A Dynamic Architecture for Task Assignment and Scheduling for Collaborative Robotic Cells

TL;DR

This paper presents a two-layer dynamic architecture for task allocation and scheduling in collaborative robotic cells, enabling efficient human-robot cooperation through optimal planning and real-time adaptation.

Contribution

It introduces a novel two-layer architecture that combines optimal task allocation with reactive online scheduling for improved collaboration.

Findings

Effective task allocation considering nominal times.

Real-time adaptation to deviations and requests.

Validated on a collaborative assembly task.

Abstract

In collaborative robotic cells, a human operator and a robot share the workspace in order to execute a common job, consisting of a set of tasks. A proper allocation and scheduling of the tasks for the human and for the robot is crucial for achieving an efficient human-robot collaboration. In order to deal with the dynamic and unpredictable behavior of the human and for allowing the human and the robot to negotiate about the tasks to be executed, a two layers architecture for solving the task allocation and scheduling problem is proposed. The first layer optimally solves the task allocation problem considering nominal execution times. The second layer, which is reactive, adapts online the sequence of tasks to be executed by the robot considering deviations from the nominal behaviors and requests coming from the human and from robot. The proposed architecture is experimentally validated on a collaborative assembly job.