Manually Selecting Multiple Obstacle Regions on Grid Maps and Planning Optimal Paths Using A* Algorithm (AStar)

Path planning on grid maps often requires manually selecting multiple obstacle regions and applying the A* algorithm to find optimal paths. Grid maps partition environments into uniform grid cells, where certain cells are marked as obstacles and become impassable.

### Implementation Approach

Manual Obstacle Selection Users can select multiple regions on grid maps through interactive interfaces (such as mouse clicks or drag operations) to mark obstacles. These obstacles can assume arbitrary shapes, typically represented as rectangular or polygonal regions. In code implementation, this involves capturing mouse coordinates and converting them to grid indices using coordinate transformation functions.

Grid Map Construction Grid maps are stored using two-dimensional arrays, where each cell's state indicates either passable or impassable (obstacle). Manually selected obstacles are marked as blocked states in corresponding array positions. The implementation typically uses a matrix where 0 represents free space and 1 indicates obstacles, with boundary checking functions ensuring valid coordinates.

A* Algorithm Path Planning A* is a heuristic search algorithm that combines Dijkstra's shortest path strategy with heuristic evaluation functions to efficiently find optimal paths from start to finish. Key implementation components include: Open and Closed Lists: Data structures storing nodes to be explored and already explored nodes, often implemented using priority queues for efficient node retrieval. Cost Calculation: Integrates actual movement cost (G-value) and heuristic estimated cost (H-value) to determine search direction. The F-value calculation (F = G + H) drives node prioritization. Path Backtracking: Upon reaching the destination, the algorithm traces back through parent nodes to form the complete path. This involves maintaining parent pointers for each node during the search process.

Optimization and Enhancements Dynamic Obstacle Adjustment: Allows runtime modification of obstacles and path replanning, requiring efficient map update functions and incremental search techniques. Heuristic Function Selection: Options include Manhattan distance for 4-directional movement or Euclidean distance for 8-directional movement, implemented through distance calculation functions that accommodate different movement constraints.

By combining interactive obstacle configuration with A* search, this approach flexibly simulates path planning requirements for various scenarios, making it suitable for applications like game AI and robot navigation systems. The implementation typically involves visualization components to display the grid, obstacles, and computed path in real-time.