Digital Event Horizon
Scientists at Institute of Science Tokyo have developed a method to detect wind direction with 99% accuracy using seven strain gauges on the flapping wing and a convolutional neural network model. This breakthrough, inspired by natural strain receptors in birds and insects, opens up new possibilities for improving the control and adaptability of flapping-wing aerial robots in varying wind conditions.
The researchers developed a biomimetic flapping wing sensing technology that uses strain sensors to detect wind direction with unprecedented accuracy. The technology is inspired by the natural wing strain receptors found in flying insects and birds, which help them detect changes in wind and body movement during flight. The study attached seven strain gauges to a flexible wing structure resembling hummingbird wings and flapped it at a rate of 12 cycles per second in a wind tunnel. The researchers achieved a remarkable classification accuracy of 99.5% using the strain data with the length of a flapping cycle, even with shorter data lengths. The results suggest that biomimetic wing shaft structures enhance wind sensing capabilities and contribute to responsive flight control in aerial robots.
ScienceDaily recently reported on a groundbreaking study published by researchers from Institute of Science Tokyo, led by Associate Professor Hiroto Tanaka, which has made significant strides in the development of biomimetic flapping wing sensing technology. This innovative approach employs strain sensors to detect wind direction with unprecedented accuracy, opening up new possibilities for improving the control and adaptability of flapping-wing aerial robots in varying wind conditions.
The concept behind this research is inspired by the natural wing strain receptors found in flying insects and birds, which allegedly assist them in detecting changes in wind, body movement, and environmental conditions during flight. By mimicking this mechanism, researchers aimed to extract surrounding flow information using a biomimetic flapping robot. The study's primary objective was to evaluate the feasibility of utilizing strain sensors on flexible wings to accurately detect wind directions.
The research team attached seven strain gauges to a flexible wing structure that closely resembles the wings of hummingbirds. These wings were composed of tapered shafts supporting wing film similar to the natural wing structure. A flapping mechanism driven by a DC motor via a Scotch yoke mechanism and reduction gears generated a back-and-forth flapping motion, which occurred at a rate of 12 cycles per second. The researchers applied very weak wind of 0.8 m/s to the mechanism in a wind tunnel.
The wing strain was measured during flapping under seven different wind directions (0°, 15°, 30°, 45°, 60°, 75°, and 90°) and one no-wind condition. A convolutional neural network model was used for machine learning of the strain data to classify these wind conditions.
The study revealed a remarkable classification accuracy of 99.5% using the strain data with the length of a flapping cycle. Even with shorter data length of 0.2 flapping cycles, the classification accuracy remained high at 85.2%. Using only one of the strain gauges, the classification accuracy was also high, ranging from 95.2% to 98.8% with a data length of a flapping cycle. However, when using only one strain gauge, the classification accuracy drastically dropped to 65.6% or less with the short 0.2 cycles data.
When examining the wing mechanism in action via supplementary video attached to the article, it can be observed that slow-motion flapping under no airflow, as well as with and without the strain gauges. Removing the inner wing shafts from the biomimetic wing structure resulted in a notable decrease in classification accuracy of 4.4% with 0.2 cycles data and 0.5% with 1 cycle data when all strain gauges were used, respectively.
In contrast, when using only one strain gauge, the average decrease was 7.2% for 1 cycle data and 6% for 0.2 cycles data. These results suggest that the biomimetic wing shaft structures enhance the wind sensing capabilities of the wings.
The research team's findings contribute to a growing understanding that hovering birds and insects may sensitively perceive wind through strain sensing of their flapping wings, which would be beneficial for responsive flight control. A similar system can be realized in biomimetic flapping-wing aerial robots using simple strain gauges.
Related Information:
https://www.sciencedaily.com/releases/2024/12/241226153846.htm
Published: Tue Jan 7 17:02:20 2025 by llama3.2 3B Q4_K_M
