Pathfinding

This project implements several pathfinding algorithms used to navigate a grid-based game board. It emphasizes demonstrating how these algorithms explore and compute the most efficient paths between a starting point and a goal. The project provides a visual representation of the board, allowing users to observe the real-time process as the algorithms search for paths, evaluate nodes, and make decisions. Designed for educational purposes, it can also be used in game development or AI simulations.

Algorithms

The project includes the following algorithms:

1. Depth-First Search (DFS)
2. Breadth-First Search (BFS)
3. Bidirectional BFS
4. Dijkstra
5. AStarDijkstra (Combination of AStar and Dijkstra)
6. AStar with different heuristic types:
   1. Manhattan
   2. Euclidean
   3. Diagonal Chebyshev
   4. Diagonal Octile

Visualization

• The board features walls (dark blocks) and floors (light-colored blocks).
• The character is represented as a blue square, while the goal is indicated by a pink square.
• Squares considered during the pathfinding process.
• Squares selected as the final route to reach the goal.

Customization

Customizations can be made to instances without modifying the code.

Character

• Movement Speed
• Starting position (if the starting position is a wall, the game will stop, and an error message will be displayed).
• Delay between consecutive pathfinding calculations.
• Ability to control the character: when the keyboardControl boolean is activated, the path disappears. If deactivated, path generation restarts from the character's current position.

PathFindingAlgorithm

• A GameObject to which you can attach the desired pathfinding algorithm script.

Loader

• Seed for generating different maps.
• Number of columns and rows of the board.
• Minimum and maximum number of walls.
• Prefabs for walls, floor, and goal.

Preview - AStar