Development and Enhancement of ROS-based SLAM Methods for the Navigation of Wheeled Mobile Robots in Dynamic Environment

In recent years, wheeled mobile robotics (WMR) for small and large-scale Industry 4.0 applications are being implemented in warehouses, factories, and smart cities. Mobile robots must navigate constantly changing dynamic environments, which present significant challenges due to the difficulty of real-time mapping, collision avoidance, and path planning optimization. This research develops an autonomous mobile robot (AMR) system capable of navigating independently through unfamiliar and uncharted indoor environments. To achieve this, a sensor system tailored to the environment is used to perform specific tasks. The data collected by these sensors is processed by an enhanced SLAM (Simultaneous Localization and Mapping) algorithm, which extends SLAM's capabilities and generates pathways to unexplored regions. A simulation environment is created in Gazebo for mobile robot mapping, integrating lidar and odometry data throughout the process. The slip rate of the four-wheel robot's steering is measured in position, leading to improved chassis pose accuracy. Currently, ROS and STM32 communicate, with the ROS chassis node packaged to receive speed commands, provide feedback from odometer data, and process transformations. Based on experiments and simulations, the system accurately maps the environment and performs precise navigation tasks.