Attitude Estimation Algorithm of Flapping-Wing Micro Air Vehicle Based on Extended Kalman Filter

Yang, Runmin; Zhang, Weiping; Mou, Jiawang; Zhang, Benyi; Zhang, Yichen

doi:10.1007/978-981-99-0479-2_131

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In general, flapping-wing drones require IMU sensor consisting of accelerometer, gyroscope, and magnetometer and optical flow sensors that can measure information in 3D space to achieve position and attitude control 50,51 . In contrast, we indirectly measure the 3D space information by determining 4-dimensional information such as the x, y, z position coordinates and speed using only two strain sensors that measure 1-dimensional strain information.…”

Section: Position Control In Windy Environmentmentioning

confidence: 99%

Fly-by-Feel: Wing Strain-based Flight Control of Flapping-Wing Drones through Reinforcement Learning

Kang,

Han,

Koh

et al. 2024

Preprint

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Although drone technology has progressed significantly, replicating the dynamic control and wind-sensing abilities of biological flights is still beyond our reach. Biological studies have revealed that insect wings are equipped with mechanoreceptors known as campaniform sensilla, which detect complex aerodynamic loads critical for flight agility. By leveraging robotic experiments designed to mimic these biological systems, we confirmed that wing strain provides crucial information about the drone's attitude, as well as the direction and velocity of the wind. We introduce a novel wing strain-based flight controller, termed 'fly-by-feel'. This methodology employs the aerodynamic forces exerted on a flapping drone's wings to deduce vital flight data, such as attitude and airflow without accelerometers and gyroscopic sensors. Our empirical approach spanned five key experiments: initially validating the wing strain sensor system for state information provision, followed by a single degree of freedom (1 DOF) control in changing winds, a two degrees of freedom (2 DOF) control for gravitational attitude adjustment, a test for position control in windy conditions, and finally, demonstrating precise flight path manipulation in a windless condition using only wing strain sensors. We have successfully demonstrated control of a flapping drone in a various environment using only wing strain sensors, with the aid of reinforcement learning-driven flight controller. The fly-by-feel system holds the potential to revolutionize autonomous drone operations, providing enhanced adaptability to environmental shifts. This will be beneficial across varied applications, from gust resistance to wind-assisted flight, paving the way toward the next generation of resilient and autonomous flying robots.

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Section: Position Control In Windy Environmentmentioning

confidence: 99%

Fly-by-Feel: Wing Strain-based Flight Control of Flapping-Wing Drones through Reinforcement Learning

Kang,

Han,

Koh

et al. 2024

Preprint

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“…Liu G et al [ 21 ] devised a multisensor integrated state sensing and estimation method to address the substantial FMAV flight fluctuation problem using Kalman principles, thereby enhancing state estimation accuracy. Yang R et al [ 22 ] proposed a data fusion and attitude estimation algorithm based on the EKF algorithm to counteract instability caused by jitter during FMAV sensor data acquisition’s transient oscillation. He W et al [ 23 ] formulated a state estimator based on uncertain perturbation to address the challenges of unknown time delay and nonlinearity in FMAVs.…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation for Flapping-Wing Micro Air Vehicles with Information Fusion Correction

Zhang,

Luo,

Guo

et al. 2024

Biomimetics

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In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed H∞ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV’s received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov–Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs’ distributed H∞ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations.

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Wing-strain-based flight control of flapping-wing drones through reinforcement learning

Kim,

Hong,

et al. 2024

Nat Mach Intell

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Attitude Estimation Algorithm of Flapping-Wing Micro Air Vehicle Based on Extended Kalman Filter

Cited by 3 publications

References 13 publications

Fly-by-Feel: Wing Strain-based Flight Control of Flapping-Wing Drones through Reinforcement Learning

Fly-by-Feel: Wing Strain-based Flight Control of Flapping-Wing Drones through Reinforcement Learning

Distributed State Estimation for Flapping-Wing Micro Air Vehicles with Information Fusion Correction

Wing-strain-based flight control of flapping-wing drones through reinforcement learning

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