Deep Learning a Quadrotor Dynamic Model for Multi-Step Prediction

Abstract

We develop a multi-step motion prediction modeling method for dynamic systems over long horizons using deep learning. Building on previous work, we propose a novel hybrid network architecture, by combining deep recurrent neural networks with a quadrotor motion model created using classic system identification methods. The proposed model takes only the initial system state and motor speeds over the prediction horizon as inputs and returns robust state predictions for up to two seconds of motion at 100 Hz. We employ recurrent neural network state initialization during training, to exploit real-world dataset collected from quadrotor vehicle flights in an indoor flight arena. Our experiments demonstrate that the proposed hybrid network model consistently outperforms both black box and rigid body dynamics predictions over single and multi-step prediction scenarios, with an order of magnitude improvements in velocity estimates in particular.