🤖 AI Summary
In industrial and societal 6.0 contexts, robot motor overheating causes unplanned shutdowns, jeopardizing human–robot safety and system availability. To address this, we propose a generative-discriminative digital twin framework based on variational autoencoders (VAEs). Our method introduces the concept of “thermal difficulty,” modeling normal thermal behavior via unsupervised learning and quantifying motion-induced thermal risk through reconstruction error—enabling pre-execution thermal feasibility prediction. It operates without labeled data or physical cooling interventions, proactively avoiding hazardous motions and supporting cross-agent thermal knowledge sharing. Experiments demonstrate accurate early-warning capability prior to actual overheating, significantly enhancing equipment uptime and collaborative safety while overcoming the production continuity limitations imposed by conventional reactive shutdown mechanisms.
📝 Abstract
Robots are unrelentingly used to achieve operational efficiency in Industry 4.0 along with symbiotic and sustainable assistance for the work-force in Industry 5.0. As resilience, robustness, and well-being are required in anti-fragile manufacturing and human-centric societal tasks, an autonomous anticipation and adaption to thermal saturation and burns due to motors overheating become instrumental for human safety and robot availability. Robots are thereby expected to self-sustain their performance and deliver user experience, in addition to communicating their capability to other agents in advance to ensure fully automated thermally feasible tasks, and prolong their lifetime without human intervention. However, the traditional robot shutdown, when facing an imminent thermal saturation, inhibits productivity in factories and comfort in the society, while cooling strategies are hard to implement after the robot acquisition. In this work, smart digital twins endowed with generative AI, i.e., variational autoencoders, are leveraged to manage thermally anomalous and generate uncritical robot states. The notion of thermal difficulty is derived from the reconstruction error of variational autoencoders. A robot can use this score to predict, anticipate, and share the thermal feasibility of desired motion profiles to meet requirements from emerging applications in Industry 6.0 and Society 6.0.