A highly maneuverable flying squirrel drone with controllable foldable wings

• URL: http://arxiv.org/abs/2504.09478v1
• Date: Sun, 13 Apr 2025 08:15:28 GMT
• Title: A highly maneuverable flying squirrel drone with controllable foldable wings
• Abstract summary: This paper suggests a new bio-inspired drone that is empowered with high maneuverability in a lightweight and easy-to-carry way.<n>The wings of a drone are fabricated with silicone membranes and sophisticatedly controlled by reinforcement learning based on human-demonstrated data.
• Score: 3.166558866794283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Typical drones with multi rotors are generally less maneuverable due to unidirectional thrust, which may be unfavorable to agile flight in very narrow and confined spaces. This paper suggests a new bio-inspired drone that is empowered with high maneuverability in a lightweight and easy-to-carry way. The proposed flying squirrel inspired drone has controllable foldable wings to cover a wider range of flight attitudes and provide more maneuverable flight capability with stable tracking performance. The wings of a drone are fabricated with silicone membranes and sophisticatedly controlled by reinforcement learning based on human-demonstrated data. Specially, such learning based wing control serves to capture even the complex aerodynamics that are often impossible to model mathematically. It is shown through experiment that the proposed flying squirrel drone intentionally induces aerodynamic drag and hence provides the desired additional repulsive force even under saturated mechanical thrust. This work is very meaningful in demonstrating the potential of biomimicry and machine learning for realizing an animal-like agile drone.

Related papers

• HoLoArm: Deformable Arms for Collision-Tolerant Quadrotor Flight [46.42648821424623]
  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.
  arXiv  Detail & Related papers  (2026-05-25T12:37:18Z)
• 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)
• 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)
• 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)
• Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling [17.368574409020475]
  Ego-motion estimation is vital for drones when flying in GPS-denied environments. We propose a self-supervised learning scheme to train a neural-network-based drone model using only onboard monocular video and flight controller data. We demonstrate the value of the neural drone model by integrating it into a traditional filter-based VIO system.
  arXiv  Detail & Related papers  (2025-04-30T14:38:01Z)
• 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) 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. 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)
• Flying in air ducts [3.1096750631623484]
  We show that a small quadrotor drone can hover and fly inside small air ducts, starting with a diameter of 350 mm. These results open a new and promising application domain for drones.
  arXiv  Detail & Related papers  (2024-10-10T21:33:57Z)
• Exploring Jamming and Hijacking Attacks for Micro Aerial Drones [14.970216072065861]
  The Crazyflie ecosystem is one of the most popular Micro Aerial Drones and has the potential to be deployed worldwide. In this paper, we empirically investigate two interference attacks against the Crazy Real Time Protocol (CRTP) implemented within the Crazyflie drones. Our experimental results demonstrate the effectiveness of such attacks in both autonomous and non-autonomous flight modes.
  arXiv  Detail & Related papers  (2024-03-06T17:09:27Z)
• From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching [15.57055572401334]
  Inverted landing is a routine behavior among a number of animal fliers. We develop a control policy general to arbitrary ceiling-approach conditions. We successfully achieved a range of robust inverted-landing behaviors in small quadcopters.
  arXiv  Detail & Related papers  (2024-02-29T21:09:08Z)
• Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones [0.08192907805418582]
  We propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning. The results show that the reinforcement learning based policy converges to identify and track the intruder drone.
  arXiv  Detail & Related papers  (2023-09-10T16:31:40Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Multi-Drone based Single Object Tracking with Agent Sharing Network [74.8198920355117]
  Multi-Drone single Object Tracking dataset consists of 92 groups of video clips with 113,918 high resolution frames taken by two drones and 63 groups of video clips with 145,875 high resolution frames taken by three drones. Agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones.
  arXiv  Detail & Related papers  (2020-03-16T03:27:04Z)

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.