## 3 Autonomous Navigation of a Mobile Robot  
Autonomous mobile robots are equipped with sensors and cameras; if they observe an unexpected obstacle while maneuvering their environments, like a crowd of people or tree, or a fallen box, they will utilize a technique of navigation such as avoidance of collisions to stop, slow, or divert their path around the object and then continue with their predefined mission [59, 60]. With the integration of artificial intelligence in mobile robotics, robots can become more advance and efficient in all applications. Computer vision and image processing play a vital role in the movement of autonomous robots with higher accuracy of path planning. Various visual skills are considered desirable in computer vision, among the most important being determining the range of objects in the predefined environment [61]. An accurate and collision-free path planning is mandatory for the movement of mobile robots to perform their assigned task with greater accuracy [62]. The navigation of mobile robots in tropical conditions is the main and most important section for researchers for the real-world implementation of robots. Figure 7 illustrates the control system of a mobile robot. This system is divided into three parts: reasoning, process, and context. The reasoning is the part where the robot makes decisions and localizes the appropriate path to perform its task. The process is the part where controlling the motion of the robot by utilizing the sensors is performed or all dynamic steps performed in between two different states, and it has haptic feedback. Lastly, context is the part of the control system where the representation of the state is performed, and commands for motion are tailored to maintain the contexts of movement. In recent years, researchers have been focusing on AI-based control strategies, such as sliding mode control systems, fuzzy logic-based control systems, agent-based control systems, ANN-based control systems, etc., for the navigation of mobile robots. One of  
Figure 7: Closed-loop robot control of the sensorimotor type [63]. the major utilizations of AI in the field of mobile robots is to develop a better-quality control system for the autonomous navigation of mobile robots. The genetic algorithm with an artificial neural network is an evolutionary technique for the estimation of optimal parameters for the gimbal joints of the mobile robot to optimize the robotic models [64]. The algorithm for the navigation of mobile robots is based upon the velocity during the methodology of obstacle avoidance and the algorithm for guidance-based tracking. A fuzzy logic-based decision-maker can be developed to integrate the two predefined algorithms intelligently [65]. There are various methods that have been developed for the purpose of optimization in the planning of trajectory for the manipulators of robots. For finding an optimal trajectory, position estimation can be done through the 3 RUU robot. After that, an objective function must be defined with two minimized terms, where the first term is associated with the total operation time and the second one is associated with the combination of the squared jerk towards the trajectory [66]. Navigation is a basic and most vital problem of mobile robots. Deep reinforcement learning plays an important role in the navigation of the mobile robot because of its powerful experience and representation learning abilities. It can be implemented for indoor navigation, social navigation, local obstacle avoidance, and multi-robot navigation. Nowadays, solutions for autonomous driving are based on machine learning and artificial vision for the perception of the environment and accelerating decision-creating tasks. This technique can be utilized for the navigation of robots in the indoor environment.