Hummingbird Wings Helped Inspire This Unique Approach To Robotic Flight Control That Could Revolutionize The Industry
Robots are no longer just the stuff of sci-fi movies or vacuum cleaners bumping into furniture. Advancements in robotic technology are moving faster than ever in this modern age.
Researchers at the Institute of Science Tokyo have developed a unique approach to robotic flight control that could revolutionize the industry.
Their method involves bio-inspired wind sensing using strain sensors on flexible wings. They can detect the direction of the wind at an accuracy rate of 99.5 percent using seven strain gauges and a convolutional neural network model. This breakthrough design was inspired by birds and insects.
Flying insects and birds have mechanical receptors on their wings that collect strain sensory data, which likely helps with their flight control.
These receptors allow them to detect changes in wind, environmental conditions, and body movement so they can adjust their flight accordingly. The researchers wanted to explore how these natural systems could function in robotic wings.
“Small aerial robots cannot afford conventional flow-sensing apparatus due to severe limitations in weight and size. Hence, it would be beneficial if simple wing strain sensing could be utilized to directly recognize flow conditions without additional dedicated devices,” said Hiroto Tanaka, the lead author of the study.
In the study, the research team investigated the use of strain sensors on hummingbird-like wings to detect flow directions with precision during tethered flapping in a wind tunnel that was supposed to simulate hovering flight in gentle wind conditions.
They attached seven strain gauges, which were low-cost and widely available, to a flexible wing structure that resembles the wings of hummingbirds.
The wings were secured to a flapping mechanism driven by a DC motor and Scotch yoke system. A rate of 12 flapping cycles per second was generated.
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Then, the team applied a weak wind of 0.8 m/s in a wind tunnel to measure wing strain under seven different wind directions at zero degrees, 15 degrees, 30 degrees, 45 degrees, 60 degrees, 75 degrees, and 90 degrees, along with one no-wind condition.
The results were a 99.5 percent classification accuracy using the strain data from the length of a full flapping cycle. Even with limited data from only 0.2 flapping cycles, the accuracy remained high at 85.2 percent.
Additional analysis revealed that data from a single strain gauge, classification accuracy for one complete flapping cycle ranged from 95.2 percent to 98.8 percent. However, the accuracy decreased to 65.6 percent or lower when relying on the shorter 0.2-cycle data.
Furthermore, the removal of inner wing shafts led to less classification accuracy, emphasizing how important biomimetic wing structures are in boosting wind-sensing capabilities.
“This study contributes to the 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,” said Tanaka. “A similar system can be realized in biomimetic flapping-wing aerial robots using simple strain gauges.”
The study was published in Advanced Intelligent Systems.
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