This paper focuses on exploring and analyzing the process of robot design and hardware implementation of the studies made on the autonomous mobile robot navigation reported in the paper, “Application of Deep Q-Learning for Wheel Mobile Robot Navigation” (Mohanty, Sah, Kumar, & Kundu, 2017). Incorporating autonomous robots into daily life for serving humanity has been a long-term goal for the robotics plethora. An autonomous mobile robot has tremendous application in various environments since they work without human intervention. The robot is defined as a device that is composed of the electronic, electrical, and mechanical systems with a brain imported from computer science. In this paper, a mobile robot is introduced which was fabricated using Raspberry Pi 3 B as a processing chip, range sensors, and camera, which are used for extracting raw sensory data from the environment and feeding it to the robot. The composed mobile robot can be remotely accessed from anywhere around the globe without being in the vicinity of the robot and can be controlled by the means of any gadget, regardless of whether a portable workstation, a versatile, or a tablet, which makes it perfectly suitable for surveillance, exploration, and military applications. For training the robot in the virtual environment, a simulation model was developed in python from scratch. The pre-trained model from the simulation was deployed for further training of the robot in the actual environment. Algorithms like obstacle detection and image recognition were merged together to equip the mobile robot with necessary controls. In the end, the progress of the robot was analyzed in different real environments and the performance accuracy of the obstacle avoidance ability of the mobile robot was calculated based on hit-rate matrices and tabulated.