天井から壁まで：向きの異なる面への小型飛行ロボットの汎用的動的止まり
From Ceilings to Walls: Universal Dynamic Perching of Small Aerial Robots on Surfaces with Variable Orientations
深層強化学習と無次元化により、様々なサイズのクアッドロータが異なる向きの面に動的に止まる能力を解析し、機体スケールと面の向きが着地性能に与える影響を実験で検証した。
著者: Bryan Habas, Aaron Brown, Donghyeon Lee, Mitchell Goldman, Bo Cheng
分類: cs.RO, cs.LG
原文アブストラクト
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.
