Machine learning-based framework for wall-perching prediction of flying robot

In contrast to the diverse landing solutions that the animals have in nature, human-made aircraft struggle when it comes to perching on vertical walls. However, traditional dynamic simulations and experiments lack the high efficiency required to analyze the perching and design the robot. This paper develops an efficient machine learning framework to predict vertical-wall perching success for flying robots with spines, overcoming traditional methods' inefficiency. A validated knowledge-based model computes the robot's transient dynamics during high-speed perching, identifying key success factors. By training the mixed sample data, a data-driven model has been proposed to predict the success or failure of an arbitrary perching event. Here, we show that this high-precision prediction optimizes robot control and structural parameters, ensuring stable perching while drastically reducing the time and cost of conventional design approaches, advancing the flying robot capabilities.