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

TL;DR

This paper presents a significantly lighter and more power-efficient avionics system for flying insect-scale robots, enabling sustained hover with minimal weight and power, using innovative sensors and sensor fusion techniques.

Contribution

Developed a sub-gram, low-power avionics system with novel sensor integration and fusion for autonomous hover in insect-scale flying robots.

Findings

Achieved 78.4 mg mass and 15 mW power consumption in avionics.

System performs comparably to larger systems in state estimation accuracy.

Replaced heavier sensors with lightweight alternatives without sacrificing performance.

Abstract

In this paper, we introduce advances in the sensor suite of an autonomous flying insect robot (FIR) weighing less than a gram. FIRs, because of their small 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 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 hovering -- 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 deg, 0.186 m/s, and 0.136 m, respectively, relative to motion capture.