Efficient Path Planning

Efficient path planning aims to find optimal routes for robots and autonomous agents, minimizing factors like distance, time, energy consumption, and risk. Current research emphasizes robust algorithms like rapidly-exploring random trees (RRT) and their variants, deep learning models for collision probability estimation, and multi-agent reinforcement learning for coordinated navigation. These advancements are crucial for improving the safety and efficiency of autonomous systems in diverse applications, from robotic surgery and autonomous driving to planetary exploration and warehouse automation.