Trouble with A* Algorithm Heuristic in Python - Underestimating Cost on Certain Nodes
I'm having a hard time understanding I've hit a wall trying to I'm upgrading from an older version and I need some guidance on This might be a silly question, but I'm implementing the A* search algorithm for a pathfinding application using Python, and I've run into an issue where the heuristic function seems to underestimate the cost of certain nodes, leading to suboptimal paths....... The heuristic I'm using is the Euclidean distance, and I've set it up as follows: ```python import math def heuristic(a, b): return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) ``` While this works well in most cases, I'm observing that in a grid with obstacles, my algorithm sometimes chooses a path that is longer than expected. For instance, when navigating around certain obstacles, the priority queue seems to favor nodes that are further away from the target because they have a lower cost estimate. I’ve tried adjusting the heuristic by adding a weight to it, like this: ```python def weighted_heuristic(a, b, weight=1.5): return weight * heuristic(a, b) ``` However, that only seems to worsen the situation. My current implementation of the A* algorithm is as follows: ```python from queue import PriorityQueue def a_star(start, goal, grid): open_set = PriorityQueue() open_set.put((0, start)) came_from = {} g_score = {start: 0} f_score = {start: heuristic(start, goal)} while not open_set.empty(): current = open_set.get()[1] if current == goal: return reconstruct_path(came_from, current) for neighbor in get_neighbors(current, grid): tentative_g_score = g_score[current] + 1 if tentative_g_score < g_score.get(neighbor, float('inf')): came_from[neighbor] = current g_score[neighbor] = tentative_g_score f_score[neighbor] = g_score[neighbor] + heuristic(neighbor, goal) if neighbor not in [i[1] for i in open_set.queue]: open_set.put((f_score[neighbor], neighbor)) return False ``` I’ve also checked that my `get_neighbors` function correctly returns valid adjacent nodes. I’m running this on Python 3.9 and have tested it against various grid configurations, but my results still show that the path taken is often longer than necessary due to heuristic misestimations. Has anyone experienced similar issues or can suggest a better heuristic function or approach to ensure more accurate pathfinding? I'm working on a CLI tool that needs to handle this. What am I doing wrong? This is part of a larger CLI tool I'm building. What's the best practice here? I'm coming from a different tech stack and learning Python. I'd really appreciate any guidance on this. This issue appeared after updating to Python latest. Has anyone else encountered this? My development environment is Ubuntu 20.04. What's the best practice here?