Sampling Based Trajectory

Sampling-based trajectory planning focuses on efficiently generating feasible and optimal paths for robots and autonomous systems navigating complex environments, prioritizing safety and efficiency. Current research emphasizes improvements to algorithms like RRT and its variants, often incorporating hierarchical structures, multi-objective optimization, and informed sampling techniques to enhance speed and robustness in dynamic settings. These advancements are crucial for enabling safe and reliable autonomous navigation in applications such as autonomous driving, UAV formation flight, and robotic manipulation, particularly in cluttered or unpredictable scenarios. The resulting improvements in computational efficiency and path quality directly impact the feasibility and performance of autonomous systems.