This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot’s interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems—Bluetooth Human Identification Smart Subsystem—was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.

The ongoing industrial revolution involves an increasing need for human-machine communication in all areas of everyday life. Efficient and flawless human-machine communication becomes a large challenge in crowded environments.The communication scheme used in present robotic and humanoid systems usually does not assume simultaneous interaction with multiple interlocutors (e.g. robot and two talking persons) as well as the associated mechanisms for distinguishing between speakers, which is being simplified to non-overlapping, sequential exchange of questions and answers between two interlocutors. This article presents a novel framework outlines to improve human-machine communication in the humanoid robot area. Concepts of acoustic-visual beamforming are already known, the implementation of which is an acoustic camera. However, according to the authors, combining acoustic and visual perception requires significant changes in the approach to the design of robotic systems in order to be able to take full advantage of the multi-talker capability. The perception of an interlocutor is more natural when a human is able to perceive multi-channel information. This provides the acoustic-visual sensors to be able to support beamforming of audio signal and assign these signals to every interlocutor in the engagement zone of a humanoid robot. This can be useful in a time regime adequate for a conversation.

The article presents a novel idea of Interaction Quality Sensor (IQS), introduced in the complete solution of Hybrid INTelligence (HINT) architecture for intelligent control systems. The proposed system is designed to use and prioritize multiple information channels (speech, images, videos) in order to optimize the information flow efficiency of interaction in HMI systems. The proposed architecture is implemented and validated in a real-world application of training unskilled workers—new employees (with lower competencies and/or a language barrier). With the help of the HINT system, the man–machine communication information channels are deliberately chosen based on IQS readouts to enable an untrained, inexperienced, foreign employee candidate to become a good worker, while not requiring the presence of either an interpreter or an expert during training. The proposed implementation is in line with the labor market trend, which displays significant fluctuations. The HINT system is designed to activate human resources and support organizations/enterprises in the effective assimilation of employees to the tasks performed on the production assembly line. The market need of solving this noticeable problem was caused by a large migration of employees within (and between) enterprises. The research results presented in the work show significant benefits of the methods used, while supporting multilingualism and optimizing the preselection of information channels.

This paper presents a novel user experience optimization concept and method, named User Experience Sensor, applied within the Hybrid Intelligence System (HINT). The HINT system, defined as a combination of an extensive AI system and the possibility of attaching a human expert, is designed to be used by relational agents, which may have a physical form, such as a robot, a kiosk, be embodied in an avatar, or may also exist as only software. The proposed method focuses on automatic process evaluation as a common sensor for optimization of the user experience for every process stage and the indicator for human-expert automatic session activation. This functionality is realized by the User Experience Sensor, which constitutes one of main elements of the self-optimizing interaction system. The authors present the optimization mechanism of the HINT system as an analogy to the process of building a Tower of Hanoi. The proposed sensor evaluates the user experience and measures the user/employee efficiency at every stage of a given process, offering the user to choose other forms of information, interaction, or expert support. The designed HINT system is able to learn and self-optimize, making the entire process more intuitive and easy for each and every user individually. The HINT system with the proposed sensor, implemented in a window assembly facility, successfully reduced assembly time, increased employees’ satisfaction, and assembly quality. The proposed approach can be implemented in numerous man–machine interaction applications.

Modern cities face the challenge of reconciling dynamic development with environmental protection, as well as ensuring a high quality of life for their residents. In a time of continuous urbanization, with more than half of the world’s population living in cities, city logistics plays a crucial role in managing complex urban environments. As part of the concept of sustainable development, city logistics aims to minimize the negative impact of transport on the environment, while increasing operational efficiency and improving the comfort of life of residents in urban agglomerations. This article presents the results of a comparative analysis of the intensity of sustainable urban logistics development in selected cities of the European Union. It examines various strategies that reduce ${\mathrm{CO}}_2$ emissions, improve air quality, and increase building energy efficiency. The analysis demonstrates that cities investing in sustainable urban logistics not only improve their environmental performance but also increase their attractiveness and competitiveness as desirable places to live and work.

This study assesses roadside grass biomass harvesting and allocation to collection centers using spatially explicit methods for preliminary district-heating supply-chain planning. Road-network data combined with mowing data for county roads were used to estimate theoretical, technically recoverable, and annual energy potentials in Kołobrzeg County, Poland, while high-resolution aerial imagery was used to identify roadside sections where biomass cannot be harvested, such as bridges, paved shoulders, gravel surfaces, and bicycle paths. Dijkstra’s algorithm calculated shortest network distances, after which a custom assignment procedure allocated road nodes and associated biomass to collection centers using either the baseline distance-based rule or its capacity-constrained extension. The estimated annual energy potential ranged from 14.9 to 38.1 TJ/a among the analyzed municipalities. In the two-center scenarios, the number of nodes assigned to the southern center increased from 25 under its $3000\mathrm{t}$ limit to 41 under equal $5875\mathrm{t}$ limits at both centers. The results show that combining aerial-image interpretation, biomass-to-energy estimation, and network-based allocation provides a transparent framework for identifying priority collection areas and organizing roadside biomass transport. The proposed workflow can support local authorities in preliminary biomass supply-chain planning and can be adapted to other regions with suitable spatial data.