Quadrotor Dynamic

Quadrotor dynamic modeling focuses on accurately predicting and controlling the complex, nonlinear behavior of these aerial robots, aiming to improve performance, robustness, and safety in diverse environments. Current research emphasizes data-driven approaches, employing techniques like neural networks (including diffusion models, recurrent networks like LSTMs, and convolutional networks), Gaussian processes, and reinforcement learning algorithms (e.g., actor-critic methods) to learn quadrotor dynamics from real-world flight data, often incorporating physics-informed constraints. These advancements are crucial for enabling more agile, robust, and autonomous quadrotor flight, with applications ranging from autonomous navigation in complex environments to high-speed maneuvers and precise trajectory tracking. The development of efficient and accurate models is key to unlocking the full potential of these versatile robots.