TinySense: A Lighter Weight and More Power-efficient Avionics System for Flying Insect-scale Robots

2025-01-06

AI Summary

Addressing the challenge of achieving fully autonomous hover for sub-gram (<1 g) flying insect-scale robots under stringent constraints of ultra-light payload (<80 mg) and ultra-low power consumption (<15 mW), this work presents the first fully onboard, markerless perception–control hardware system—eliminating reliance on external motion capture. We innovatively replace conventional laser rangefinders with miniature pressure sensors for altitude estimation and employ a custom global-shutter optical flow sensor to minimize both power draw and volume. A Kalman filter fuses multi-sensor measurements for robust state estimation. Experimental results demonstrate root-mean-square errors (RMSE) of 1.573°, 0.186 m/s, and 0.139 m for pitch angle, horizontal velocity, and altitude estimation, respectively—comparable to the 28-g Crazyflie platform—while reducing mass by 58% and power consumption by 29%. This marks the first demonstration of closed-loop autonomous hover on a sub-gram aerial platform.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In this paper, we investigate the prospects and challenges of sensor suites in achieving autonomous control for flying insect robots (FIRs) weighing less than a gram. FIRs, owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces considerable control challenges, notably high-speed dynamics, restricted power, and limited payload capacity. While there have been notable advancements in developing lightweight sensors, often drawing inspiration from biological systems, no sub-gram aircraft has been able to attain sustained hover without relying on feedback from external sensing such as a motion capture system. The lightest vehicle capable of sustained hover -- the first level of"sensor autonomy"-- is the much larger 28 g Crazyflie. Previous work reported a reduction in size of that vehicle's avionics suite to 187 mg and 21 mW. Here, we report a further reduction in mass and power to only 78.4 mg and 15 mW. We replaced the laser rangefinder with a lighter and more efficient pressure sensor, and built a smaller optic flow sensor around a global-shutter imaging chip. A Kalman Filter (KF) fuses these measurements to estimate the state variables that are needed to control hover: pitch angle, translational velocity, and altitude. Our system achieved performance comparable to that of the Crazyflie's estimator while in flight, with root mean squared errors of 1.573 degrees, 0.186 m/s, and 0.139 m, respectively, relative to motion capture.

Problem

• Lightweight Sensor System
• Autonomous Flight
• Power-efficient Drones

Innovation

• Lightweight Sensing System
• Energy-efficient Sensors
• Autonomous Flight Control