Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development
2. Artificial Intelligence, Robotics, and Their Application in (Smart) Cities
- production controlled by machines
- real-time production, where intelligent machines determine the optimal utilization capacity of production
- decentralization of production, where the machine is self-organized
- no alterations to machines required by humans.
- Automation, but also using computer programs for open data and to enable big data for analytics (e.g., as part of London’s industrial strategy; Hong Kong’s plan for big data analytics; evident in Lyon; as part of Moscow’s Industry 4.0; and Ottawa’s predictive analytics and machine learning for enhanced operations).
- Decision-making (e.g., acting towards preventing managerial errors and facilitate making optimal decisions in Moscow—applied to the economy and governance).
- Education (e.g., Eindhoven’s high-tech campus to foster high-tech hardware innovations); access via training and e-services via “one-stop shops” (e.g., Helsinki; Hong Kong—to nurture young talent; also evident in Cape Town; Lyon’s robots to teach mathematics and in junior high; robotic assistants and professional retraining in Moscow; and in Sydney as part of National Centre of Indigenous Excellence after school program and experiential learning in the City of Sydney Libraries).
- Smart infrastructure (e.g., for e-services in Hong Kong; London, including sensors for improved energy managed as part of Digital Greenwich; sensors used in Moscow for automation/automatic processing; and New York’s Robotic Monitoring Network for water quality, etc.).
- Smart mobility (e.g., Dubai Autonomous Transportation Strategy; Melbourne’s robot bike garages; and Toronto’s Quayside to reduce congestion through last mile delivery).
Conflicts of Interest
- Hall, W.; Pesenti, J. Growing the Artificial Intelligence Industry in the UK. Department for Digital, Culture, Media & Sport and Department for Business, Energy and Industrial Strategy. 2017; p. 78. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/652097/Growing_the_artificial_intelligence_industry_in_the_UK.pdf (accessed on 16 January 2020).
- Ingwersen, P.; Serrano-López, A.E. Smart city research 1990–2016. Scientometrics 2018, 117, 1205–1236. [Google Scholar] [CrossRef][Green Version]
- Adunadepo, A.-M.D.; Sunday, O. Artificial Intelligence for Sustainable Development of Intelligent Buildings. In Proceedings of the 9th CIDB Postgraduate Conference, Cape Town, South Africa, 1–4 February 2016; p. 10. [Google Scholar]
- Boenig-Liptsin, M. AI and robotics for the city: Imagining and transforming social infrastructure in San Francisco, Yokohama, and Lviv. Field Actions Sci. Rep. 2017, 17, 16–21. [Google Scholar]
- Macrorie, R.; Marvin, S.; While, A. Robotics and automation in the city: A research agenda. Urban Geogr. 2019. [Google Scholar] [CrossRef][Green Version]
- Pacheco, A.; Cano, P.; Flores, E.; Trujillo, E.; Marquez, P. A smart classroom based on deep learning and osmotic IoT computing. In Proceedings of the 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI), Bogota, Colombia, 3–5 October 2018; p. 6. [Google Scholar] [CrossRef]
- Feng, L.; Liu, F.; Shi, Y. City brain, a new architecture of smart city based on the Internet brain. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanjing, China, 9–11 May 2018; p. 8. [Google Scholar] [CrossRef][Green Version]
- Wang, Y.; Hu, X.; Dai, W.; Zhou, J.; Kuo, T. Vocal emotion of humanoid robots: A study from brain mechanism. Sci. World J. 2014, 2014, 216341. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Tian, Y.-H.; Chen, X.-L.; Xiong, H.-K.; Li, H.-L.; Dai, L.-R.; Chen, J.; Xing, J.-L.; Chen, J.; Wu, X.-H.; Hu, W.-M.; et al. Towards human-like and transhuman perception in AI 2.0: A review. Front. Inf. Technol. Electron. Eng. 2017, 18, 58–67. [Google Scholar] [CrossRef]
- Torras, C. Service robots for citizens of the future. Eur. Rev. 2016, 24, 17–30. [Google Scholar] [CrossRef][Green Version]
- Rahman, A.A.; Hamid, U.Z.A.; Chin, T.A. Emerging technologies with disruptive effects: A review. Perintis eJournal 2017, 7, 111–128. [Google Scholar]
- Kaivo-oja, J.; Roth, S. The Technological Future of Work and Robotics. ZBW—Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft, Kiel und Hamburg, 2015. Available online: https://hdl.handle.net/10419/118693 (accessed on 16 January 2020).
- Del Casino, V.J., Jr. Social geographies II: Robots. Prog. Human Geogr. 2016, 40, 846–855. [Google Scholar] [CrossRef]
- Grieco, L.A.; Rizzo, A.; Colucci, S.; Sicari, S.; Piro, G.; di Paola, D.; Boggia, G. IoT-aided robotics applications: Technological implications, target domains and open issues. Comput. Commun. 2014, 54, 32–47. [Google Scholar] [CrossRef]
- Nikitas, A.; Kougias, I.; Alyavina, E.; Tchouamou, E.N. How can autonomous and connected vehicles, electromobility, BRT, hyperloop, shared use mobility and mobility-as-a-service shape transport futures for the context of smart cities? Urban Sci. 2017, 1, 36. [Google Scholar] [CrossRef][Green Version]
- Dia, H. The real-time city: Unlocking the potential of smart mobility. In Proceedings of the Australasian Transport Research Forum 2016 Proceedings, Melbourne, Australia, 16–18 November 2016; p. 22. [Google Scholar]
- National Science and Technology Council. Preparing for the Future of Artificial Intelligence; Executive Office of the President of the United States, National Science and Technology Council, Committee on Technology: Washington, DC, USA, October 2016; p. 58. Available online: https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf (accessed on 16 January 2020).
- Tripathi, A. Internet of Things: The key enabler of smart cities in India. Int. J. Bus. Administr. Manag. Res. 2016, 2, 15–19. [Google Scholar]
- Engin, Z.; Treleaven, P. Algorithmic government: Automating public services and supporting civil servants in using data science technologies. Comput. J. 2018, 62, 448–460. [Google Scholar] [CrossRef][Green Version]
- Serrano, W. Digital systems in smart city and infrastructure: Digital as a service. Smart Cities 2018, 1, 134–154. [Google Scholar] [CrossRef][Green Version]
- Wei, Z.; Peters, M.A. ‘Intelligent capitalism’ and the disappearance of labour: Whitherto education? Educ. Philos. Theory 2019, 51, 757–766. [Google Scholar] [CrossRef]
- Pîrvu, B.C.; Zamfirescu, C.B. Smart factory in the context of 4th industrial revolution: Challenges and opportunities for Romania. IOP Conf. Series Mater. Sci. Eng. 2017, 227, 10. [Google Scholar] [CrossRef]
- Madakam, S.; Ramaswamy, R. 100 new smart cities (India’s smart vision), IEEE Xplore (06 August 2015). In Proceedings of the 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), Riyadh, Saudi Arabia, 17–19 February 2015; pp. 1–6. [Google Scholar] [CrossRef]
- Camarinha-Matos, L.; Afsarmanesh, H. Collaborative systems for smart environments: Trends and challenges. In Proceedings of the 15th Working Conference on Virtual Enterprises (PROVE), Amsterdam, The Netherlands, 6–8 October 2014; IFIP Advances in Information and Communication Technology, AICT-434. Springer: Berlin/Heidelberg, Germany, 2014; pp. 3–15. [Google Scholar] [CrossRef]
- El-Bendary, N.; Fouad, M.M.M.; Ramadan, R.A.; Banerjee, S.; Hassanien, A.E. Smart environmental monitoring using wireless sensor networks. Chapter 23. In Wireless Sensor Networks: From Theory to Applications; El Emary, I.M.M., Ramakrishnan, S., Eds.; CRC Press: Boca Raton, FL, USA, 2013; pp. 733–755. [Google Scholar] [CrossRef]
- Liebig, T.; Piatkowski, N.; Bockermann, C.; Morik, K. Predictive trip planning—Smart routing in smart cities. In Proceedings of the EDBT/ICDT 2014 Joint Conference, Athens, Greece, 28 March 2014; p. 8. [Google Scholar]
- Meilland, M.; Comport, A.I.; Rives, P. Dense omnidirectional RGB-D mapping of large scale outdoor environments for real-time localisation and autonomous navigation. J. Field Robot. 2015, 32, 474–503. Available online: https://hal.inria.fr/hal-01010429 (accessed on 16 January 2020). [CrossRef][Green Version]
- Ren, Y.; Liu, W.; Liu, Y.; Xiong, N.N.; Liu, A.; Liu, X. An effective crowdsourcing data reporting scheme to compose Cloud-based services in mobile robotic systems. IEEE Access 2018, 54683–54700. [Google Scholar] [CrossRef]
- Chamoso, P.; de la Prieta, F. Swarm-based smart city platform: A traffic application. Adv. Distrib. Comput. Artif. Intell. J. 2015, 4, 89–98. [Google Scholar] [CrossRef][Green Version]
- Munoz, J.M.; Naqvi, A. Artificial intelligence and urbanization: The rise of the Elysium City. J. Econom. Polit. Econ. 2017, 4, 1–13. [Google Scholar] [CrossRef]
- Omar, M.; Mehmood, A.; Choi, G.S.; Park, H.W. Global mapping of artificial intelligence in Google and Google Scholar. Scientometrics 2017, 113, 1269–1305. [Google Scholar] [CrossRef]
- Cappelli, M.A. Regulation on Safety and Civil Liability of Intelligent Autonomous Robots: The Case of Smart Cars. Ph.D. Thesis, University of Trento, Trento, Italy, 2015; p. 213. [Google Scholar]
- Hislop, D.; Coombs, C.; Taneva, S.; Barnard, S. Impact of Artificial Intelligence, Robotics and Automation Technologies on Work; Chartered Institute of Personnel and Development/CIPD: London, UK, 2017; p. 31. [Google Scholar] [CrossRef]
- Ramadoss, T.S.; Alam, H.; Seeram, R. Artificial intelligence and Internet of Things enabled circular economy. Int. J. Eng. Sci. 2018, 7, 55–63. [Google Scholar] [CrossRef]
- Marvin, S.; While, A.; Kovacic, M.; Lockhard, A.; Macrorie, R. Urban Robotics and Automation: Critical Challenges, International Experiments and Transferable Lessons for the UK. UK-RAS Network, 2018. Available online: https://www.ukras.org/wp-content/uploads/2018/09/UK_RAS_wp_Urban_010618_print.pdf (accessed on 16 January 2020).
- Lee, J.H.; Hancock, M. Toward a framework for smart cities: A comparison of Seoul, San Francisco and Amsterdam. In Proceedings of the Smart Green Cities Conference: Innovations for Smart Green Cities: What’s Working, What’s Not, What’s Next, Palo Alto, CA, USA, 26–27 June 2012; p. 24. [Google Scholar]
- Lee, J.H.; Hancock, M.G.; Hu, M.-C. Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technol. Forecast. Soc. Change 2014, 89, 80–99. [Google Scholar] [CrossRef]
- Thornbush, M.; Golubchikov, O. Sustainable Urbanism in Digital Transitions, From Low Carbon to Smart Sustainable Cities; Springer: Cham, Germany, 2020. [Google Scholar] [CrossRef]
- Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power; Profile Books: London, UK, 2019. [Google Scholar] [CrossRef]
- Nagenbord, M. Urban robotics and responsible urban innovation. Ethics Inf. Technol. 2018. [Google Scholar] [CrossRef][Green Version]
- Telenor Group. Tech Trends 2020. Available online: https://www.telenor.com/innovation/research/tech-trends-2020/ (accessed on 16 January 2020).
|Urban Challenge||The Potential of Robotics and Autonomous Systems|
|Congested transport infrastructure in growing cities||Automatic autonomous vehicles (AVs) allow more efficient use of transport infrastructure and can radically reduce the demand for parking in central areas and free up valuable space for housing and recreation;|
Automated traffic control systems making use of artificial intelligence (AI) and real-life sensor information;
Unmanned aerial vehicles (UAVs) exploit underused urban airspace.
|Low carbon energy networks and ecological management||Automation enables buildings and infrastructure to respond to climate change (e.g., regulating energy use and comfort, air quality); |
Sensors and AI can underpin the development and management of green infrastructure.
|Assisted living for an aging population and inclusion||Automated and robotic health and social care support assisted living. Scope to extend age- friendly urban environments.|
AVs extend personal mobility.
|Infrastructure maintenance and repair||More efficient monitoring, repair, and control through robotics, especially in contexts where human accessibility is difficult or unpleasant.|
|Controlled internal environments for leisure and food||Automation and AI provide the climate control needed to manage advances in controlled internal environments for food growing and leisure.|
|Urban security and policing||UAVs and automated robotic policing help extend policing and surveillance.|
|City (Country)||Term Frequency|
|Dubai (UAE)||Artificial intelligence (1) = 1|
|Eindhoven (The Netherlands)||Robot (1) = 1|
|Helsinki (Finland||Artificial intelligence (3), robot (1) = 4|
|Hong Kong (China)||Artificial intelligence (2), robot (1) = 3|
|London (UK)||Artificial intelligence (5), AI (1), robot (1) = 7|
|Lyon (France)||Robot (4) = 4|
|Melbourne (Australia)||Robot (1) = 1|
|Moscow (Russia)||Artificial intelligence (5), AI (6), robot (7) = 18|
|New York (USA)||Robot (1) = 1|
|Ottawa (Canada)||Artificial intelligence (5) = 5|
|Sydney (Australia)||Robot (2) = 2|
|Toronto (Canada)||Robot (1) = 1|
|Total:||Artificial intelligence (21), AI (7), robot (20) = 48|
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Golubchikov, O.; Thornbush, M. Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development. Smart Cities 2020, 3, 1133-1144. https://doi.org/10.3390/smartcities3040056
Golubchikov O, Thornbush M. Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development. Smart Cities. 2020; 3(4):1133-1144. https://doi.org/10.3390/smartcities3040056Chicago/Turabian Style
Golubchikov, Oleg, and Mary Thornbush. 2020. "Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development" Smart Cities 3, no. 4: 1133-1144. https://doi.org/10.3390/smartcities3040056