Trajectory Tracking
Trajectory tracking, the precise following of a predefined path by a system, is a crucial problem across robotics and autonomous systems, aiming to improve accuracy, robustness, and efficiency. Current research emphasizes developing advanced control strategies, such as nonlinear model predictive control (NMPC) and adaptive control methods, often integrated with machine learning techniques like deep reinforcement learning (DRL) and neural networks, to handle complex dynamics and uncertainties. These advancements are driving improvements in applications ranging from unmanned aerial vehicles (UAVs) and autonomous driving to soft robotics and assistive devices, impacting fields like precision agriculture, search and rescue, and healthcare.
Papers
Model Predictive Loitering and Trajectory Tracking of Suspended Payloads in Cable-Driven Balloons Using UGVs
Julius Wanner, Eric Sihite, Alireza Ramezani, Morteza Gharib
Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
Alessandro Saviolo, Guanrui Li, Giuseppe Loianno