Workstation Suitability Maps: Generating Ergonomic Behaviors on a Population of Virtual Humans With Multi-Task Optimization

In industrial workstations, the morphology of the worker is a key factor for the feasibility and the ergonomics of an activity. Existing digital human modeling tools can simulate different morphologies at work, but hardly scale to a large population of workers because of limited consideration of morphology-specific behaviors and computational cost. This letter presents a framework to efficiently evaluate the suitability of a workstation over a large population of workers in a physics-based simulation. Activities are simulated through a two-step optimization process, involving a quadratic-programming-based whole-body controller and a multi-task optimizer for behavioral adaptation. On a screwdriving scenario, we demonstrate how our framework can help ergonomists improve workstation designs thanks to the resulting suitability maps where generated behaviors are optimized for each morphology w.r.t. ergonomics and performance.

Automated summary

This letter presents a framework for efficiently evaluating the suitability of a workstation over a large population of workers using physics-based simulation. The framework simulates different morphologies and adapts behaviors through optimization algorithms, aiming to help ergonomists improve workstation designs.