Path Tracking

Path tracking focuses on guiding a system, such as a robot or autonomous vehicle, along a predefined trajectory, optimizing for speed, safety, and accuracy. Current research emphasizes robust algorithms that handle uncertainties in environment perception and system dynamics, employing techniques like model predictive control (MPC), extended Kalman filters (EKFs), and reinforcement learning (RL) to achieve this. These advancements are crucial for applications ranging from autonomous driving and robotics to human-robot collaboration, improving efficiency and safety in diverse contexts. The integration of machine learning, particularly neural networks like LSTMs, is a significant trend, enabling adaptive control and real-time performance in complex scenarios.