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.

Intelligent service robots are currently being developed to cater to demands in emerging areas of robotic applications, ranging from entertainment to health care. These service robots are intended to be operated by nonexpert users, and their service tasks involve direct interaction between these robots and their human users. Thus, human-friendly interactive features are generally preferred for such service robots. Humans prefer to use voice instructions, responses, and suggestions to convey ideas to their peers. However, information conveyed through natural language communication is imprecise because it tends to contain uncertain/qualitative information instead of precise quantitative information. Therefore, the ability to cope with uncertain information in natural language instructions is mandatory for human-friendly service robots. This paper presents a review of service robots and systems that can cope with uncertain information in natural language instructions. The available literature has been investigated and analyzed to identify the limitations of the existing methods and possible improvements. The identified limitations and possible improvements are presented as the outcomes of the review.

High penetration of renewable energies to smart grid technologies has lead the traditional electrical transmission and distribution network to undergo a significant evolution. Here, advanced control strategies are required to ensure the load balance in order to keep the voltage and frequency variation within an acceptable range. With the integration of dc energy sources and storages, another control constraint would be added due to the limitations in a storage system. Among recent developments in control techniques, fuzzy logic and multi agent systems have emerged due to its strong control action in an uncertain environment. Researchers have made satisfactory efforts in optimizing hybrid renewable energy sources using these technologies. This paper strives to highlight the approaches of controlling the energy storage systems, dc voltage, frequency, and ac voltage with the aid of the fuzzy logic and multi agent systems related controllers.

Service robots have been developed to enhance the living standard of people. Users of these service robots are not experts in robotic domain. Hence, they prefer to have human-friendly features in these service robots. These service robots often need to navigate toward their users when accomplishing service tasks demanded by the users. Therefore, the ability of a service robot to approach a user in a human-friendly manner would increase the rapport between the user and the robot. This paper proposes a method for human-friendly approaching of a service robot toward a user who is having a conversation with another person. The algorithm behind the proposed approaching method has been designed in such a way that the robot can replicate the natural human tendencies to a greater extent. Natural approaching behavior of humans identified from a previously conducted human study has been utilized for this purpose. The proposed approaching method has been implemented on MIRob platform and a user study has been conducted to validate the capabilities of the proposed concept. The experimental results validate that the proposed approaching method of the robot is capable of maintaining the satisfaction of the users during approaches.

To generate context-aware behaviors in robots, robots are required to have a careful evaluation of its encounters with humans. Unwrapping emotional hints in observable cues in an encounter will improve a robot’s etiquettes in a social encounter. This article presents an extended human study conducted to examine how several factors in an encounter influence a person’s preferences upon an interaction at a particular moment. We analyzed the nature of conversation preferred by a user considering the type of conversation a robot could have with its user, having the interaction initiated by the robot itself. We took an effort to explore how such preferences differ as the factors present in the surrounding alter. A social robot equipped with the capability to initiate a conversation is deployed to conduct the study by means of a wizard-of-oz (WoZ) experiment. During this study, conversational preferences of users could vary from “no interaction at all” to a “long conversation.” We changed three factors in an encounter which can be different from each other in each circumstance: the audience or outsiders in the environment, user’s task, and the domestic area in which the interaction takes place. Conversational preferences of users within the abovementioned conditions were analyzed in a later stage, and critical observations are highlighted. Finally, implications that could be helpful in shaping future social human-robot encounters were derived from the analysis of the results.

Assistive robots are developed to uplift living standards of human being. An assistive robot needs to be friendly, reliable, and understandable in order to be a human like companion. A robot should be able to understand its user effectively and the robot should be able to expand its knowledge based on experience gain from activities or conversations. Therefore the assistive robot should have a memory and a knowledge base regarding objects that it comes across in daily activities. It should be able to process data and understand relationships between attributes of objects to self-understand the need of a user. Therefore this paper propose a method to create an object memory and a knowledge base to understand relations between objects attributes and create a cognitive map to enhance interaction between human and robot which will help to make a robot more human-friendly. Conversation Management Module (CMM), Attribute Analyzing Module (AMM) and Object Knowledge Base (OKB) have been introduced in order to create a cognitive map of objects. Capabilities of the robot have been demonstrated and evaluated from experimental results.