Introducing robotics in the healthcare sector revolutionizes medical services by providing advanced treatments, medication management, and robotic assistance while overcoming resource limitations. In the current healthcare domain, an intermediate robotic communication platform is essential for distributing equal medical services, facilitating remote consultations, and maintaining the integrity of medical education, especially in rural areas and during pandemics. This work introduces ARIS, a multitasking medical service robot designed for telemedicine aspects and to facilitate remote medical education activities such as ward rounds. The prototype called ARIS 1.0 was developed, including a three-wheeled omnidirectional mobile platform, a torso and a novel movable neck mechanism with a face. The prototype robot can generate an online summarized report using its integrated language interaction and IoT-based vital sign extraction modules. The ROS-based semi-autonomous navigation facilitates the robot to be an assistive agent, allowing it to either accompany doctors or visit patients individually. Ultimately, ARIS 1.0 serves telepresence and novel regional language capabilities, specifically Sinhala-based self-communication features. This enables inter-party communication among doctors, medical students, and patients. The functionalities of ARIS 1.0 were validated in an emulated indoor environment to evaluate their feasibility. The results indicate that ARIS 1.0 is feasible for providing remote medical services. Furthermore, the paper discusses several promising research directions related to the proposed concept.

Cleaning glass surfaces is a prevailing maintenance problem in high-rise buildings. In the traditional methods of cleaning windows, hanging on ropes poses significant occupational hazards to workers. Furthermore, most glass facades feature window frames to securely fasten the glass panels to the building structure, ensuring durability and elegance. In this context, existing robotic cleaning methods are limited by their capability to move-over window frames and need more flexibility to access tight corners and curved surfaces. This paper presents a novel reconfigurable glass cleaning robot called "WiBot" to address these limitations. WiBot is a kinematic chain comprising modular linkages with a prismatic joint and two revolute joints at each end. Each revolute joint has a suction unit that enables locomotion and adhesion. Window frames are detected using image processing with an onboard camera, and design optimizations were performed to improve the robot’s capabilities. The prototype WiBot 1.0 was developed, and several experiments were conducted to evaluate the feasibility of the proposed system focusing on robot motion, window frame detection and move-over mechanism. The results show that WiBot can overcome the limitations of existing window cleaning solutions. Finally, several promising research directions are mentioned involving the proposed reconfigurable robot architecture in cleaning operations.

The rapid increase in the elderly and disabled population has been identified as a growing socioeconomic problem. Due to reasons such as a lack of reliable caretakers and the need to empower the elderly and disabled population, it is important to have assistive devices. The interactive capabilities of these devices should match the nature of the interaction that prospective users would have with their companions. Humans communicate with each other in many modalities, such as speech, hand gestures, head gestures, gaze, etc. Hand gestures have been a popular modality that has been used in these interactive devices for speech and mobility-impaired wheelchair users. There have been many gesture models that have been developed recently for hand gesture-controlled wheelchair navigation. Natural hand gestures that are used in human-human interactions include both static and dynamic gestures. Therefore, it was logical to include both of these gestures in a navigational gesture model or hand gesture-controlled navigational system. However, hand tremors that are prevalent among the elderly and disabled community could affect the nature of the hand gesture. These tremors can vary from person to person, and hence the fixed ranges cannot be used for hand features. Hand features such as palm velocity, fingertip velocity, and others will have different ranges from person to person. Due to these reasons, a static hand gesture intended by the human user could be identified as a dynamic gesture. Further, this could lead to the misrecognition of gestures defined in gesture models. Therefore, a system is proposed in this paper to validate the gestures by considering the activity of hand features in 3D regions defined for the gesture. The accuracies of the improved system for static and dynamic gestures were 0.9849 and 0.9840, which were improvements from the accuracies of 0.8994 and 0.8479.

An intelligent wheelchair is a system that evolves its functions for the well-being of humankind in the present status of robotics. Due to the active evolution of these wheelchair systems wheelchair-mounted robotic arms can be caught as the next level of development. However, the current state of intelligence is far behind the expansion and this lack causes to add extra cognitive load on handicapped users. Most of the available hindrances to autonomous operation could certainly be achieved by replicating natural human behaviors. Under these circumstances understanding human cognition on positioning a wheelchair around a table will drastically ease the autonomous object manipulation tasks by these systems. Hence to understand natural human behavior, a human study with three sub-studies was designed and reveal the prominent factors behind human cognition. Results were analyzed statistically to identify the significance of the considered factors. In lite of the study, approaching and positioning of the wheelchair mainly depend on the object position, obstacle position, and obstacles configuration within the considered workspace. Further orientation mainly depends on the object's position and approaching direction. Besides, these outcomes would be extremely beneficial in synthesizing human cognition to build human-friendly mobile robots for uplifting users' lives as well.

Itis a prevalent trend nowadays in most places that the elderly population keeps increasing. Also, there is a community that has physical disabilities, especially mobility. Due to the lack of trustworthy caretakers, the busy lives of family members, and psychological issues attributed to loneliness, intelligent service robotic devices have been developed. Vocal navigational commands consist of uncertain terms. There are methods introduced to enhance these uncertainties using spatial parameters such as obstacle distances, previous robot movements, robot localization, etc. However, they have only considered the vocal information provided by the user and deduced the rest of the information from external spatial information. In reality, users include other partial information from complementary modalities such as hand gestures and there is a significant probability that the partial information carried by the hand gestures might change the interpretation completely. Therefore, this paper presents an intelligent system that would enhance the understanding of distance-related uncertainties of vocal navigational commands using multimodal fusion of hand gesture information. Partial gesture information extracted from hand gestures has been used to interpret the distance-related uncertainties using a fuzzy logic-based approach. Experiments were conducted to validate the intelligent system and the user ratings given were used to validate the system.

There are many cognitive and psychophysical theories to explain human behavior as well as the behavior of robots. Even so, we still lack a model to perceive and predict appropriate behaviors for both the human and the robot during a human-robot encounter. Humans make an instant evaluation of their surroundings and its people before approaching a person or a situation. As robots become more common in social environments, a similar perception of the situation around a human user prior to an interaction is required. Social constraints during an interaction could be demolished by a faulty assessment. Through this paper, we discuss the requirements of a robot to proactively perceive a situation's nature and take an effort to report functional units which come into play during such an encounter. We further identify the cues that are utilized by such intelligent agents to simulate and evaluate the outcomes of their environment. From this, we discuss the requirements of a unified theory of cognition during human-robot encounters. We also highlight implications for design constraints in such a scenario.

Perception of the intention of humans prior to an interaction is a demanding skill during human-robot interaction (HRI). This skill is even more sought after during robot-initiated HRI. Initiating an interaction in an inappropriate situation can be avoided when robots are equipped with the ability to decide when to interact and when not to. Many of the existing systems investigate only a few characteristics of humans which demonstrate inner state of mind and are based on complex monitoring mechanisms which limit their use in most of the scenarios. This work presents an autoregressive model based on observable physical and emotional human cues to determine the level of interest displayed by a human towards an interaction with a robot. This model was implemented on a service robotic platform and the behavior of the robot was controlled using the model. The behavior of the robot was determined by means of proxemic approach and the nature of conversation with the human. The outcomes of the model were evaluated by analyzing user feedback in different situations inside a simulated social environment. Using the model, robot was given the ability to analyze the situation of its human user in an emotionally intelligent manner, prior to an interaction. The behavior of the model was reviewed by user feedback in order to validate the findings. Results of the experiment are presented and findings of the study are discussed.

Service robots that possess social interactive capabilities are vital to cater to the demand in emerging domains of robotic applications. A service robot frequently needs to interact with users when performing service tasks. The comfortability of users depends on the human-robot proxemics during these interactions. Hence, a service robot should be capable of maintaining proper proxemics that improves the comfort of users. The proxemics preferences of users might depend on diverse attributes of a robot, such as emotional state, noise level, and physical appearance. Therefore, it is vital to gain a better understanding of a robot's attributes which influence human-robot proxemics behavior. This paper contributes to an exploratory study to analyze the effects on human-robot proxemics preferences due to a robot's attributes; facial and vocal emotions, level of internal noises, and the physical appearance. Four sub-studies have been conducted to gather the required human-robot proxemics data. The gathered data have been analyzed through statistical tests. The test statistics reveal that facial and vocal emotions, internal noise level, and the physical appearance of a robot have significant effects on proxemics preferences of humans. The outcomes of this exploratory study would be useful in designing and developing human-robot proxemics strategies of a service robot that would enhance social interaction.

Humans prefer to use voice commands to guide their peer companions in daily assistive tasks. In human perspective, they expect same behavior from assistive robot companions as well. The paper presents an approach towards using such voice instructions based on uncertain and qualitative information to describe object placements. Consider a case in which a set of objects has to be arranged on a table in a particular spatial area. In such a situation, humans will prefer to use a single command regarding overall arrangement rather than repeating the same command for each and every object placement. In such scenarios, people will be comfortable with commands which are simple and with non-technical words. Most of such commands include uncertain spatial terms such as “Left”, “Middle”, “Right” and uncertain qualitative terms such as “Together”, “Little separately”, “Separately” to describe the arrangement. For example “Keep all objects together in the middle of the table” and “Keep objects separately on the right side of the table” can be considered. But in some situations, these commands will not give a direct idea about the placement location of the objects. For instance, “Keep the middle of the table free” can be cited. Therefore, the robot must be able to understand precisely such information in commands before executing them. The experiments are conducted in a simulated domestic environment. Results of the experiment are presented and discussed.

Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, proxemics planner of a service robot should be cable of deciding the approaching proxemics based on user behavior. This paper proposes a method to decide the approaching proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.

Assistive robots are developed for supporting the daily activities of human beings to uplift the living standards. Assistive robots should be friendly, reliable, active, comprehensible and capable of creating interactive conversations with users in order to be a friendly companion for the human. Humans tend to include uncertain terms related to directions and distances to describe or express ideas. Furthermore, an assistive robot should be capable of analyzing and understanding the numerical meaning of uncertain terms for the purpose of creating a cognitive map in order to build friendly interactions between the user and the robot. Moreover, this paper proposes a method to identify the spatial relation between the objects in a given environment and describes such environments using uncertain terms related to spatial information based on a cognitive map created by the proposed system while having interactive conversations with the user. Conversation Management Module (CMM) and Spatial Information Processor (ISP) and Cognitive Map Creator (CMC) have been introduced in order to create interactive conversations while processing the uncertain information based on a cognitive map. Capabilities of the robot has been demonstrated and validated from the experimental result.

The use of social robots in the domestic environment has increased during the past few decades. These robots are intended to maintain long-term interactions with humans while involving in a variety of tasks including daily activities, entertainment, and assisting elderly or disabled people. The ability to learn user's preferences and adapting interaction accordingly is a must for such robots. As the domestic environment consists of non-experts, social robots must possess more natural and human-friendly interaction capabilities. This paper presents an Autobiographical Memory(AM) based intelligent system which can learn user preferences through natural interactions and provide user adaptive services for each user in the multi-user domestic environment. The system is capable of learning user's preferences from hislher own statements and from another person's statements. Furthermore, the system is capable of adapting to user's hidden preference and changes of preferences easily. The robot's memory has been structured in such a way that it can easily remember the user groups and the relationship between users. This facilitates the robot for learning preferences which are common to a group of users. The system has been tested and validated using snack and beverage suggestion scenario.

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.

Service robots have become a widely used tool in human-friendly assistive tasks in many aspects including social environments. Maintaining a sustainable interaction with humans is essential in performing assistive tasks in this regard. Therefore, a robot must be equipped with intelligent cognitive skills in decision making as well as in making friendly relationships with its human user. Human-like capabilities such as initiating a conversation at the right moment without distracting and maintaining an appropriate interaction are important cues in this context. This paper presents a human study conducted by means of a wizard-of-oz (WoZ) experiment to identify the behavioral features in humans that can be utilized by an assistive robot in a domestic environment to assess the situation prior to an interaction. Both verbal and nonverbal responses of participants towards an interaction initiated by a robot were recorded and analyzed to identify human behavioral changes that portray an interest towards interaction. The experiment was conducted in a simulated domestic environment and findings of the experiment are presented and discussed so that these findings could be made use of when designing human-like social robots in future. Furthermore, human behavioral changes observed during the study are analyzed and critical observations are highlighted.

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.

Development of intelligent service robots is a growing research area within the field of robotics. Users of these service robots expect human-like interaction abilities from the robots. These service robots must be capable of navigating in human populated environments by considering the comfort of the people for having smooth interaction with the users since many of the daily routine tasks involve the navigation. Therefore, a robot's way of approaching to users during interactions must be human-friendly in order to enhance the rapport between the human users and the robot. However, the natural approaching behavior of humans has to be studied in order to implement such human-like navigation abilities in service robots. Therefore, this paper contributes in a human study conducted for identifying the approaching behavior of a third person toward two persons who are having a conversation. The study has been conducted as three sub studies to identify the parameters that alter the approaching behavior. The key aspects of the approaching behavior of humans have been identified by analyzing the experimental scenarios. The capabilities required for a service robot for implementing a human-like approaching mechanism are discussed based on the outcomes of the human study. Furthermore, the directions for future investigation of human approaching behavior through human studies are also provided.

Human friendly service robots should possess human like interaction and reasoning capabilities. Humans prefer to use voice instructions in order to communicate with peers. Those voice instructions often include linguistic notions and descriptors that are fuzzy in nature. Therefore, the human friendly robots should be capable of understanding the fuzzy information in user instructions. This paper proposes a method in order to interpret directional information in navigational user commands by considering the environment dependent fuzziness associated with the directional linguistic notions. A module called Direction Interpreter has been introduced for handling the fuzzy nature of directional linguistic notions. The module has been implemented with a fuzzy logic system that is capable of modifying the perception of the robot about the directional information according to the surrounding environment of the robot. This modification is done by weighting the output membership function with the distribution of the free space around the robot. According to the experimental results, the proposed system is capable of replicating the natural directional perception of humans that depends on the environment to a greater extent than the existing approaches.

Human-robot interaction mechanisms are being developed to cater to growing elderly and disabled population. There are still voids in achieving human-likeness before initiation of an interaction. Interaction scenario could be made interesting and effective by engraving basic cognitive skills into the robot's intelligence. Skills related to human-like interaction depends on cognitive skills and interpretation of the existing situation. Most robot users encounter a common problem with their robots. That is robot trying to interact with the user when he's engaged. In robot's perspective, the robot is not fully capable of deciding when to interact with the user. This paper presents a model to decide when to interact with the user, minimizing such failures. The proposed model has separate functional units for decision making on a user's nonverbal interaction demanding. User's availability for interaction is deduced through extracted information. The system observes a user for his bodily movements and behavior for a specified time duration. The extracted information is analyzed and then put through a module called Interaction Demanding Pose Identifier to interpret the interaction demanding of the user. The identified pose and other calculated parameters are fed into the Fuzzy Interaction Decision Making Module in order to interpret the degree of interaction demanding of the user. Interaction demanding is taken into consideration before going for direct interaction with the user. This method is implemented and tested in a simulated domestic environment with users in a broad age gap. Implementation of the method and results of the experiment are presented.

In the recent past, domestic service robots have come under close scrutiny among researchers. When collaborating with humans, robots should be able to clearly understand the instructions conveyed by the human users. Voice interfaces are frequently used as a mean of interaction interface between users and robots, as it requires minimum amount of work overhead from the users. However, the information conveyed through the voice instructions are often ambiguous and cumbersome due to the inclusion of imprecise information. The voice instructions are often accompanied with gestures especially when referring objects, locations, directions etc. in the environment. However, the information conveyed solely through these gestures is also imprecise. Therefore, it is more effective to consider a multimodal interface rather than a unimodal interface in order to understand the user instructions. Moreover, the information conveyed through the gestures can be used to improve the understanding of the user instructions related to object placements. This paper proposes a method to enhance the interpretation of user instructions related to the object placements by interpreting the information conveyed through voice and gestures. Furthermore, the proposed system is capable of adapting the understanding, according to the spatial arrangement of the workspace of the robot. Fuzzy logic system is proposed in order to evaluate the information conveyed through these two modalities while considering the arrangement of the workspace. Experiments have been carried out in order to evaluate the performance of the proposed system. The experimental results validate the performance gain of the proposed multimodal system over the unimodal systems.

Service robots are being developed as a solution for the implications associated with the aging population. They are intended to be operated by non-expert users and hence human friendly interaction abilities are preferred. Humans prefer to use voice instructions that include uncertain information, lexical symbols and notions instead of instructions with precise quantitative values. Hence, service robots must be capable of interpreting such instructions in human like manner. This paper proposes a method in order to navigate a service robot inside a domestic environment in human friendly manner by using voice instructions that include phrases with uncertain information associated with relative references such as “large table”, “table left of the door” and “table close to the door”. A module called Relative Uncertainty Interpretation (RUI) module has been introduced in order to evaluate the relative references related uncertainties. The RUI module has been implemented by considering the natural tendencies of humans. Experiments have been carried out in an artificially created domestic environment and the results of the proposed system have been compared with the results of a user study in order to evaluate the performance of the proposed system.

Assistive robots can support activities of elderly people to uplift the living standard. The assistive robots should possess the ability to interact with the human peers in a human friendly manner because those systems are intended to be used by non-experts. Humans prefer to use voice instructions that include uncertain information and lexical symbols. Hence, the ability to understand uncertain information is mandatory for developing natural interaction capabilities in robots. This paper proposes a method to understand uncertain information such as “close”, “near” and “far” in navigational user commands based on the current environment and the experience of the robot. A robot experience model (REM) has been introduced to understand the lexical representations in user commands and to adapt the perception of the robot on uncertain information in heterogeneous domestic environments. The user commands are not bounded by a strict grammar model and this enables the users to operate the robot in a more natural way. The proposed method has been implemented on the assistive robot platform. The experiments have been carried out in an artificially created domestic environment and the results have been analyzed to identify the behaviors of the proposed concept.

Service robots are being developed as a supportive aid for elderly people. Those robots are operated by non-expert users in heterogeneous domestic environments. Hence, the ability of a robot to be operated in a more natural human friendly manner enhances the overall satisfaction of the user. Humans prefer to use voice in order to convey instructions. Those voice instructions often include uncertain terms such as “little” and “far”. Therefore, the robotic assistants should possess the competency to appropriately interpret the quantitative meanings of such terms. The quantitative meaning of uncertain terms related to the spatial information depends on the spatial arrangement of the environment. This paper proposes a method in order to evaluate the uncertain information in user commands by replicating the natural tendencies of humans about the spatial arrangement of the environment. A module called Occupied Density analyzer has been deployed to analyze the occupied density distribution. A function has been defined to estimate the perceptive distance based on the occupied density distribution. The perception of the uncertain terms is adjusted according to the perceptive distance of that particular scenario. Particulars on rationale behind the proposed method are explained with due attention to the natural human tendencies. Experiments have been carried out in order to evaluate the performance and the behaviors of the proposed system.

This paper proposes a method for interpretation of fuzzy voice commands based on the vocal cues. The fuzzy voice commands include fuzzy linguistic terms like “little” and quantitative meaning of such terms depends on the environmental conditions. Therefore the robot's perception of the corresponding environment is modified by acquiring the user's perception through a series of vocal cues. The user's willingness to change the robot's perception is identified based on the vocal cues to improve the adaptation process. The primitive behaviors related to the end-effector movements of a robot manipulator are considered and evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues to adapt the robot's perception by modifying the BEN. The user's satisfactory level for the robot's movements is utilized to track the satisfaction of the user. A situation of cooperative rearrangement of the user's working space is used to illustrate the proposed system by a PA-10 robot manipulator.

This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy linguistic information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy linguistic information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy linguistic information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy linguistic information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.