An Ergonomic Role Allocation Framework for Dynamic Human-Robot Collaborative Tasks

TL;DR

This paper presents an ergonomic role allocation framework for human-robot collaboration that dynamically assigns tasks based on ergonomic risk, reducing human fatigue and frustration through adaptive decision-making.

Contribution

The study introduces a novel framework integrating ergonomic risk assessment with task allocation using AND/OR Graphs, adaptable to various risk indicators.

Findings

High-risk actions are correctly identified and avoided for humans.

The framework reduces fatigue and frustration in collaborative tasks.

Effective dynamic role allocation improves ergonomic outcomes.

Abstract

By incorporating ergonomics principles into the task allocation processes, human-robot collaboration (HRC) frameworks can favour the prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for the variability of human actions and states; therefore, planning and dynamically assigning roles in human-robot teams remains an unaddressed challenge.This study aims to create an ergonomic role allocation framework that optimises the HRC, taking into account task features and human state measurements. The presented framework consists of two main modules: the first provides the HRC task model, exploiting AND/OR Graphs (AOG)s, which we adapted to solve the allocation problem; the second module describes the ergonomic risk assessment during task execution through a risk indicator and updates the AOG-related variables to influence future task allocation. The proposed framework can be combined with any time-varying ergonomic risk indicator that evaluates human cognitive and physical burden. In this work, we tested our framework in an assembly scenario, introducing a risk index named Kinematic Wear.The overall framework has been tested with a multi-subject experiment. The task allocation results and subjective evaluations, measured with questionnaires, show that high-risk actions are correctly recognised and not assigned to humans, reducing fatigue and frustration in collaborative tasks.