From Ceilings to Walls: Universal Dynamic Perching of Small Aerial Robots on Surfaces with Variable Orientations

TL;DR

This paper presents a universal dynamic perching method for small aerial robots on surfaces with various orientations, using a combination of non-dimensionalization and deep reinforcement learning to optimize landing performance across different scales.

Contribution

It introduces a systematic framework for understanding and achieving consistent perching behavior across robot sizes and surface orientations, validated through simulations and experiments.

Findings

Geometric scaling ensures consistent perching behavior.

Joint damping ratios affect landing success under vertical approaches.

A critical velocity threshold is necessary for successful perching.

Abstract

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.