This paper is based on a previously developed bio-inspired Flapping Wing Aerial Vehicle(FWAV),RoboFalcon,which can fly with a morphing-coupled flapping pattern.In this paper,a simple flapping stroke control system bas...This paper is based on a previously developed bio-inspired Flapping Wing Aerial Vehicle(FWAV),RoboFalcon,which can fly with a morphing-coupled flapping pattern.In this paper,a simple flapping stroke control system based on Hall effect sensors is designed and applied,which is capable of assigning different up-and down-stroke speeds for the RoboFalcon platform to achieve an adjustable downstroke ratio.The aerodynamic and power characteristics of the morphing-coupled flapping pattern and the conventional flapping pattern with varying downstroke ratios are measured through a wind tunnel experiment,and the corresponding aerodynamic models are developed and analyzed by the nonlinear least squares method.The relatively low power consumption of the slow-downstroke mode of this vehicle is verified through outdoor flight tests.The results of wind tunnel experiments and flight tests indicate that increased downstroke duration can improve aerodynamic and power performance for the RoboFalcon platform.展开更多
In order to design and verify control algorithms for flapping wing aerial vehicles(FWAVs),calculation models of the translational force,rotational force and virtual mass force were established with the basis on the mo...In order to design and verify control algorithms for flapping wing aerial vehicles(FWAVs),calculation models of the translational force,rotational force and virtual mass force were established with the basis on the modified quasi-steady aerodynamic theory and high lift mechanisms of insect flight.The simulation results show that the rotational force and virtual mass force can be ignored in the hovering FWAVs with simple harmonic motions in a cycle.The effects of the wing deformation on aerodynamic forces were investigated by regarding the maximum rotational angle of wingtip as a reference variable.The simulation results also show that the average lift coefficient increases and drag coefficient decreases with the increase of the maximum rotational angle of wingtip in the range of 0-90°.展开更多
Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However...Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.展开更多
基金supported by National Natural Science Foundation of China under Grants No.52175277 and 12272318,and ND Basic Research Funds under Grants G2022WDwas supported in part by the Basic Research Program of Shenzhen under GrantJCYJ20190806142816524.
文摘This paper is based on a previously developed bio-inspired Flapping Wing Aerial Vehicle(FWAV),RoboFalcon,which can fly with a morphing-coupled flapping pattern.In this paper,a simple flapping stroke control system based on Hall effect sensors is designed and applied,which is capable of assigning different up-and down-stroke speeds for the RoboFalcon platform to achieve an adjustable downstroke ratio.The aerodynamic and power characteristics of the morphing-coupled flapping pattern and the conventional flapping pattern with varying downstroke ratios are measured through a wind tunnel experiment,and the corresponding aerodynamic models are developed and analyzed by the nonlinear least squares method.The relatively low power consumption of the slow-downstroke mode of this vehicle is verified through outdoor flight tests.The results of wind tunnel experiments and flight tests indicate that increased downstroke duration can improve aerodynamic and power performance for the RoboFalcon platform.
基金National Natural Science Foundation of China(5177041109)。
文摘In order to design and verify control algorithms for flapping wing aerial vehicles(FWAVs),calculation models of the translational force,rotational force and virtual mass force were established with the basis on the modified quasi-steady aerodynamic theory and high lift mechanisms of insect flight.The simulation results show that the rotational force and virtual mass force can be ignored in the hovering FWAVs with simple harmonic motions in a cycle.The effects of the wing deformation on aerodynamic forces were investigated by regarding the maximum rotational angle of wingtip as a reference variable.The simulation results also show that the average lift coefficient increases and drag coefficient decreases with the increase of the maximum rotational angle of wingtip in the range of 0-90°.
基金supported by the Fundamental Research Funds for the Central Universities,China。
文摘Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.
