In this challenge, you will implement a maze solver in Rust. A maze can be represented as a grid where each cell is either an open path or a wall. The goal is to navigate from a starting point to an ending point using the shortest path possible. This problem will test your understanding of control flow in Rust, including loops, conditionals, and possibly recursion.

Your task is to write a function solve_maze that takes a 2D vector of characters representing the maze and two tuples representing the start and end points. The maze will be given as a grid of characters where 'S' represents the start, 'E' represents the end, '.' represents an open path, and '#' represents a wall.

The function should return a vector of tuples representing the path from start to end if a path exists, or an empty vector if no path exists.

• The maze will always contain exactly one 'S' and one 'E'.
• You can only move up, down, left, or right.
• Implement the function using appropriate control flow constructs (loops, conditionals, and/or recursion).
• Ensure your solution handles edge cases, such as no available path or mazes with various sizes.
• If the maze is a dead-end, return an empty vector.
• If the maze has multiple solutions, return the shortest path.

let maze = vec![
    vec!['S', '.', '#', '#', '#'],
    vec!['#', '.', '#', '.', '.'],
    vec!['#', '.', '.', '.', '#'],
    vec!['#', '#', '#', '.', '#'],
    vec!['#', '#', '#', 'E', '#']
];
let start = (0, 0);
let end = (4, 3);
 
let path = solve_maze(maze, start, end);
assert_eq!(
    path,
    vec![
        (0, 0), // starting point
        (0, 1), // right
        (1, 1), // down
        (2, 1), // down
        (2, 2), // right
        (2, 3), // right
        (3, 3), // down
        (4, 3) // down
    ]
);

In the example above, we start from 'S' at (0, 0), the first move would be going to the right (0, 1), then down (1, 1), and so on until we reach the end at (4, 3).

Did You Know?

Maze solving algorithms are not just for games. They are used in robotics for pathfinding, in computer networks for routing, and in various optimization problems. Algorithms such as Depth-First Search (DFS), Breadth-First Search (BFS), and A* are commonly used to solve these problems efficiently.

1. Collections:

   • VecDeque: A double-ended queue from the std::collections module, which is useful for implementing a queue for BFS. Methods like push_back and pop_front will be helpful.

2. Indexing:

   • Use usize for indices and be cautious with arithmetic operations to avoid overflow. The wrapping_add method can help with safe arithmetic.

3. 2D Vector Initialization:

   • Initialize 2D vectors for visited and path tracking. Use nested vec! macros for creating the initial structure.

4. Backtracking Path:

   • Once the end point is reached, backtrack using the path vector to reconstruct the path from end to start. Collect these coordinates in a vector and reverse it to get the path from start to end.

5. Boundary Checks:

   • Ensure the new coordinates are within the maze boundaries and check if the cell is a wall ('#') or already visited.