An ontology to enable optimized task partitioning in human-robot collaboration for warehouse kitting operations

Abstract

Collaborative teams of human operators and mobile ground robots are becoming popular in manufacturing plants to assist humans with a lot of the repetitive tasks such as the packing of related objects into different units, an operation known as kitting. In this paper, we present an ontology to provide a unified representation of all kitting-related tasks, which are decomposed into atomic actions that are either computational involving sensing, perception, planning, and control, or physical involving actuation and manipulation. The ontology is then used in a stochastic integer linear program for optimum partitioning of the atomic tasks between the robots and humans. Preliminary experiments on a single robot, single human case yield promising results where the kitting operations are completed with lower durations and manipulation failure rates using human-robot partnership versus just the human or only the robot. This success is achieved by the robot seeking human assistance for visual perception tasks while performing the other tasks primarily on its own.