python package for fast shortest path computation on 2D polygon or grid maps

Implementation of common pathfinding algorithms

Pathfinder written in Python

👓 Visualization of the Breadth First Search pathfinding algorithm

djikstra's pathfinding algorithm visualized in python

[Rebuild] Snake Remake 🐍 is an upgraded edition of my past project, 'Unconventional Snake Xenia.' It's an AI-driven snake game featuring a custom neural network. 🧠 The menu-driven interface offers visualizations of different pathfinding algorithms! 🎮

Console utility to find path on RGB image

Python implementation of common pathfinding algorithms in 3D grid space

A Bresenham's line based global path planning algorithm. A recursive path planning algorithm was developed that operates on the grid maps represented by a masked array and solves potential looping problems using a state machine-based loop breaking mechanism.

Explore the fascinating world of pathfinding algorithms with this interactive Pathfinding Visualizer built in Python using Pygame. This project provides a graphical representation of pathfinding algorithms on a grid, allowing users to experiment, visualize, and gain insights into the efficiency of different algorithms.

Provides visualizations of various pathfinding algorithms implemented in Python and Pygame.

This is a project to see 4 different algorithms for pathfinding, and study them.

Exploring path sequences in GA4 BigQuery data

Multi-Agent Pathfinding using A* in Conflict Based Search algorithm

Path of least action analysis on energy landscapes

A-star pathfinding algorithm used to create shortest road finding method

AI plays snake game using BFS (Breadth-First Search) algorithm.

SIMROUTE: Weather Ship Routing (WSR) Code. The software is constructed considering available Copernicus Marine Environment Monitoring Service (CMEMS) wave predictions systems of free use.

Simple Python project that uses standard libraries in conjunction with PyGame for the GUI. Allows the user to create a maze using their mouse, define starting position [S_i,S_j], goal position [G_i,G_j], and run different pathfinding algorithm visualizations from informed (Greedy, A*) to uninformed (DFS,BFS) and choose between L1 and L2 heuristics