Reading
A path planning teaching and experiment platform implemented purely in Python, supporting the conversion of any image into a navigable grid, and using eight AI algorithms (BFS, DFS, A*, MDP, Q-Learning, neural networks, etc.) for real-time solving and visual comparison.
Section 01
A path planning teaching and experiment platform implemented purely in Python, supporting the conversion of any image into a navigable grid, and using eight AI algorithms (BFS, DFS, A*, MDP, Q-Learning, neural networks, etc.) for real-time solving and visual comparison.
Section 02
Section 03
Vision-Path-Planner is an AI path planning experiment platform built from scratch. Its core capability is to automatically convert any image—whether it's a hand-drawn maze, architectural floor plan, 3D rendered scene, or satellite map—into a navigable grid map, and use eight different AI algorithms for path solving and visual comparison.
The unique feature of this project is its full independent implementation: no existing path planning libraries or AI frameworks are used; all algorithms, neural networks, and image processing logic are handwritten by the developers. This makes it an excellent teaching tool for learning path planning algorithms and reinforcement learning.
Section 04
The system can automatically process four types of input images without manual configuration:
Section 05
Converts black-and-white maze images into binary grids through threshold segmentation and morphological cleaning.
Section 06
Uses HSV color space recognition: colored devices are considered obstacles, dark walls block passage, and light-colored floors are walkable areas. This processing method is particularly suitable for indoor navigation and robot path planning scenarios.
Section 07
Extracts passable roads from satellite images using low-saturation road detection combined with adaptive thresholds.
Section 08
Intelligent cropping, boundary removal, and automatic color inversion processing, compatible with map formats commonly used in robot systems like ROS.
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