Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation

TL;DR

This paper introduces a novel method for dynamic role allocation in human-robot collaboration that optimizes ergonomic safety and task efficiency using online measurements and adaptive algorithms.

Contribution

It presents a new approach that combines ergonomic risk prediction with adaptive role allocation using AND/OR Graphs for flexible human-robot cooperation.

Findings

Successfully controls task allocation to ensure ergonomic safety

Adapts to changing environments and task demands

Demonstrates potential for improved human-robot collaboration

Abstract

Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operator's ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.