Like Path

Like path research focuses on efficiently and effectively determining optimal routes in various contexts, from navigating complex environments to planning robot trajectories. Current research emphasizes developing robust algorithms, such as those based on density estimation with normalizing flows, graph-based planning with equitable workload distribution, and tangent-based methods for generating topologically diverse paths. These advancements aim to improve pathfinding in high-dimensional spaces, non-convex environments, and scenarios lacking explicit road structures, with applications ranging from robotics and autonomous vehicle navigation to data analysis and machine learning.