Forward Dynamic

Forward dynamics modeling focuses on predicting the future state of a dynamic system (e.g., robot, satellite) given its current state and applied forces. Current research emphasizes developing accurate and efficient forward dynamics models for diverse systems, employing techniques like neural networks (e.g., biLSTMs, Neural Processes), meshfree methods, and analytical approaches tailored to specific robot architectures (e.g., parallel-serial manipulators). These advancements are crucial for improving control algorithms (e.g., model predictive control), enabling safe and efficient autonomous navigation, and facilitating realistic simulations of complex interactions, such as on-orbit satellite servicing.