An emerging trend in utilizing service robots in a vast range of application areas could be seen nowadays as a promising effort to uplift the living standard. These service robots are intended to be used by non-expert users, and their service tasks often require navigation in human-populated environments. Thus, human-friendly navigation behavior is expected from these robots by users. A service robot should be aware of Human–Robot Proxemics (HRP) to facilitate human-friendly navigation behavior. This paper presents a review on HRP. Both user studies conducted for exploring HRP preferences and methods developed toward establishing HRP awareness in service robots are considered within the scope of the review. The available literature has been scrutinized to identify the limitations of state of the art and potential future work. Furthermore, important HRP parameters and behavior revealed by the existing user studies are summarized under one roof to smooth the availability of data required for developing HRP-aware behavior in service robots.

The development of floor cleaning robots is an emerging area in robotics. Maximizing the area coverage is a foremost mission for a floor cleaning robot. Reconfigurable floor cleaning robots outperform floor cleaning robots with fixed morphology in the aspect of area coverage. A reconfigurable robot should be more flexible in changing its morphologies by considering the shapes of objects occupied in an environment to gain more coverage. Nevertheless, the state of the art methods of tiling robots considers only a limited number of morphologies for the reconfiguration, which is not sufficient to match the shape of an object. Therefore, this paper proposes a novel method to synthesize an appropriate morphology for a reconfigurable robot in accordance with the shape of an object. The proposed concept is named hTetro-Infi since it is not limited to a finite number of morphologies. The major novelty of the proposed concept overt the state of the art is the consideration of an infinite number of morphologies for the reconfiguration without sticking into a limited number of morphologies. Feedforward Neural Network (FNN) and Adaptive Neuro- Fuzzy Inference System (ANFIS) were used for determining the hinge angle required for synthesizing a given morphology. Different configurations of FNNs and ANFISs were trained and evaluated to find the most suitable configurations. The area coverage performance of the proposed hTetro-Infi was compared against that of the state of the art methods of an existing class of tiling robots, which considers only a limited number of morphologies, through simulations. According to the statistical conclusions, the proposed hTetro- Infi is capable of significantly improving area coverage compared to an existing tiling-theory based floor cleaning robot. Furthermore, the area coverage improvement of hTetro-Infi is noteworthy. Therefore, the proposed concept is beneficial in improving the abilities of a reconfigurable cleaning robot. Real-world experiments with the hardware platform of the robot for evaluating the performance is expected to be conducted in the next phase of the work. Furthermore, consideration of hTetro-Infi for navigation through confined areas is proposed for future work.

Floor cleaning robots have been developed to cope with the issues arisen with conventional cleaning methods that involve extensive human labor. hTetro is a self-reconfigurable floor cleaning robot that has been introduced to improve area coverage. Polyomino tiling theory is utilized by hTetro to plan area coverage. Energy usage and area coverage are distinct for different tiling arrangements, and they are often conflicting entities. Therefore, hTetro needs to maintain the tradeoff between area coverage and energy usage to improve its performance. This paper proposes a novel method to determine the tradeoff between area coverage and energy usage of a tiling theory-based self-reconfigurable floor cleaning robot per user preference. A linguistic option such as “High coverage” that represents user preference has uncertainty since fuzzy linguistic terms do not possess definitive meaning. Moreover, the meaning of such user preference depends on the present status of the robot. Thereby, a novel fuzzy inference system is proposed to determine the tradeoff between area coverage and energy usage by interpreting the meaning of user preference while accounting for the present status of the robot. A Weighted Sum Model (WSM) based Multiple-criteria decision-making (MCDM) method is adapted per user preference interpreted by the fuzzy inference system. The behavior of the proposed system has been evaluated considering heterogeneous test cases. The behavior of the test cases confirms the applicability of the proposed concept for adapting the tradeoff between area coverage and energy usage of a self-reconfigurable floor cleaning robot based on user preference.

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

Power transformers are vital assets in electric power systems, as they are essential in maintaining a reliable supply and very high in cost. Nowadays, society has become more and more dependent on the availability of power, putting pressure on the reliability, availability and cost efficiency of power supply. These assets operate under high electric stresses and although transformers are extremely energy efficient, the dissipated heat is a limiting factor for the maximum loading of power transformers. The insulating medium must be capable of dealing with large electric stresses, strong electro-mechanical forces and high temperatures. The life of a power transformer mainly depends on the condition of the paper-oil insulation system. Manufacturers often define the expected life of power transformers to be between 25 and 40 years. Some transformers in service are now approaching this age, and it is important to estimate their remaining lifetime in order to prevent premature shutdown of transformers. Knowing the condition of the transformers is an essential factor to make an economical decision for transformer replacement and maintenances. This paper presents a mathematical model to estimate the remaining life through assessment of furfural content in the oil and degree of polymerization of the insulating paper.