Manipulability Optimization and Thermal Control of Industrial Robots in Real-Time Using Digital Twins, Augmented Reality, and OPC UA

Abstract

Planning efficient motions of robots is often inhibited by the difficulty to spatially imagine and enhance their dexterity and agility in the task space. To accommodate this complexity, meet short changeover times, and support inclusion along with sustainability goals, robot operators with different experiences and facing a robot diversity need responsive and actionable interfaces. These enable the prediction and optimization of hidden (i.e., internal) performance-critical properties of physical robots, such as their velocity manipulability in the task space and thermal loads in the joint space, in real-time on concerned components. We address these fundamental challenges encountered in several high-level robotized applications by developing a virtual spatial augmentation of physical robots that translates their complex, otherwise invisible manipulability and thermal states, into accessible and interactive visual cues easily interpreted and used by even novices to improve the robot dexterity in the null-space and anticipate issues due to overheating joints. An upskilling immersion based upon augmented reality and enriched with overlaid digital twins is leveraged to this end. While following values targeted by our overarching Metarobotics framework, we stress the non-invasive and sustainable characteristic of the approach. Furthermore, our framework transparently embeds in existing robotized industrial settings, systems of systems, and workflows without production standstills. We emphasize on its semantic interoperability, versatility, and openness driven by the OPC UA standard, share results from experiments, and pointed out emerging basic research implications that deserve further attention.