Enhanced Activity Detection in Mechanical Robot Dog Using Dynamic Strain-Based FBG Sensors and YOLO-v7

Abstract

In the modern world, robots have become increasingly essential across various industries. Activity monitoring has emerged as a key method for diagnosing environmental conditions and enhancing the intelligence of mechanical robots. Vibration or strain from different activities is a critical parameter for evaluating and detecting activities, which presents a significant challenge in accurately assessing robotic performance across diverse tasks. This article demonstrates a novel method for activity monitoring of mechanical robot-dog machines that prevents motor wear, reduces high maintenance costs, and increases the durability of machines. The method utilizes an optical fiber-based fiber Bragg grating (FBG) sensor system to detect dynamic strain resulting from vibration signals generated by robotic movements, ensuring precise monitoring of robotic dog activities and the you only look once version 7 (YOLO-v7) algorithm for activity detection. Two model modules are implemented: the first experiment collects dynamic strain data for up to eight possible activities, and the second experiment collects the five different weights dragged by the robot dog. YOLO-v7 ensures and evaluates robot activities. The detection results demonstrate the eight activities and different weight-carrying model accuracy of 95.31% and 98.81%, respectively. The model performance shows that strains from the motor machine are accurately detected, signaling anomalies. Thus, our proposed experimental setup is flexible, cost-effective, robust, computationally efficient, fast, and improves the sensing quality of robot pose action monitoring