A Cost-Effective Thermal Imaging Safety Sensor for Industry 5.0 and Collaborative Robotics

TL;DR

This paper presents a cost-effective thermal imaging safety sensor designed for Industry 5.0, enabling reliable human proximity detection in collaborative robotics with high accuracy and low computational cost.

Contribution

The paper introduces a novel hybrid detection approach for thermal sensors that enhances safety in human-robot collaboration in Industry 5.0 environments.

Findings

97% accuracy in human detection using thermal images

Low computational cost of the hybrid detection method

Effective safety mechanism for collaborative robotics

Abstract

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.