Productive Multitasking for Industrial Robots

TL;DR

This paper introduces a systematic approach for enabling concurrent multitasking in industrial robots by defining modular skills that can operate independently, enhancing efficiency in small-batch production environments.

Contribution

It establishes specifications for skills that support partial execution, proposes an implementation using finite-state machines and behavior trees, and demonstrates benefits through extensive ARIAC trials.

Findings

Skills support partial execution and concurrent operation

Implementation improves robot productivity in small-batch scenarios

Experimental results show significant efficiency gains

Abstract

The application of robotic solutions to small-batch production is challenging: economical constraints tend to dramatically limit the time for setting up new batches. Organizing robot tasks into modular software components, called skills, and allowing the assignment of multiple concurrent tasks to a single robot is potentially game-changing. However, due to cycle time constraints, it may be necessary for a skill to take over without waiting on another to terminate, and the available literature lacks a systematic approach in this case. In the present article, we fill the gap by (a) establishing the specifications of skills that can be sequenced with partial executions, (b) proposing an implementation based on the combination of finite-state machines and behavior trees, and (c) demonstrating the benefits of such skills through extensive trials in the environment of ARIAC (Agile Robotics for Industrial Automation Competition).