A data-driven control method for ground locomotion on sloped terrain of a hybrid aerial-ground robot

In this work, we present a data-driven solution for the attitude control of DoubleBee on slopes. DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels. Inspired by the physics modeling of the system, we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects, motor torques and rotation axis shift. The proposed neural network is Lipschitz continuous, has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network. Then, we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds. Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network, and a 40% reduction in tracking error compared to a baseline PID controller.