Sampling Based Path Planning

Sampling-based path planning aims to efficiently find optimal or near-optimal paths for robots navigating complex environments, focusing on speed and robustness. Current research emphasizes improving the efficiency of algorithms like RRT* and PRM*, often incorporating techniques like hierarchical planning, neural network-based heuristics to guide sampling, and bidirectional search strategies to accelerate convergence. These advancements are crucial for enabling autonomous navigation in dynamic and cluttered environments, with applications ranging from industrial robotics to autonomous aerial vehicles.