## 2 Background  
TRs' importance is vital and has remained a topic of interest among scientists over the last few decades. Implementing TRs in many application areas--e.g., industry, healthcare, and academia--is challenging. Much research has been carried out to achieve this task, but each approach has limitations. In the recent era, human beings' social lives have become dependent on technology. Although technologies have greatly improved the lifestyles of human beings, along with their workplaces and social gatherings, more investigations could be conducted to improve customer satisfaction and perform systematic analysis. TRs and autonomous vehicles (AVs) might be attractive alternatives in the human social ecosystem. In [1], a remote manipulator was implemented, considered to be the pioneer in robotic arms. Implementing these TRs is useful in hazardous or COVID-19 environments that are inaccessible to humans [23]. In [24], the 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 interaction environment. In [25], immersed virtual reality was developed to provide guidelines for user design. Many challenges remain, including the adjustable height [26; 27; 28; 29; 30], motion along the slope surface, system stability, and low-speed control [31; 32; 33; 34; 35; 36; 37; 38; 39]. The well-known application areas of mobile robots include ocean exploration, approaching the moon, implementation in nuclear plants [40; 41] and, recently, during COVID-19 [10; 42; 43]. It is often hard to conduct repairs in such scenarios; therefore, the alternate approach of a mobile robot to perform these tasks from remote locations is quite attractive. Moreover, the negotiation with the end consumer is condensed to mission provisions, and then automation can minimize mobile robots' communications with experts. There are many approaches that have been developed by researchers, especially for deciding and controlling the robots [34; 35]. Another approach to defining the trajectory control using the software architecture of TRs was presented by [20; 44; 45; 46]. Telepresence robots are utilized in many applications and have shown tremendous results in human-robot interaction [47; 48]. A search approach was proposed by [49]. It is easy to satisfy the environmental constraints, but with low dynamic probability and high computational complexity. The authors of [50] proposed a lattice trajectory technique requiring non-uniform sampling. Another approach suggested by [51] uses the interpolation method, but the results are not optimal, and the track is not smooth. A more general architecture of TRs is presented in [24; 25]. A TR comprises both software and hardware architectures. It contains the hardware components, e.g., biosensors, which obtain information from the patient and send it to the consultant at a remote location using the available communications technology [6; 10; 36]. It also contains components that can produce the control inputs so that the robot can move in a stable position using the actuator connected to it. These actuators control the hardware actions, e.g., motion, speed, and position [37; 38; 39]. The present work focuses on the identification and stabilization of the TR and the development of microcontroller-based architectures. The fundamental responsibility of the design is to control the driving behavior and avoid obstacles throughout the whole journey of the TR. 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 ensure safe driving with obstacle avoidance control. The term \"maneuver\" is most likely utilized in the literature to describe path planning. Still, to ensure more clarity and consistency, the term \"behaviour\" is employed to label the whole journey in the present research article.