Trajectory Sampling

Trajectory sampling is a computational technique focused on efficiently generating and evaluating a set of possible future paths (trajectories) for a system, often used in robotics, autonomous driving, and generative modeling. Current research emphasizes improving sampling efficiency and effectiveness through methods like informed RRT*, model-based risk minimization, and the integration of probabilistic models (e.g., Gaussian processes, Bayesian neural networks, deep Gaussian covariance networks) to better handle uncertainty and improve decision-making. These advancements are significantly impacting fields like autonomous navigation, where safer and more efficient path planning is crucial, and generative AI, where high-fidelity model generation is enhanced by deterministic sampling strategies.