Designing safe and collaborative robotic experiences in manufacturing environments

Abstract

The transition from Industry 4.0 to Industry 5.0 marks a shift towards a more human-centric and sustainable manufacturing paradigm. In this context, collaborative robots, or cobots, remain as a key technology, supporting the human-centred goals of Industry 5.0 while also responding to economic demands for flexibility and mass customisation. Cobots are industrial robots equipped with in-built features that allow them to work alongside human operators without the need for traditional safety barriers. These robots aim to overcome the limitations of traditional industrial robots by combining the mechanical strength of industrial robots with the cognitive abilities of humans. However, the removal of safety barriers in cobot workspaces raises significant concerns regarding both physical and cognitive safety. This project addresses these concerns by designing a safe and collaborative robotic system that incorporates safety measures to ensure safe human-robot interaction in shared workspaces. The designed system uses Speed and Separation Monitoring, a safety method outlined in ISO/TS 15066:2016, which monitors human presence and adjusts the robot's speed in real-time based on proximity, reducing the risk of collisions. A Human-Machine Interface is implemented to provide real-time feedback to the operator, thereby enhancing cognitive ergonomics and building operator trust. The system was designed using the V-Model design methodology, a widely used approach in systems and mechatronics design. A multi-modal sensing approach is chosen, using two sensors with complementary characteristics to enhance redundancy and environmental robustness. The system was implemented using the Robot Operating System (ROS), which facilitates communication between the system components and ensures modularity and scalability. Decision-making is controlled by a Behaviour Tree, which processes sensor data and makes safety-critical decisions to adjust the robot’s behaviour intelligently. The system was evaluated through a series of tests, demonstrating its effectiveness in maintaining safety while enabling continuous operation. Results showed that the system adapts well to human presence, reducing robot speed when needed and halting operations in high-risk situations. Participant feedback confirmed that the system instilled a sense of safety and trust.