Navigation System for Wheeled Robots Using SLAM GMapping and LiDAR
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Abstract
One of the primary challenges in developing autonomous robots is enabling them to operate in dynamic, unstructured environments, where traditional navigation methods often fail. For instance, self-driving cars must continuously adapt to changing road conditions, traffic, and unexpected obstacles. In this context, efficient sensors and computing systems play a vital role. LiDAR (Light Detection and Ranging) technology has proven highly effective for autonomous robots, particularly for environmental mapping and obstacle avoidance. This project utilizes the LiDAR A1 sensor to support the SLAM (Simultaneous Localization and Mapping) algorithm, allowing the robot to navigate and map its surroundings autonomously.
The Raspberry Pi 4B was chosen as the central processing unit for its capacity to handle complex algorithms in real-time and to facilitate communication between sensors and actuators. Precise motor control is achieved using an Arduino Mega, enhancing the robot's ability to respond dynamically to its environment. By integrating SLAM with LiDAR and robust motor control, this research aims to create a robot that can not only perceive its surroundings but also navigate them autonomously. This project contributes to the field of autonomous robotics, particularly in navigation and environmental mapping, offering a practical, efficient approach for robots to operate independently in complex settings.
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