From Ceilings to Walls: Universal Dynamic Perching of Small Aerial Robots on Surfaces with Variable Orientations
This work addresses the challenge of drone autonomy in unstructured environments by advancing perching capabilities, though it is incremental as it builds on existing perching research with a focus on scaling and mechanical design effects.
The study tackled the problem of enabling small aerial robots to dynamically perch on surfaces with variable orientations, achieving universal perching capabilities across different robot sizes and surface angles by validating that maintaining geometric proportions ensures consistent behavior, with a critical velocity threshold identified for success.
This work demonstrates universal dynamic perching capabilities for quadrotors of various sizes and on surfaces with different orientations. By employing a non-dimensionalization framework and deep reinforcement learning, we systematically assessed how robot size and surface orientation affect landing capabilities. We hypothesized that maintaining geometric proportions across different robot scales ensures consistent perching behavior, which was validated in both simulation and experimental tests. Additionally, we investigated the effects of joint stiffness and damping in the landing gear on perching behaviors and performance. While joint stiffness had minimal impact, joint damping ratios influenced landing success under vertical approaching conditions. The study also identified a critical velocity threshold necessary for successful perching, determined by the robot's maneuverability and leg geometry. Overall, this research advances robotic perching capabilities, offering insights into the role of mechanical design and scaling effects, and lays the groundwork for future drone autonomy and operational efficiency in unstructured environments.