A Modular Soft Sensing Skin for Fast Measurement of Wing Deformation in Small Unmanned Aerial Vehicles

Area of Science:

• Robotics
• Bio-inspired engineering
• Sensor technology

Background:

• Natural fliers like insects and birds exhibit superior flight control in complex environments by rapidly sensing and reacting to atmospheric disturbances.
• Engineered systems struggle with large sensor arrays due to slow data processing, hindering real-time response.
• Existing methods for disturbance detection in aerial vehicles often face latency issues with extensive sensor networks.

Purpose of the Study:

• To develop a low-latency, modular soft sensing system for small unmanned aerial vehicles (UAVs).
• To enable rapid detection of wing deformations and atmospheric disturbances for enhanced flight control.
• To overcome the limitations of traditional sensor processing in large-scale sensing arrays for aerial robotics.

Main Methods:

• A modular soft sensing skin with a large array of high-resolution soft strain sensors was developed to cover the UAV's wingspan.
• Decentralized computation was implemented to efficiently manage data from 32 embedded sensors.
• The system was designed for high sampling rates to capture wing dynamics and minimize noise.

Main Results:

• The modular soft sensing system demonstrated efficient data management and fast sampling rates suitable for capturing wing dynamics.
• The hardware architecture significantly reduced system noise, achieving a high signal-to-noise ratio.
• The system successfully utilized all 32 sensors, providing rich data on wing deformation.

Conclusions:

• The proposed modular soft sensing system enables fast and reliable disturbance detection in small UAVs.
• This approach overcomes latency challenges associated with large sensor arrays in engineered fliers.
• The technology holds potential for enhancing control in both soft and rigid robotic systems through advanced soft sensing.