FAVbot: An Autonomous Target Tracking Micro-Robot with Frequency Actuation Control

TL;DR

FAVbot is a compact, autonomous micro-robot that combines a novel frequency-controlled actuation mechanism with CNN-based vision for target tracking, enabling adaptive navigation within a tiny 3-cm form factor.

Contribution

This work introduces the first micro-robot with integrated frequency-controlled actuation and neural network vision in a 3-cm size, advancing autonomous capabilities at this scale.

Findings

Effective frequency-controlled steering via mechanical resonance

Successful object detection and target tracking in dynamic environments

Demonstration of the smallest controllable multi-directional single-actuator robot

Abstract

Robotic autonomy at centimeter scale requires compact and miniaturization-friendly actuation integrated with sensing and neural network processing assembly within a tiny form factor. Applications of such systems have witnessed significant advancements in recent years in fields such as healthcare, manufacturing, and post-disaster rescue. The system design at this scale puts stringent constraints on power consumption for both the sensory front-end and actuation back-end and the weight of the electronic assembly for robust operation. In this paper, we introduce FAVbot, the first autonomous mobile micro-robotic system integrated with a novel actuation mechanism and convolutional neural network (CNN) based computer vision - all integrated within a compact 3-cm form factor. The novel actuation mechanism utilizes mechanical resonance phenomenon to achieve frequency-controlled steering with a single piezoelectric actuator. Experimental results demonstrate the effectiveness of FAVbot's frequency-controlled actuation, which offers a diverse selection of resonance modes with different motion characteristics. The actuation system is complemented with the vision front-end where a camera along with a microcontroller supports object detection for closed-loop control and autonomous target tracking. This enables adaptive navigation in dynamic environments. This work contributes to the evolving landscape of neural network-enabled micro-robotic systems showing the smallest autonomous robot built using controllable multi-directional single-actuator mechanism.