Path Planner

Path planning algorithms aim to efficiently compute collision-free routes for robots or autonomous agents navigating complex environments, often incorporating constraints like speed, energy consumption, and adherence to rules. Current research emphasizes improving robustness and efficiency in challenging scenarios, focusing on methods like hybrid A*, deep reinforcement learning integrated with classical planners, and novel sampling techniques that reduce wasted computation in unknown spaces. These advancements are crucial for enabling safe and effective autonomous navigation in diverse applications, ranging from robotics and autonomous driving to agricultural vehicles and medical procedures.