Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation
This addresses the problem of limited rapid redeployment of cobots in manufacturing and logistics for human workers, though it appears incremental in applying existing graph-based methods to a new context.
The paper tackles the challenge of rapidly deploying collaborative robots in changing manufacturing environments by proposing a novel method that optimizes assembly strategies and distributes effort in human-robot cooperative tasks. The approach uses adapted AND/OR Graphs and an allocation algorithm based on online ergonomic measurements, with preliminary experiments demonstrating success in ensuring safe and ergonomic conditions for human workers.
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.