A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

TL;DR

This paper presents an extension of the FlexHRC framework enabling human operators to coordinate with multiple heterogeneous robots for product defect inspection tasks, optimizing task allocation in collaborative manufacturing environments.

Contribution

The paper introduces an extended FlexHRC framework that supports concurrent and sequential task allocation for human-robot teams in defect inspection scenarios, accommodating multiple robots and human interaction.

Findings

Successful implementation of the extended framework in a defect inspection use case.

Effective coordination between human operators and multiple robots demonstrated.

Enhanced flexibility in task allocation improves inspection efficiency.

Abstract

The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the \textsc{FlexHRC} framework presented in \cite{darvish2018flexible} to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended \textsc{FlexHRC} framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of product defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink Robotics and a Kuka youBot mobile manipulator.