Trajectory Smoothing

Trajectory smoothing aims to generate smoother, more efficient, and safer robot movements or data representations by mitigating abrupt changes and inconsistencies. Current research focuses on developing robust algorithms, such as model predictive control (MPC) and factor graph optimization (FGO), often integrated with other techniques like neural networks or probabilistic methods, to achieve real-time smoothing across diverse applications including robotics, autonomous driving, and video stabilization. These advancements are crucial for improving robot performance, enhancing the safety and reliability of autonomous systems, and enabling more natural human-robot interaction.