Predictive Path

Predictive path planning focuses on generating optimal trajectories for autonomous systems, primarily robots and vehicles, by predicting future states and optimizing control actions. Current research emphasizes integrating stochastic model predictive control (MPPI), often enhanced by machine learning techniques like Gaussian processes and normalizing flows, to handle uncertainties and complex environments. These advancements improve robustness and efficiency, particularly in challenging scenarios such as autonomous racing and navigation in cluttered spaces. The resulting improvements in path planning have significant implications for robotics, autonomous driving, and other fields requiring safe and efficient navigation.