Two-Dimensional Path Planning Algorithm Based on Ant Colony Optimization

The code includes detailed explanations, but beginners may require additional background knowledge to fully grasp its implementation. To facilitate understanding, I'll share key technical insights: the algorithm utilizes pheromone trail updating and heuristic information to guide artificial ants toward optimal paths through probabilistic node selection. Each ant maintains a path memory to avoid revisiting nodes, while evaporation mechanisms prevent premature convergence. The implementation features dynamic parameter adjustment for balancing exploration and exploitation. We've conducted rigorous testing to verify the code's correctness and reliability, ensuring stable performance across various grid-based environments. You can confidently deploy this solution without concerns about operational issues. In summary, this practical implementation offers both robust functionality and user-friendly integration capabilities for path planning applications.