Meeting sustainable development goals via robotics and autonomous systems

Abstract

Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities.

Metadata

Item Type:	Article
Authors/Creators:	Guenat, S Purnell, P Davies, ZG Nawrath, M Stringer, LC Babu, GR Balasubramanian, M Ballantyne, EEF https://orcid.org/0000-0003-4665-0941 Bylappa, BK Chen, B De Jager, P Del Prete, A Di Nuovo, A Ehi-Eromosele, CO Eskandari Torbaghan, M Evans, KL Fraundorfer, M Haouas, W Izunobi, JU Jauregui-Correa, JC Kaddouh, BY Lewycka, S MacIntosh, AC Mady, C Maple, C Mhiret, WN Mohammed-Amin, RK Olawole, OC Oluseyi, T Orfila, C Ossola, A Pfeifer, M Pridmore, T Rijal, ML Rega-Brodsky, CC Robertson, ID Rogers, CDF Rougé, C Rumaney, MB Seeletso, MK Shaqura, MZ Suresh, LM Sweeting, MN Taylor Buck, N Ukwuru, MU Verbeek, T Voss, H Wadud, Z Wang, X Winn, N Dallimer, M
Copyright, Publisher and Additional Information:	© The Author(s) 2022. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Dates:	Published: 21 June 2022 Published (online): 21 June 2022 Accepted: 6 June 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	24 Jun 2022 15:30
Last Modified:	24 Jun 2022 15:30
Status:	Published
Publisher:	Nature Research (part of Springer Nature)
Refereed:	Yes
Identification Number:	10.1038/s41467-022-31150-5
Related URLs:	Dataset
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:188404