Stochastic Trajectory

Stochastic trajectory research focuses on modeling and predicting the paths of dynamic systems under uncertainty, aiming to generate reliable and efficient trajectories for various applications. Current research emphasizes continuous-time representations using models like Gaussian processes, neural ordinary differential equations, and B-splines, often incorporating control inputs and addressing challenges like incomplete data and robustness to disturbances. These advancements are significant for improving robot navigation, motion planning, and state estimation in robotics and other fields requiring accurate prediction and control of dynamic systems.