Sampling Based Path

Sampling-based path planning aims to efficiently find optimal or near-optimal paths in complex environments, primarily by iteratively sampling the search space. Current research focuses on improving the efficiency and optimality of these methods, employing techniques like neural networks (e.g., generative adversarial networks and control barrier function-based controllers) to guide sampling and enhance convergence speed, as well as adaptive sampling strategies that balance exploration and exploitation. These advancements are crucial for real-time applications in robotics and autonomous systems, enabling faster and more reliable path planning in challenging scenarios.