Minimal Path

Minimal path research focuses on finding the most efficient or optimal path between two points, often subject to constraints like obstacles, resource limitations, or dynamic system properties. Current research emphasizes developing efficient algorithms, such as A*, Large Neighborhood Search, and various adaptations of minimal path methods combined with neural networks, to solve these problems for diverse applications, including multi-robot coordination, coverage planning, and trajectory optimization for robots and autonomous vehicles. These advancements improve the speed and quality of path planning, impacting fields like robotics, autonomous systems, and logistics by enabling more efficient and robust navigation and task execution.