Decision enhancement of speed and separating monitoring modes for human-robot collaborative safety

• Improved decision-making accuracy enhances safety in human-robot collaboration. • AI and ML optimizes robot speed dynamically in response to operator proximity. • Integration of protective and dynamic distances ensures safety. • This integration facilitates human robot collaboration. Involving human robot collaboration advancements are transforming industrial safety protocols. This paper proposes an enhancement approach to improve robot decision accuracy, in Speed and Separation Monitoring (SSM), according to ISO/TS 15066 safety standard. This approach integrates Machine Learning (ML), Artificial Intelligence (AI), for decision-making using data extracted from an active depth camera, which tracks operators’ hand movements and measures distances on line. The developed algorithm enables the robot to make decisions based on protective separation distance (PSD) and dynamic separation distances (DSDs). A test rig developed to determine separation distances required across four zones for safe pick-and-place application. The result shows that the defined thresholds enhances both safety and operation efficiency. This creates a suitable collaborative environment for the operator, and makes the task easier to perform.