Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson

Applications of robotics services in Smart Cities

The results of the data acquired indicate that the use of robotics is increasing throughout the various economic activities in particular regions (Rivera et al., 2020).


Information Technology


Generally utilized in Smart Cities, Cloud Robotics is a virtual infrastructure for utility computing that saves enormous amounts of data produced by sensors, monitoring devices, analysis tools, and visualization platforms. As a result, an expanded network for transferring and processing data collected about the physical world is created, ensuring the quality of service or making it available in searchable databases and repositories to significantly improve citizens’ quality of life. This technology enables autonomous abstract agents to communicate and cooperate with a cloud computing infrastructure.


A number of authors offer some suggestions for the management of data by developing algorithms to meet particular requirements. For instance, Rahman et al. (2017) create a genetic algorithm (GA) based scheme that makes it possible to identify the optimal offloading choices that help to enhancing the performance of applications in Smart Cities and enhancing the quality of various services. On the other hand, Beigi, et al. (2017) offer a scalable platform that offers millisecond processing time for applications based on a large number of sensors suitable for urban projects, which leads to a significantly improved reduction in latency and increased system efficiency in the data processing. Additionally, flexible methods that may be used for various panoramas are being created for smart cities.




A proposal to replace teachers with avatars and other remote entities that can be used to provide training and education, especially when teachers or tutors are impossible to meet was made in one article about the use of robotics in educational activities. Direct control of these devices is possible, as well as programming them to carry out particular tasks. The study focuses on the TeachLiveTM system, which is being utilized in US institutions and one university

in the United Arab Emirates for managing classrooms, delivering content, and improving teaching methods (Hughes, 2014).




Researchers are currently thinking about different approaches to improve the accessibility and caliber of these services in fields connected to population health. The kiosk of health is an autonomous robotic platform that Grigorescu et al. (2019) propose as a way to check important medical data. In order to optimize key performance indicators like patient length of stay, resource utilization rate, and average patient waiting time, Oueida et al. (2018) concentrate on attendance activities in hospital institutions, particularly with regard to queuing systems. They propose a Resource Preservation Network (RPN) structure based on the Petri Network system that describes the flow of patients in the emergency room from the moment they arrive until they are released.




Robotics are frequently used in the field of transportation to monitor air and ground traffic. In growing Smart Cities where they are used for the general and individual demands of many segments of the population, unmanned aerial vehicles (UAV) use for air traffic monitoring is becoming more widespread. To this purpose, managing UAV traffic is already a reality in many places, and as a result, study on this topic is developing for a better understanding of the steps that should be taken. In order to ensure safe interactions for the increasing number of these vehicles, Foina et al. (2015) offer solutions through a community platform that involves residents in the creation of airspace and concentrates on vehicle-city and vehicle-vehicle coordination issues.

In order to prevent accidents, ease traffic congestion, increase the availability of parking spaces, and regulate traffic flow, research is being done on the correlation between air quality and traffic intensity as well as the analysis of traffic data using robotic systems for land traffic monitoring. In light of the aforementioned, monitoring systems based on effective vehicle tracking algorithms are being implemented in a number of cities, helping to develop an intelligent urban traffic control system.

Lwowsky et al. (2017) describe an initial prototype of a pedestrian categorization and a detection system that delivers precise location data to a person in an image frame and can be implemented on mobile systems. This technology can help identify nearby pedestrians and help avoid potential mishaps.

Righetti et al. (2018) suggest building a real-time map of parking places utilizing specialized sensors that can provide periodic information to a cloud server, as well as cameras that can infer the condition of parking lots. Data that users’ mobile devices will have access to will enable time optimization, which would lower fuel use and air pollution.


Environment Issues

The generation and analysis of data pertaining to environmental issues in Smart Cities have been aided by the usage of sensors in these cities. It was proposed a simulator based on city mapping using 3D technology, specifying locations displaced with virtual systems that continuously collect environmental data and send it to a public cloud for analysis. These systems include sensors for noise, air pollution, humidity, temperature, and ultrasonics. Additionally, Rahman et al. (2017) place an emphasis on the analysis of data collection for these kinds of decisions, such as wastewater monitoring during heavy rains, anticipating the risk of flooding to promptly warn the populace, or limiting vehicle traffic in areas with high air pollution. The authors emphasize the need for extremely precise and cautious decision-making when using environmental data, with an eye toward the decisions’ potentially significant influence on residents and their behavior.


Professional Services


In terms of cleaning services, papers discuss how important it is to use robotics-based technology in the treatment of wastewater (i.e., sewage systems) by increasing inspection accuracy and maximizing the city’s sewage cleaning capabilities, as the current procedures call for hazardous and unhealthy working conditions. The system should keep track of sewage and water systems, air and sediment monitoring, and sewage status in order to spot sewage segments where sediment buildup or structural flaws have restricted their functionality. Remote operations, video and picture collection, digitalization and mapping, communication, and teleoperation can all be used to carry out this kind of action. This environment includes sidewalk pavement inspection and sidewalk sweeping, garbage detection, and park management (Rivera et al., 2020).




The most common applications of robotic services in Smart Cities are in the areas of surveillance and emergency response. Specific uses for surveillance systems have significantly decreased crime, and as a result, expenses associated with crime and victimization.


Social assistance


The Raven project, an interactive robotic object (IRO) intended to address the issues of homeless people’s fundamental requirements through indirect interaction with the rest of the population through virtual messaging, is put out in the social sphere. In this instance, the IRO manifests physically as a mailbox that is positioned on the main city streets with the goal of providing basic necessities, such as the precise food and services that homeless people in community centers want. As a result, bystanders can access and handle these requests either individually or collectively (Tan et al., 2017).



UAVs have been utilized for tourism in Smart Cities to assist tour guides in leading tourists to a variety of locations as well as to communicate educational and audiovisual data through mobile applications. On the other hand, Vazquez et al. (2016) proposes improvements for passenger pleasure based on new technologies take into account their views and assessments for the creation of practical applications and tailored services.


The applications could be many. In the following video, some of the available solutions regarding Security, Logistics, and Maintenance are presented.



Source: Capra Robotics (more info available at: Capra Hircus smart-city – Robotic Infrastructure & Services Provider | Robot Center)


Additionally, in the following video, you can see several different applications of robotics services in the tourism sector:



Rivera, R., Amorim, M., & Reis, J. (2020, June). Robotic services in smart cities: An exploratory literature review. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-7). IEEE.

Rahman, A., Jin, J., Cricenti, A., Rahman, A., & Panda, M. (2017, December). Motion and connectivity aware offloading in cloud robotics via genetic algorithm. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.

Beigi, N. K., Partov, B., & Farokhi, S. (2017, October). Real-time cloud robotics in practical smart city applications. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1-5). IEEE.

Hughes, C. E. (2014, November). Human surrogates: Remote presence for collaboration and education in smart cities. In Proceedings of the 1st International Workshop on Emerging Multimedia Applications and Services for Smart Cities (pp. 1-2).

Grigorescu, S. D., Argatu, F. C., Paturca, S. V., Cepisca, C., Seritan, G. C., Adochiei, F. C., & Enache, B. (2019, March). Robotic platform with medical applications in the smart city environment. In 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (pp. 1-6). IEEE.

Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., & Baker, T. (2018). An edge computing based smart healthcare framework for resource management. Sensors, 18(12), 4307.

Foina, A. G., Sengupta, R., Lerchi, P., Liu, Z., & Krainer, C. (2015, November). Drones in smart cities: Overcoming barriers through air traffic control research. In 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) (pp. 351-359). IEEE.

Lwowski, J., Kolar, P., Benavidez, P., Rad, P., Prevost, J. J., & Jamshidi, M. (2017, June). Pedestrian detection system for smart communities using deep Convolutional Neural Networks. In 2017 12th System of Systems Engineering Conference (SoSE) (pp. 1-6). IEEE.

Righetti, F., Vallati, C., & Anastasi, G. (2018, June). IoT applications in smart cities: A perspective into social and ethical issues. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 387-392). IEEE.

Tan, H., Yang, F., Tummalapalli, N. T., Bhatia, C., & Barde, K. (2017, March). Raven: A street robot to address homelessness. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 397-398).

S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, and M. Norambuena, ‘Model Predictive Control for Power Converters and Drives: Advances and Trends’, IEEE Transactions on Industrial Electronics, vol. 64, no. 2, pp. 935–947, Feb. 2017