An Extreme-Edge TCN-Based Low-Latency Collision-Avoidance Safety System for Industrial Machinery

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 (3× less than SoA), with an extremely low latency of 5.0 ms and an energy consumption of 8.2 mJ per inference (both >2.3× 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.

Keywords

• Collision avoidance
• embedded systems
• incremental learning
• microcontroller
• public dataset
• real-time