I built this little simulation in Python with Pygame to visualize how a robot can navigate a maze. The core of the program is an A* search on a grid where each cell represents a step of the robot’s path; walls are drawn as rectangles that block movement, and I let the user click to set a start point, end point, and drag to create obstacles. Once a path is found with astar, the code converts the sequence of cells into compact instructions like “MOVE RIGHT x3” or “DOWN x2”, which can be fed into an actual robot controller. The algorithm uses Manhattan distance as a heuristic, and a priority queue (heapq) ensures optimality by exploring neighbors in order of estimated cost. I’ve kept the implementation modular so it could easily be wrapped into ROS: one could publish the computed nav_msgs/Path or even the raw instruction list to a custom node that interprets them into motor commands.