๐ค AI Summary
This work proposes HoLoArm, a quadrotor drone with flexible arms inspired by dragonfly wing nodus structures, to address safety and stability challenges posed by collisions in dense human environments. The design uniquely integrates bioinspired compliant mechanisms with reinforcement learningโbased control, enabling omnidirectional passive deformation and exceptional collision resilience. Through collision dynamics modeling, tailored flexible arm design, and comprehensive experimental validation, HoLoArm demonstrates rapid recovery to stable hover within 0.3โ0.6 seconds after impact, withstands collision speeds up to 7.6 m/s, and supports a payload of 540 g. This study establishes a foundational step toward fully soft-bodied aerial robots capable of robust operation in complex, dynamic environments.
๐ Abstract
The increasing use of drones in human-centric applications highlights the need for designs that can survive collisions and recover rapidly, minimizing risks to both humans and the environment. We present HoLoArm, a quadrotor with compliant arms inspired by the nodus structure of dragonfly wings. This design provides natural flexibility and resilience while preserving flight stability, which is further reinforced by the integration of a Reinforcement Learning (RL) control policy that enhances both recovery and hovering performance. Experimental results demonstrate that HoLoArm can passively deform in any direction, including axial one, and recover within 0.3-0.6 s depending on the direction and level of the impact. The drone can survive collisions at speeds up to 7.6 m/s and carry a 540 g payload while maintaining stable flight. This work contributes to the morphological design of soft aerial robots with high agility and reliable safety, enabling operation in cluttered and human shared environments, and lays the groundwork for future fully soft drones that integrate compliant structures with intelligent control.