A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks
Ilias El Makrini, Mohsen Omidi, Fabio Fusaro, Edoardo Lamon, Arash Ajoudani, Bram Vandcrborght
Abstract
Work-related musculoskeletal disorders (MSD) are one of the major cause of injuries and absenteeism at work. These lead to important cost in the manufacturing industry. Human-robot collaboration can help decreasing this issue by appropriately distributing the tasks and decreasing the workload of the factory worker. This paper proposes a novel generic task allocation approach based on hierarchical finite-state machines for human-robot assembly tasks. The developed framework decomposes first the main task into sub-tasks modelled as state machines. Based on capabilities considerations, workload, and performance estimations, the task allocator assigns the sub-task to human or robot agent. The algorithm was validated on the assembly of a crusher unit of a smoothie machine using the collaborative Franka Emika Panda robot and showed promising results in terms of productivity thanks to task parallelization, with improvement of more than 30% of the total assembly time with respect to a collaborative scenario, where the agents perform the tasks sequentially.
BibTeX
@inproceedings{iros2022_ahierarchicalfin,
title = {A Hierarchical Finite-State Machine-Based Task Allocation Framework for Human-Robot Collaborative Assembly Tasks},
author = {Ilias El Makrini and Mohsen Omidi and Fabio Fusaro and Edoardo Lamon and Arash Ajoudani and Bram Vandcrborght},
booktitle = {IROS 2022},
year = {2022}
}