Published Paper Details:

The goal of the project "Self-Navigation Robotics: Mastering Autonomous Path" is to create an intelligent robot car that can drive itself from a starting point to a user-specified destination while dynamically changing its course to avoid obstacles in real time. In order to accomplish flawless autonomous navigation in challenging surroundings, the project blends a powerful combination of sensor technologies, innovative algorithms, and hardware components. This study presents a unique camera module, LiDAR, ROS2, Gazebo, and Raspberry Pi self-navigating vehicle system. LiDAR enables accurate 3D mapping and obstacle recognition, while ROS2 provides seamless system integration and communication. Gazebo provides a reliable simulation environment for verifying system functionality prior to a system being physically deployed. The Raspberry Pi functions as the main processing unit, managing decision-making algorithms and combining sensor data. The camera module improves versatility in a range of circumstances by enhancing perception with visual signals in addition to LiDAR. More advancements are possible when the system architecture prioritizes scalability and real-time responsiveness. Experimental validation the system's autonomous navigation competency highlights its potential for robotics research and industrial automation applications.