AI-Powered Collision Avoidance Safety System for Industrial Woodworking Machinery

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

“…A relevant earlier work tackling US-and-DL-based functional safety for woodworking machinery is by Conti et al [12], who employ TEMPONet, a TCN previously applied to embedded biosignal processing in real-time [18], [19]. The previous work by Conti et al [12] stemmed from the same project as this paper but only has the nature of a technical report documenting an incomplete stage of the research.…”

“…A relevant earlier work tackling US-and-DL-based functional safety for woodworking machinery is by Conti et al [12], who employ TEMPONet, a TCN previously applied to embedded biosignal processing in real-time [18], [19]. The previous work by Conti et al [12] stemmed from the same project as this paper but only has the nature of a technical report documenting an incomplete stage of the research. Although a direct accuracy comparison is not viable since [12] relies on a different 1-channel dataset, it is possible to highlight several advancements (also reported in Table 1): (i) the proposed system mounts 9 ultrasound sensors, whereas the previous work mounted just 1; (ii) this work releases the dataset open-source; (iii) this work employs a smaller DNN, reducing the hardware resources and latency budget for execution; (iv) this work tackles a noisy environment by implementing an incremental training protocol instead of brute-force data augmentation.…”

“…All safety equipment applied on industrial machines must get certified according to standards, such as the ones by the International Electrotechnical Commission (IEC) (covering electrical, electronic, and related technologies), that define the Safety Integrity Level to be met. Machinery-halting safety systems such as [12] and the one presented in this work fall under the regulations concerning non-contact Electro-Sensitive Protective Equipment (ESPE) sensors (e.g., photodiodes). More in detail, IEC 61508 [1] regards any electrical/electronic/programmable electronic (E/E/PE) for func- tional safety systems, such as sensors, control logic, or actuators, and also microprocessors; EN IEC 61496 [11] focuses on the requirements of design, building, and verification of systems based on non-contact ESPEs to detect persons in a safety system, focusing on indoor environments; EN IEC 62046 [20] addresses ESPEs for human detection for safety, focusing on industrial environments with machinery.…”

“…• This work implements a system based on 9 US sensors, an FPGA, and an MCU, mounted onto an industrial woodworking machine. MCU yielded a profiling which outperforms the Stateof-the-Art (SoA) TCN model for the task [12]: memory footprint of 560 B (3× smaller than SoA), with a latency of 5.0 ms and energy consumption of 8.2 mJ per inference (both 2.3× less than SoA). The proposed solution improves detection robustness against acoustic interference characteristic of a manufacturing environment, working with a resource budget fit for real-time execution on resource-constrained edge computing platforms.…”

“…The proposed solution improves detection robustness against acoustic interference characteristic of a manufacturing environment, working with a resource budget fit for real-time execution on resource-constrained edge computing platforms. Table 1 reports a scheme of the advances of this work compared to the SoA represented by Conti et al [12]. The proposed paradigm is generalizable to different sectors; in particular, the limited hardware requirements allow the scaleup of the approach, enabling adoption in scenarios with more sensors and, thus, wider monitored space in terms of the number of machines and extent of the probed areas.…”