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 role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

Wall cleaning robots are developed to cater to the demands of the building maintenance sector. The ability to climb vertical surfaces is one of the crucial requirements of a wall cleaning robot. Robots that can climb vertical surfaces by adhesion to a surface are preferred since those do not require additional support structures. Vacuum suction mechanisms are widely used in this regard. The suction force acting on the robot due to the negative pressure built up is used by these robots for the adhesion. A robot will fall off or overturn when the pressure difference drops down a certain threshold. In contrast, if the pressure difference becomes too high, the excessive amount of frictional forces will hinder the locomotion ability. Moreover, a wall cleaning robot should be capable of adapting the adhesion force to maintain the symmetry between safe adhesion and reliable locomotion since adhesion forces which are too low or too high hinder the safety of adhesion and reliability of locomotion respectively. Thus, the pressure difference needs to be sustained within a desired range to ensure a robot’s safety and reliability. However, the pressure difference built up by a vacuum system may unpredictably vary due to unexpected variation of air leakages due to irregularities in surfaces. The existing wall cleaning robots that use vacuum suction mechanisms for adhesion are not aware of the adhesion status, or subsequently responding to them. Therefore, this paper proposes a design for a wall cleaning robot that is capable of adapting vacuum power based on the adhesion-awareness to improve safety and reliability. A fuzzy inference system is proposed here to adapt the vacuum power based on the variation of the adhesion and the present power setting of the vacuum. Moreover, an application of fuzzy logic to produce a novel controlling criterion for a wall cleaning robot to ensure safety and reliability of operation is proposed. A fuzzy inference system was used to achieve the control goals, since the exact underlying dynamics of the vacuum-adhesion cannot be mathematically modeled. The design details of the robot are presented with due attention to the proposed control strategy. Experimental results confirmed that the performance of a robot with proposed adhesion-awareness surpasses that of a robot with no adhesion-awareness in the aspects of safety, reliability, and efficiency. The limitations of the work and future design suggestions are also discussed.

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.