A Human-Centered Dynamic Scheduling Architecture for Collaborative Application

TL;DR

This paper presents a human-centered, dynamic scheduling architecture for collaborative robotic systems that optimizes task allocation considering human variability and job quality, validated through experiments.

Contribution

Introduces a two-layered, real-time adaptive scheduling architecture explicitly considering human factors and job quality in collaborative robotics.

Findings

Effective task scheduling improves human-robot collaboration performance.

Real-time monitoring enables dynamic adaptation of task allocation.

Architecture enhances job quality and team efficiency.

Abstract

In collaborative robotic applications, human and robot have to work together during a whole shift for executing a sequence of jobs. The performance of the human robot team can be enhanced by scheduling the right tasks to the human and the robot. The scheduling should consider the task execution constraints, the variability in the task execution by the human, and the job quality of the human. Therefore, it is necessary to dynamically schedule the assigned tasks. In this paper, we propose a two-layered architecture for task allocation and scheduling in a collaborative cell. Job quality is explicitly considered during the allocation of the tasks and over a sequence of jobs. The tasks are dynamically scheduled based on the real time monitoring of the human's activities. The effectiveness of the proposed architecture is experimentally validated.