Dynamic Target

Dynamic target tracking and control is a rapidly evolving field focused on developing algorithms and systems capable of reliably interacting with moving targets in diverse environments. Current research emphasizes robust methods for predicting target motion, incorporating techniques like Linear Quadratic Regulators (LQR), model predictive control (MPC), and neural networks (including transformers and neural radiance fields) to achieve accurate and efficient tracking, even under conditions of high uncertainty or occlusion. These advancements have significant implications for various applications, including autonomous robotics (e.g., grasping, aerial chasing, and off-road driving), missile guidance, and cooperative multi-agent systems.