Abstract

A dynamic and flexible manufacturing environment presents many challenges in the movement of autonomous mobile robots (AMRs), leading to delays due to the complexity of operations while negotiating even a simple route. Therefore, an understanding of rules related to AMR movement is important both from a utility perspective as well as a safety perspective. Our survey from literature and industry has revealed a gap in methodology to test rules related to AMR movement in a factory environment. Testing purely through simulations would not able to capture the nuances of shop floor interactions whereas physical testing alone would be incredibly time-consuming and potentially hazardous. This work presents a new methodology that can make use of observations of AMR behaviour on selected cases on the shop floor and build up the fidelity of those simulations based on observations. This paper presents the development of a Highway Code for AMRs, development of simulation models for an ideal AMR (based on the rules from the Highway Code), and physical testing of real AMR in an industrial environment. Finally, a behavioural comparison of an ideal AMR and a real AMR in five scenarios (taken from the shop floor of an industrial partner) is presented. This work could enable informed decisions regarding the implementation of AMRs through identification of any adverse behaviours which could then be mitigated either through improvements on the AMR or through establishing shop floor protocols that reduce the potential impact of these behaviours.

Highlights

  • Robots in the industrial sector have evolved from powerful, stationary machines into sophisticated, mobile platforms to address a broader range of automation needs

  • We introduce the notion of the ideal Autonomous mobile robots (AMRs) and real AMR

  • Scenario: The AMR travels along the vehicle path; a faster truck begins to travel in the same direction along the vehicle path; the faster truck is obstructed by the AMR

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Summary

Introduction

Autonomous mobile robots (AMRs) utilise feedback from sensors to navigate their environment [1]. AMRs have found applications in various industries due to their high efficiency and low operating costs. They are currently seen as a critical component of ‘Industry 4.0’ for ideas such as smart factories and self-organisation [3]

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