Path Finding Algorithm

Pathfinding algorithms seek to efficiently determine optimal routes between points, avoiding obstacles and considering various constraints. Current research focuses on improving the speed and efficiency of classic algorithms like A*, often by integrating them with machine learning models (e.g., using LLMs for global context or neural networks to learn heuristics) or developing novel approaches like RRT variants for multi-goal scenarios. These advancements are crucial for applications ranging from robotics and autonomous navigation to optimizing public transportation systems and improving the performance of AI agents in dynamic environments.