GPS-Independent Indoor Navigation-Enabled Autonomous Wheeled Robot with Obstacle Avoidance

In certain situations, GPS-based wheeled robots are ineffective in places where GPS signals are unavailable or unreliable, such as remote areas and indoor spaces. As a result, GPS independent autonomous wheeled robots are gaining importance for applications like warehouse management, autonomous delivery, healthcare assistance, smart building automation, indoor transportation systems, and robotic assistance for elderly or disabled individuals. This paper proposes a GPS independent navigation system for wheeled robots, enabling them to autonomously follow predefined paths and to strategically avoid obstacles during its mobility. The robot modifies its route in real-time, according to obstacle detection. The wheeled robot utilizes linear regression machine learning technique to predict and avoid future obstacles. The system incorporates ultrasonic sensors for obstacle detection, wheel encoders to track wheel rotations and distance traveled, and a gyroscope for orientation control. This allows the robot to detect changes in its rotational movement and to correct its mobility or direction accordingly. By dynamically adjusting the wheeled robot's path and travel time based on sensor inputs, the system ensures smooth navigation in cluttered or remote environments. Obtained Experimental results indicates that our proposed navigation model outperforms existing systems in path efficiency and obstacle avoidance, offering a more reliable and efficient GPS-independent navigation solution.