Feasible Path

Feasible path planning aims to find collision-free and kinematically valid paths for robots or other agents navigating complex environments, often under time or resource constraints. Current research emphasizes efficient algorithms, including those based on mixed-integer linear programming, sampling-based methods (like RRT and its variants), and learned approaches using diffusion models or neural networks for path prediction and optimization. These advancements are crucial for improving the autonomy and reliability of robots in diverse applications, such as autonomous driving, aerial navigation, and multi-robot coordination.