In the recent era, the social lives of human beings have depended on technology. Although technologies have greatly improved the lifestyle of human beings, including in the workplace and social gatherings, more investigation can be undertaken to achieve customer satisfaction and systematic analysis [18,19,20,21,22,23]. TRs and autonomous vehicles (AVs) might be attractive alternatives in the human social ecosystem. In [24], a remote manipulator, considered the pioneer robotic arm, was implemented. Implementing these TRs is useful in a COVID-19 or otherwise hazardous environment that is inaccessible to humans [25,26,27,28]. In [29], researchers developed TRs for offices, healthcare systems, and nursing homes. Another useful application is augmented virtual reality, which is useful to simulate the feeling of a human–robot interactive environment [30,31,32,33]. In [34], immersed virtual reality was developed to provide guidelines for user design. Many challenges remain, including the implementation of adjustable height [22], motion along the slope surface [35], system stability [36], and low-speed control [37,38,39,40,41].

The well-known application areas of mobile robots include ocean exploration, approaching the moon, implementation in nuclear plants [42,43,44,45], and, recently, COVID-19 [46,47,48,49,50]. It is often difficult to repair in such scenarios; therefore, the alternate approach of a mobile robot to accomplishing these tasks from a remote location is quite demanding. In addition, the negotiation with the end consumer is condensed to mission provisions, and then automating mobile robots’ communications with experts minimize it. Total operational autonomy is also required, especially in perception, decision, and control. However, the specificity of the application domain generates particular constraints which may sometimes be antagonistic, according to the relevant scientific discipline [36,37]. The trajectory control in the software architecture of TRs is presented by [22,50,51,52,53,54] in a dynamic traffic environment. Telepresence robots are utilized in many applications, and have shown tremendous results in human–robot interactions [49,50].

A more general architecture of the telepresence robot is presented in [26,37]. A telepresence robot comprises both software and hardware architectures. It contains hardware components, e.g., biosensors, which obtain information from the patient and send it to the consultant at a remote location using the available communication technology [6,10,38]. It also contains components which can produce the control inputs necessary for the robot to move in a stable position using the actuator connected to it. These actuators control the hardware actions, e.g., motion, speed, and position [39,40,41]. The presented work focuses on the identification and stabilization of the TR and the development of micro-controller-based architecture. The core responsibility of the design is to control the driving behavior and to avoid obstacles in the TR’s track The design also contains the module with the best driving path, called the decision-making module. The function of this module is to provide the best path and safe driving with obstacle avoidance control. The term “maneuver” is most likely utilized in the literature to describe path planning. Still, for clarity and consistency, the term “behavior” is employed to label the entire journey of the presented research article. According to the activities generated by the mini-computer in computing, this study also considered other independent attributes such as position, trajectory, orientation, and speed.

Much research has been conducted in the healthcare system using the methods outlined in [55,56,57,58]. The benefits of employing these approaches in the healthcare environment have been evident during the COVID-19 pandemic, when it was required that doctors keep physical distance from their patients to protect themselves. However, to further improve and diagnose the patient, doctors must interact with them; here, the demand for telepresence robots arises. To send robots to the patient ward, care must be taken to avoid collisions of the robots with various obstacles in the hospital environment. Researchers need to propose algorithms for the proper operation of telepresence robots. Each approach has its pros and cons, the proposed approach included, and the idea is to implant a telepresence robot into the hospital environment with fewer obstacles. Utilizing the proposed approach, the doctors help to obtain an initial interaction with the patient and the telepresence robot while maintaining a safe distance. The first try was conducted considering the parameters given in Table 1.

A review of the literature review found that most human–robot interactions were implemented efficiently, except for disconnection or delays [55]. The proposed approach is comparatively easy to implement, more flexible, and efficient. Further, various factors, e.g., design variations and human interaction deficiencies, were discussed in terms of the capability of object avoidance and new peripheral connection to exhibit better human-like behavior remotely.

The research gaps have been clearly mentioned and are now highlighted as:

• There is a dire demand for a telepresence robot to be designed that could be utilized in the pandemic situation. A safe physical distance between the target and the transmitter is usually required.
• The design of such a robot, capable of human–robot interaction, is a popular topic to explore, and is receiving much attention in academia, industry, and healthcare systems.

The telepresence robot is an extremely nonlinear system with a gearbox for power transmission, and its precise mathematical model is not simple to derive. Therefore, a system identification approach was implemented to find the approximate model of the system [59]. The root locus method was used to design a speed controller by introducing appropriate poles and zeros [59]. The results validated the identified model of the system. The results were also compared with the A* algorithm [60] to highlight the importance of the proposed work.

A more generalized diagram is shown in Figure 1, equipped with a telepresence robot (auto-MERLIN). The mobile robot aimed to equip auto-MERLIN to leave prescribed paths, navigate, detect obstacles, and avoid them. It required entirely new control electronics to be developed. The robot utilized the powerful direct current (DC) motor TruckPuller3 7.2 V for the drive and the powerful model-equipped servo motor HiTec HS-5745MG for steering [16]. The drive motor was equipped with an optical position encoder from the company M101B MEGATRON Elektronik AG & Co. [17], which the speed and direction can determine.

The TR is designed to maneuver around in dynamic environments (i.e., offices), encountering fixes and moving obstacles. It is worth underscoring the challenges for the telepresence robot while maneuvering in a distant place, controlled by a commanding user. There are various approaches which are worth mentioning. The background section can be further enhanced by adding the following approaches, with each approach’s limitation given in Table 2.

None of the previous studies considered a non-technologically-oriented controller to operate telepresence robots remotely. It is not possible for a telepresence robot to be used as a simple on–off to cover all behaviors displayed by remote telepresence robots. Despite a simple user controller technique, it appears that previous telepresence robot controllers had no control over expressive material, nor what we consider to be the need to rationalize the design to control telepresence robots.