HoLoArm: Deformable Arms for Collision-Tolerant Quadrotor Flight
- URL: http://arxiv.org/abs/2605.25790v1
- Date: Mon, 25 May 2026 12:37:18 GMT
- Title: HoLoArm: Deformable Arms for Collision-Tolerant Quadrotor Flight
- Abstract summary: We present HoLoArm, a quadrotor with compliant arms inspired by the nodus structure of dragonfly wings.<n>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.<n>Drone can survive collisions at speeds up to 7.6 m/s and carry a 540 g payload while maintaining stable flight.
- Score: 46.42648821424623
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Effector-Centric NMPC of Tiltable-Multirotors for Offset-Free Omnidirectional Aerial Manipulation [9.325443556436436]
This work presents a design analysis and a wrench-based control framework for tiltable-multirotors in aerial manipulation.<n>A four-rotor tiltable configuration provides a balance between interference-free propeller sizing and hovering efficiency.<n>We develop an effector-centric nonlinear model predictive control framework that integrates design choices, handling, and disturbance compensation.
arXiv Detail & Related papers (2026-08-18T14:18:48Z) - MorphQuad: Morphable Quadrotor for Superhuman Maneuverability, Manipulation, and Resiliency [0.0]
Infrastructure maintenance, contact-based inspection, and emergency response can benefit from aerial vehicles that act as a flying human hand.<n>MMR: maneuverability to fly in arbitrary orientations to reach remote and tight locations.<n>We present MorphQuad: a morphable quadrotor that enjoys MMR.
arXiv Detail & Related papers (2026-07-02T21:01:26Z) - Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls [57.166020875486474]
We propose textbfAero-World, a method for converting a pretrained image-to-video diffusion model into a controllable aerial video generator.<n>A frozen latent-space Physics Probe, trained independently on real video--IMU pairs, provides differentiable inertial-consistency supervision during LoRA finetuning.<n>On AeroBench, Aero-World improves mean AAS from 57.7 to 63.6 over action-only finetuning and gives a stronger quality-control trade-off than AirScape.
arXiv Detail & Related papers (2026-05-19T12:02:17Z) - Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching [77.28042137892943]
We present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour.<n>We train motion-tracking reinforcement learning expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy.<n>We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot.
arXiv Detail & Related papers (2026-02-17T18:59:11Z) - A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
arXiv Detail & Related papers (2026-02-06T15:58:50Z) - Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
arXiv Detail & Related papers (2025-12-08T11:42:41Z) - Wake Vectoring for Efficient Morphing Flight [1.6799377888527687]
We introduce a passive wake vectoring mechanism that recovers lost thrust during morphing.<n>This solution achieves up to a 40% recovery of vertical thrust in configurations where no useful thrust would otherwise be produced.<n>Our findings highlight a new direction for morphing aerial robot design, where passive aerodynamic structures, inspired by thrust vectoring in rockets and aircraft, enable efficient, agile flight without added mechanical complexity.
arXiv Detail & Related papers (2025-12-04T19:31:17Z) - Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control [38.02123507620609]
Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance.<n>We demonstrate insect-like saccade movements with lateral speed and acceleration of 197 centimeters per second and 11.7 meters per second square.<n>The robot can also perform saccade maneuvers under 160 centimeters per second wind disturbance and large command-to-force mapping errors.
arXiv Detail & Related papers (2025-08-05T03:40:11Z) - A highly maneuverable flying squirrel drone with agility-improving foldable wings [3.166558866794283]
This paper proposes a highly maneuverable drone equipped with agility-enhancing foldable wings.<n>The complex aerodynamics of the foldable wings are modeled using a physics-assisted recurrent neural network (paRNN)<n> Experimental results demonstrate that the proposed flying squirrel drone achieves a 13.1% improvement in tracking performance.
arXiv Detail & Related papers (2025-04-13T14:57:11Z) - Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
arXiv Detail & Related papers (2025-02-24T14:53:45Z) - Lander.AI: Adaptive Landing Behavior Agent for Expertise in 3D Dynamic
Platform Landings [2.5022287664959446]
This study introduces an advanced Deep Reinforcement Learning (DRL) agent, Lander:AI, designed to navigate and land on platforms in the presence of windy conditions.
Lander:AI is rigorously trained within the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind turbulence.
The experimental results showcased Lander:AI's high-precision landing and its ability to adapt to moving platforms, even under wind-induced disturbances.
arXiv Detail & Related papers (2024-03-11T10:20:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.