An Extreme-Edge TCN-Based Low-Latency Collision-Avoidance Safety System for Industrial Machinery
OPEN ACCESS
Loading...
Author / Producer
Date
2024
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Modern manufacturing industry relies on complex machinery that requires skills, attention, and precise safety certifications. Protecting operators in the machine's surroundings while at the same time reducing the impact on the normal workflow is a major challenge. In particular, safety systems based on proximity sensing of humans or obstacles require that the detection is accurate, low-latency, and robust against variations in environmental conditions. This work proposes a functional safety solution for collision avoidance relying on Ultrasounds (US) and a Temporal Convolutional Network (TCN) suitable for deployment directly at the edge on a low-power Microcontroller Unit (MCU). The setup allowed to acquire a sensor-fusion dataset with 9 US sensors mounted on a real industrial woodworking machine. Applying incremental training, the proposed TCN achieved sensitivity 90.5%, specificity 95.2%, and AUROC 0.972 on data affected by the typical acoustic noise of an industrial facility, an accuracy comparable with the State-of-the-Art (SoA). Deployment on an STM32H7 MCU yielded a memory footprint of 560 B (3x less than SoA), with an extremely low latency of 5.0 ms and an energy consumption of 8.2 mJ per inference (both >2.3x less than SoA). The proposed solution increases its robustness against acoustic noise by leveraging new data, and it fits the resource budget of real-time operation execution on resource-constrained embedded devices. It is thus promising for generalization to different industrial settings and for scale-up to wider monitored spaces.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
12
Pages / Article No.
16009 - 16021
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Collision avoidance; embedded systems; incremental learning; microcontroller; public dataset; real-time; temporal convolutional networks (TCN); time series; TinyML; ultrasounds
Organisational unit
03996 - Benini, Luca / Benini, Luca