Path Planning

Path planning focuses on finding optimal routes between points, avoiding obstacles, and satisfying various constraints, crucial for robotics, autonomous vehicles, and logistics. Current research emphasizes efficient algorithms like rapidly-exploring random trees (RRT) and probabilistic roadmaps (PRM), incorporating advanced techniques such as centroidal Voronoi tessellation, diffusion models, and large language models (LLMs) for improved performance and adaptability in complex, dynamic environments. These advancements are driving progress in areas like multi-robot coordination, robust navigation in uncertain conditions, and the integration of AI for more intelligent and efficient pathfinding in real-world applications.