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.

翻译：通过将人类工程学原则纳入任务分配过程,人类机器人协作框架可以有利于预防与工作相关的肌肉骨骼紊乱(WMSDs),在这方面,现有的离线方法没有考虑到人类行动和国家的变异性;因此,规划和动态分配人类机器人小组的作用仍是一个尚未解决的挑战。 本研究旨在建立一个人类工程学作用分配框架,在考虑任务特点和人类状况测量的情况下,对人权理事会进行选择,从而形成一个具有历史价值的人类工程学作用分配框架。 提出的框架由两个主要模块组成:第一个模块提供与工作有关的任务模式,即利用和/或图表(AOGs),我们根据分配问题进行调整;第二个模块通过风险指标说明任务执行期间的人类工程学风险评估,并更新与人类机器人小组有关的变量,以影响未来任务分配。拟议框架可以与评估人类认知和体力负担的任何时间变化性人类工程学风险指标结合起来。在这项工作中,我们测试了我们的框架,在一个名为Kinematic Werial的工作模型中引入了一种名为Kinematic Werial 和被测量的多式任务分配结果。