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The COVID-19 outbreak demonstrated the viability of various remote working solutions, telepresence robots (TPRs) being one of them. High-quality video transmission is one of the cornerstones of using such solutions, as most of the information about the environment is acquired through vision. This study aims to compare the camera capabilities of four models of popular telepresence robots using compact reduced LogMAR and Snellen optometry charts as well as text displayed on a projector screen. The symbols from the images are extracted using the Google Vision OCR (Optical Character Recognition) software, and the results of the recognition are compared with the symbols on the charts. Double 3 TPR provides the best quality images of optometric charts, but the OCR results of measurements of the image on the projector do not show the clear advantage of one single model over the others. The results demonstrated by Temi 2 and Double 3 TPRs are generally better than the others, suggesting that these TPRs are more feasible to be used in teaching and learning scenarios.

Medical teleconsultation was among the initial use cases for early telepresence research projects since medical treatment often requires timely intervention by highly specialized experts. When remote medical experts support interventions, a holistic view of the surgical site can increase situation awareness and improve team communication. A possible solution is the concept of immersive telepresence, where remote users virtually join the operating theater that is transmitted based on a real-time reconstruction of the local site. Enabled by the availability of RGB-D sensors and sufficient computing capability, it becomes possible to capture such a site in real time using multiple stationary sensors. The 3D reconstruction and simplification of textured surface meshes from the point clouds of a dynamic scene in real time is challenging and becomes infeasible for increasing capture volumes. This work presents a tightly integrated, stateless 3D reconstruction pipeline for dynamic, room-scale environments that generates simplified surface meshes from multiple RGB-D sensors in real time. Our algorithm operates directly on the fused, voxelized point cloud instead of populating signed-distance volumes per frame and using a marching cube variant for surface reconstruction. We extend the formulation of the dual contouring algorithm to work for point cloud data stored in an octree and interleave a vertex-clustering-based simplification before extracting the surface geometry. Our 3D reconstruction pipeline can perform a live reconstruction of six incoming depth videos at their native frame rate of 30 frames per second, enabling the reconstruction of smooth movement. Arbitrarily complex scene changes are possible since we do not store persistent information between frames. In terms of mesh quality and hole filling, our method falls between the direct mesh reconstruction and expensive global fitting of implicit functions.

Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are many challenges in obtaining human–robot interactions regarding maneuverability, controllability, stability, drive layout and autonomy. In this paper, we proposed a stability and control design for a telepresence robot called auto-MERLIN. The proposed design simulated and experimentally verified self-localization and maneuverability in a hazardous environment. A model from Rieckert and Schunck was initially considered to design the control system parameters. The system identification approach was then used to derive the mathematical relationship between the manipulated variable of robot orientation control. The theoretical model of the robot mechanics and associated control were developed. A design model was successfully implemented, analyzed mathematically, used to build the hardware and tested experimentally. Each level takes on excellent tasks for the development of auto-MERLIN. A higher level always uses the services of lower levels to carry out its functions. The proposed approach is comparatively simple, less expensive and easily deployable compared to previous methods. The experimental results showed that the robot is functionally complete in all aspects. A test drive was performed over a given path to evaluate the hardware, and the results were presented. Simulation and experimental results showed that the target path is maintained quite well.

Background: Over the last few decades, telepresence robots (TRs) have drawn significant attention in academic and healthcare systems due to their enormous benefits, including safety improvement, remote access and economics, reduced traffic congestion, and greater mobility. COVID-19 and advancements in the military play a vital role in developing TRs. Since then, research on the advancement of robots has been attracting much attention. Methods: In critical areas, the placement and movement of humans are not safe, and researchers have started looking at the development of robots. Robot development includes many parameters to be analyzed, and trajectory planning and optimization are among them. The main objective of this study is to present a trajectory control and optimization algorithm for a cognitive architecture named auto-MERLIN. Optimization algorithms are developed for trajectory control. Results: The derived work empirically tests the solutions and provides execution details for creating the trajectory design. We develop the trajectory algorithm for the clockwise direction and another one for the clockwise and counterclockwise directions. Conclusions: Experimental results are drawn to support the proposed algorithm. Self-localization, self-driving, and right and left turn trajectories are drawn. All of the experimental results show that the designed TR works properly, with better accuracy and only a slight jitter in the orientation. The jitter is found due to the environmental factor caught by the sensors, which can be filtered easily. The results show that the proposed approach is less complex and provides better trajectory planning accuracy.

Background: The development of telepresence robots is getting much attention in various areas of human–robot interaction, healthcare systems and military applications because of multiple advantages such as safety improvement, lower energy and fuel consumption, exploitation of road networks, reduced traffic congestion and greater mobility. Methods: In the critical decision-making process during the motion of a robot, intelligent motion planning takes an important and challenging role. It includes obstacle avoidance, searching for the safest path to follow, generating appropriate behavior and comfortable trajectory generation by optimization while keeping road boundaries and traffic rules as important concerns. Results: This paper presents a state machine algorithm for avoiding obstacles and speed control design to a cognitive architecture named auto-MERLIN. This research empirically tested the proposed solutions by providing implementation details and diagrams for establishing the path planning and obstacle tests. Conclusions: The results validate the usability of our approach and show auto-MERLIN as a ready robot for short- and long-term tasks, showing better results than using a default system, particularly when deployed in highly interactive scenarios. The stable speed control of the auto-MERLIN in case of detecting any obstacle was shown.

The use of virtual reality as a safety training technology is gaining attention in the construction industry. While current studies focus mainly on the development of VR-based safety training programs, studies focusing on improving its effectiveness is still lacking. Thus, this study aims to understand the psychological process of training transfer and determine the factors that affect VR safety training effectiveness. The study analysed survey data from 248 construction workers who finished construction safety training using VR using PLS-SEM. The results show that the telepresence experienced through the VR and the risk perception of the trainees regarding occupational accidents significantly affect their satisfaction with VR safety training, which affected its effectiveness. Considering that the use of VR in the construction safety training context is still in its early stages, the results of our study, which comprehensively analyses both the technological and psychological aspects of VR safety training, could provide meaningful implications to VR training content developers. Furthermore, the theoretical approach of our study could be implemented in future studies focusing on the topic of training effectiveness.

Telepresence robots are becoming popular in social interactions involving health care, elderly assistance, guidance, or office meetings. There are two types of human psychological experiences to consider in robot-mediated interactions: (1) telepresence, in which a user develops a sense of being present near the remote interlocutor, and (2) co-presence, in which a user perceives the other person as being present locally with him or her. This work presents a literature review on developments supporting robotic social interactions, contributing to improving the sense of presence and co-presence via robot mediation. This survey aims to define social presence, co-presence, identify autonomous “user-adaptive systems” for social robots, and propose a taxonomy for “co-presence” mechanisms. It presents an overview of social robotics systems, applications areas, and technical methods and provides directions for telepresence and co-presence robot design given the actual and future challenges. Finally, we suggest evaluation guidelines for these systems, having as reference face-to-face interaction.

This paper introduces a real-time 3D reconstruction of a human captured using a depth sensor and has integrated it with a holographic telepresence application. Holographic projection is widely recognized as one of the most promising 3D display technologies, and it is expected to become more widely available in the near future. This technology may also be deployed in various ways, including holographic prisms and Z-Hologram, which this research has used to demonstrate the initial results by displaying the reconstructed 3D representation of the user. The realization of a stable and inexpensive 3D data acquisition system is a problem that has yet to be solved. When we involve multiple sensors we need to compress and optimize the data so that it can be sent to a server for a telepresence. Therefore the paper presents the processes in real-time 3D reconstruction, which consists of data acquisition, background removal, point cloud extraction, and a surface generation which applies a marching cube algorithm to finally form an isosurface from the set of points in the point cloud which later texture mapping is applied on the isosurface generated. The compression results has been presented in this paper, and the results of the integration process after sending the data over the network also have been discussed.

Transferring skills and expertise to remote places, without being present, is a new challenge for our digitally interconnected society. People can experience and perform actions in distant places through a robotic agent wearing immersive interfaces to feel physically there. However, technological contingencies can affect human perception, compromising skill-based performances. Considering the results from studies on human factors, a set of recommendations for the construction of immersive teleoperation systems is provided, followed by an example of the evaluation methodology. We developed a testbed to study perceptual issues that affect task performance while users manipulated the environment either through traditional or immersive interfaces. The analysis of its effect on perception, navigation, and manipulation relies on performances measures and subjective answers. The goal is to mitigate the effect of factors such as system latency, field of view, frame of reference, or frame rate to achieve the sense of telepresence. By decoupling the flows of an immersive teleoperation system, we aim to understand how vision and interaction fidelity affects spatial cognition. Results show that misalignments between the frame of reference for vision and motor-action or the use of tools affecting the sense of body position or movement have a higher effect on mental workload and spatial cognition.

This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.