# Parameterization and Design of Telepresence Robot to Avoid Obstacles
## 2 Background
This search for functional independence raises major questions in control theory, knowledge-based and reasoning systems, and architecture. However, the specificity of the application domain generates particular constraints, which may sometimes be antagonistic according to the concerned scientific discipline. Historically, many approaches have been developed for performing robot behaviors [3; 26; 28]. A general hierarchical scheme of autonomous vehicles (AVs) can be found in [29; 30]. An AV or robot consists of both software and hardware architectures. Some hardware elements, such as sensors, provide imperative information for the software module [8; 12; 31]. Furthermore, the software module of AVs generates control commands according to the safety and comfort of the trajectory to the actuator. The actuator controls the hardware activities of AVs, such as steering, acceleration, deceleration, etc. Much research work focuses on identifying and stabilizing the AV and developing a micro-controller-based architecture [10; 12; 32]. The core responsibility of the design is to control the driving behavior and avoid an obstacle at each instant [8; 33; 34]. Another important module is the driving behavior planner, which is a decision-making module that provides reasonable obstacle avoidance and safe driving actions [35; 36]. Another word, \"maneuver,\" is also used in the literature for this purpose, but for more clarity and consistency, the term \"behavior\" will be used to describe it throughout this paper. According to the actions generated by the behavior decision-making module, other independent attributes such as position, trajectory, orientation, and speed are considered [23; 37; 38]. A popular trajectory definition is a sequence of free-space spatiotemporal states feasible for vehicle dynamics [7; 12]. The trajectory control in the software architecture of AVs was presented by [25; 39; 40; 41] in a dynamic traffic environment. Many trajectory controllers, such as geometric and kinematic, dynamic, optimal, adaptive, model-based and classical controllers, have been implemented for trajectory tracking control [26; 35; 38; 42]. In the past literature, a trajectory planning survey was presented by [13] for autonomous ground vehicles (AGVs), and [43] proposed a comprehensive discussion of motion prediction models and risk estimators. The authors in [44] investigated the functional architecture for autonomous driving and the reasoning for distributing the principal components across the architecture. An overview of the control design methods to handle uncertain forecasts for semi-autonomous and autonomous vehicles was given by [33; 37; 45; 46; 47]. From the survey papers, it is easy to conclude that each article investigates individual components such as motion planning, behavior planning, and trajectory control rather than an integrated system [28; 44]. One important area is sensor selection and integration. Researchers are exploring using various sensors, such as cameras, lidar, radar and ultrasonic sensors, to detect obstacles and determine the robot's location. They are also investigating how to integrate these sensors best to provide a robust and reliable obstacle-detection system [48]. Another area of focus is control algorithms. Researchers are developing advanced algorithms that can accurately predict the robot's motion and path and quickly respond to environmental changes to avoid obstacles. These algorithms typically involve using sensor data to estimate the robot's state and then using that information to plan and execute safe trajectories [49]. A third area of focus is human-robot interaction (HRI). Telepresence robots are often operated by humans, who may not have much experience with robotics. Researchers are investigating how to make the control interfaces for telepresence robots more intuitive and easier to use to enable more effective human-robot collaboration [50].