🤖 AI Summary
This study addresses the complex safety risks posed by humanoid robots in smart manufacturing, where human-like dynamic motions introduce temporal and stochastic accident characteristics that conventional passive safety mechanisms—relying on fixed force or distance thresholds—fail to adequately handle. To overcome this limitation, the authors propose a novel risk-aware framework integrating trust calibration with precursor-driven reasoning. For the first time, the approach couples the temporal evolution of precursors with dynamic trust assessment, employing a Logistic-Exponential model to capture multi-source precursor cues over time and defining trust as the reciprocal of predicted accident probability to enable real-time adaptive behavior. Evaluated on a dataset comprising 126 incidents and 241 precursors, the method identifies 12 dominant accident patterns and demonstrates successful early warning and proactive intervention in “fall-and-collide” simulations, advancing humanoid robot safety from static thresholds toward evidence-based, dynamic risk inference.
📝 Abstract
Humanoid robots are emerging as co-workers in smart manufacturing, yet their dynamic, human-like movements introduce safety risks that differ fundamentally from those of fixed or wheeled robots. Conventional safety paradigms based on reactive force or distance limits fail to capture the sequential, uncertain nature of humanoid failures. This study proposes a precursor-driven, trust-calibrated framework to enable proactive humanoid risk perception. Accident evolution is modeled through sequential precursor cues using a Logistic-Exponential (LE) formulation that couples logistic escalation from diverse precursors with exponential decay for temporal dissipation. Trust is defined as the inverse of the estimated accident probability, allowing humanoids to adapt behavior in real time, reducing aggressiveness when risk intensifies, and restoring confidence as stability returns. A multi-source dataset of 126 documented events and 241 precursors revealed twelve dominant accident modes, most evolving through overlapping cues within one second. A simulated case study ("fall-onto-human") demonstrated how the LE-Trust coupling can trigger early intervention and prevent collapse. The results advance humanoid safety from static thresholds toward dynamic, evidence-based inference, establishing a foundation for risk-aware and trustworthy human-robot collaboration in Industry 5.0 environments.