Complex Trajectory

Complex trajectory research focuses on efficiently planning, optimizing, and analyzing intricate movement paths in diverse systems, from robots navigating complex environments to biomolecules undergoing conformational changes. Current efforts concentrate on developing novel algorithms, including those based on neural networks (e.g., NeRF, graph neural networks), variational methods, and physics-informed learning, to improve trajectory generation, prediction, and analysis. These advancements are crucial for enhancing the performance of autonomous systems, improving the understanding of complex biological processes, and enabling more accurate reconstruction of dynamic scenes from limited data. The resulting improvements in trajectory modeling have significant implications across robotics, computer vision, and biophysics.