Bio-Inspired Wind Sensing Using Strain Sensors Could Transform Robotic Flight Control

Researchers at the Institute of Science Tokyo have developed a revolutionary method for detecting wind direction with 99% accuracy using strain gauges on flexible wings and a convolutional neural network model. This innovation, inspired by the natural strain receptors found in birds and insects, holds the potential to significantly enhance the control and adaptability of flapping-wing aerial robots in various wind conditions.

Insects and birds rely on mechanical receptors on their wings to gather strain sensory data, which helps them adjust their flight in response to wind, body movement, and environmental changes. Motivated by this natural mechanism, the researchers aimed to replicate these strain sensing capabilities using a biomimetic flapping robot.

The study, published in Advanced Intelligent Systems on November 11, 2024, investigates the use of strain sensors attached to flexible wings designed to mimic the wings of a hummingbird. Led by Associate Professor Hiroto Tanaka, the researchers tested the wings in a wind tunnel that simulated hovering flight under gentle wind conditions.

"Small aerial robots are often too limited in size and weight to use conventional flow-sensing devices. Therefore, it would be highly advantageous if we could use simple wing strain sensing to directly detect flow conditions without the need for additional devices," explained Tanaka.

The researchers equipped the wings with seven strain gauges, cost-effective and commercially available sensors, attached to a flexible wing structure similar to a hummingbird's wings. These wings were mounted on a flapping mechanism powered by a DC motor, which generated a back-and-forth flapping motion at 12 cycles per second.

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In the wind tunnel, the researchers applied a weak wind speed of 0.8 m/s and measured the wing strain under seven different wind directions (ranging from 0° to 90°) and a no-wind condition. Using a convolutional neural network (CNN), they trained the system to classify the various wind conditions based on the strain data.

The results were striking, with the system achieving 99.5% accuracy in classifying the wind direction using the full flapping cycle data. Even with data from just 0.2 flapping cycles, the accuracy remained high at 85.2%.

Using only one strain gauge, the classification accuracy ranged between 95.2% and 98.8% for data from a full flapping cycle, but dropped to 65.6% or lower for the 0.2 cycle data. This suggests that using multiple strain sensors across different locations on the wing allows for precise wind direction recognition with minimal data.

When the inner wing shafts were removed, the classification accuracy slightly decreased, but the effect was small (a drop of 4.4% for 0.2 cycle data and 0.5% for 1 cycle data with all strain gauges). The use of a single strain gauge caused a larger decrease, with accuracy dropping by an average of 7.2% for full cycle data and 6% for 0.2 cycle data. These findings indicate that the biomimetic wing shaft structure improves the wind sensing capabilities.

Tanaka concludes, "This study helps to enhance our understanding of how hovering birds and insects may sense wind through strain sensing in their flapping wings, which aids in responsive flight control. A similar system could be implemented in biomimetic flapping-wing robots using simple strain gauges."