Grid Based A* Algorithm

Key Components:

• Binary Heap: A custom binary heap implementation is used to efficiently store nodes in a priority queue-like structure, ensuring nodes with lower total cost (f) are given higher priority.
• Node Representation: Each cell in the grid is represented as a node containing information about its position, cost from the start (g), heuristic cost to the goal (h), and parent node.
• Heuristic: Euclidean distance is used as the heuristic function to estimate the cost from each node to the goal, guiding the search towards the target efficiently.
• Valid Movement: The algorithm considers four possible movements - up, down, left, and right, with diagonal movements omitted for simplicity.
• Obstacle Avoidance: Obstacles in the grid are represented as blocked cells, and the algorithm avoids expanding nodes that correspond to obstacles.
• Path Reconstruction: Once the goal node is reached, the algorithm reconstructs the shortest path by tracing back through the parent nodes from the goal to the start.

Usage:

The algorithm takes as input a grid representing the environment, start and end points, and returns a list of coordinates representing the shortest path from the start to the end. It employs a systematic exploration strategy, expanding nodes with the lowest estimated total cost until the goal is reached or all possible paths are explored.

Benefits:

• Efficiently finds the shortest path in a grid-based environment.
• Suitable for applications requiring path planning in robotics, video games, and navigation systems.

Limitations:

• Assumes movement is restricted to four cardinal directions.
• Performance may degrade in large-scale grids or environments with complex obstacles.

Applications:

• Robotic navigation in warehouses, factories, and outdoor environments.
• Video game AI for character movement and navigation.
• Routing algorithms in transportation and logistics systems.

Test Results:

Test Case 1

Test Case 2

Test Case 3

Test Case 4

Test Case 5