Computational Implementation and Optimization of ROS-based SLAM Techniques for Wheeled Mobile Robot Navigation in Dynamic Environments

Automation has come a long way thanks to the introduction and development of technologies that are now indispensable to industrial operations. As autonomous systems have developed, they have made it feasible to enhance human labour by cooperating with them and taking over the most taxing jobs. In this work, an autonomous mobile robot (AMR) system is created that can navigate on its own throughout an unfamiliar and uncharted interior area. An environment-appropriate sensoristics system is implemented to carry out the specified mission. The data provided by the sensors is utilized by an active SLAM algorithm that expands the capabilities of SLAM and creates pathways toward unknown regions. An environment in Gazebo was created for mobile robot mapping, and lidar and odometer data were integrated throughout the simulation. It was successful to measure the slip rate of the four-wheel robot's steering in-situ, resulting in improved chassis pose accuracy. Currently, ROS and STM32 are communicating, the ROS chassis node is packaged, and it can receive speed commands, feedback odometer data, and TF transformations. Based on experiments and simulations, the system can map the environment accurately and perform precise navigation tasks.