Continuous Time Trajectory

Continuous-time trajectory research focuses on generating and optimizing smooth, dynamically feasible paths for various applications, from robotics and autonomous driving to traffic monitoring and scientific simulations. Current efforts concentrate on developing algorithms that satisfy complex constraints (e.g., collision avoidance, energy efficiency, adherence to temporal logic specifications), often employing techniques like Bézier curves, reinforcement learning, and Bayesian filtering within frameworks such as Hamiltonian neural networks and Poisson multi-Bernoulli mixture filters. This work is crucial for advancing robotics, autonomous systems, and scientific modeling by enabling more robust, efficient, and safe control of dynamic systems.