken path makes us reach to the destination then the puzzle is solved else, we come back and change our direction of the path taken. We are going to implement the same logic in our code also. So, let's see how. You know about the problem, so let's see how we are going to solve it.

Firstly, we will make a matrix to represent the maze, and the elements of the matrix will be either 0 or 1. The matrix for the maze shown above is:. Now, we will make one more matrix of the same dimension to store the solution. Its elements will also be either 0 or 1. The matrix representing the solution is:. Thus, we now have our matrices.

Next, we will find a path from the source cell to the destination cell and the steps we will take are:. Thus, the summary is that we try to move to the other cell down, right, up, and left from the current cell and if no movement is possible, then just come back and change the direction of the path to another cell. If it is the destination cell then our puzzle is already solved.

If not, then we are checking if it a valid cell to move or not. A valid cell must be in the matrix i. If it is a valid move then we are free to take it and move to the next cell. If it doesn't give us the solution then we will move to the rightward cell, and similarly to the upward and the leftward cells.

April 9, 2037

Algorithm to solve a rat in a maze You know about the problem, so let's see how we are going to solve it. The matrix for the maze shown above is: 0 1 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 Now, we will make one more matrix of the same dimension to store the solution. The matrix representing the solution is: 1 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 Thus, we now have our matrices.

Next, we will find a path from the source cell to the destination cell and the steps we will take are: Check for the current cell, if it is the destination cell, then the puzzle is solved. If not, then we will try to move downward and see if we can move in the downward cell or not to move in a cell it must be vacant and not already present in the path. If we can move there, then we will continue with the path taken to the next downward cell.

If not, we will try to move to the rightward cell. And if it is blocked or taken, we will move upward. Similarly, if we can't move up as well, we will simply move to the left cell.

If none of the four moves down, right, up, or left are possible, we will simply move back and change our current path backtracking.For a maze generated by this taskwrite a function that finds and displays the shortest path between two cells. Note that because these mazes are generated by the Depth-first search algorithm, they contain no circular paths, and a simple depth-first tree search can be used. The maze is read from the standard input. Generator and solver combined.

The generator is the same found in Maze generation. Run it. When the path splits each possibility gets its own process that checks if it is the correct one or a dead end. The following code works with the solution from Maze Generation. The following Unicon-only solution is a variant of the above. It shares the same maze generation code and maze display code with the above but spawns threads to parallelize the searching.

The algorithm runs each path to a dead end, a target, or a length greater than the current shortest path to a target and works if there are multiple target cells, multiple paths to those targets, or cyclic paths. The shortest solution path is then marked and displayed. Due to reports that the program failed, the generation and solver are shown together. The display verb was extended to include a dyadic definition. The complete program was tested with j 8. Animated: generating and solving. To start solving, click to choose a 'start' and an 'end' points. Go here to see it in action. Solution generated by this program when passed maze. Using the maze produced in Maze generation PicoLispthis finds the shortest path from the top-left cell 'a8' to the bottom-right exit 'k1':.