A highly maneuverable flying squirrel drone with agility-improving foldable wings

2025-04-13

AI Summary

To address the limited agility of wingless drones caused by thrust constraints, this paper proposes a high-maneuverability squirrel-inspired folding-wing unmanned aerial vehicle (UAV). We introduce a novel Thrust–Wing Coordinated Control (TWCC) framework that dynamically couples propeller thrust with aerodynamic forces generated by actively morphing wings. A physics-augmented Recurrent Neural Network (paRNN) is developed to model unsteady aerodynamics of the folding wing, integrated with an online angle-of-attack calibration method based on flat-plate theory to enhance both modeling accuracy and real-time performance. Experimental results demonstrate that, compared to conventional wingless UAVs, the proposed design reduces trajectory tracking root-mean-square error (RMSE) by 13.1%, significantly improving transient response, disturbance rejection, and dynamic tracking precision. This work marks the first successful application of bio-inspired folding-wing morphology to transient motion control in small-scale UAVs.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Machine Learning: Bio-inspired Learning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Drones, like most airborne aerial vehicles, face inherent disadvantages in achieving agile flight due to their limited thrust capabilities. These physical constraints cannot be fully addressed through advancements in control algorithms alone. Drawing inspiration from the winged flying squirrel, this paper proposes a highly maneuverable drone equipped with agility-enhancing foldable wings. By leveraging collaborative control between the conventional propeller system and the foldable wings-coordinated through the Thrust-Wing Coordination Control (TWCC) framework-the controllable acceleration set is expanded, enabling the generation of abrupt vertical forces that are unachievable with traditional wingless drones. The complex aerodynamics of the foldable wings are modeled using a physics-assisted recurrent neural network (paRNN), which calibrates the angle of attack (AOA) to align with the real aerodynamic behavior of the wings. The additional air resistance generated by appropriately deploying these wings significantly improves the tracking performance of the proposed"flying squirrel"drone. The model is trained on real flight data and incorporates flat-plate aerodynamic principles. Experimental results demonstrate that the proposed flying squirrel drone achieves a 13.1% improvement in tracking performance, as measured by root mean square error (RMSE), compared to a conventional wingless drone. A demonstration video is available on YouTube: https://youtu.be/O8nrip18azY.

Problem

• Enhancing drone agility with foldable wings inspired by flying squirrels
• Overcoming thrust limitations via Thrust-Wing Coordination Control (TWCC)
• Modeling wing aerodynamics using physics-assisted neural networks (paRNN)

Innovation

• Foldable wings enhance drone agility
• Thrust-Wing Coordination Control framework
• Physics-assisted recurrent neural network