Autonomous Navigation for TurtleBot3 Robots in Gazebo Simulation Environment

Abstract

The fast-paced growth in the field of robotics has driven the creation of autonomous navigation systems that are necessary for robots to work autonomously in diverse environments. This research targets TurtleBot3, an immensely popular robotic platform that is known for its affordability and adaptability. The objective of TurtleBot3 is to improve the capabilities within the Gazebo simulation environment, an open-source robotic simulator that provides a realistic virtual environment for testing and developing navigation algorithms for robotics. The combination of intricate sensor technologies, leading-edge control systems, and innovative artificial intelligence strategies is the core of this research, enabling autonomous navigation in complicated surroundings. The initial focus of the study is the application and utilization of path planning algorithms, such as A * (A Star) and Rapidly exploring Random Tree (RRT) and using Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations demonstrate that the A * algorithm achieved a path accuracy rate of over 95% in static environments, while the RRT algorithm proved effective in dynamic, multi-dimensional spaces. The aim of this analysis is to enhance autonomous navigation algorithms using the Gazebo simulation environment. Conducting tests in a virtual setting minimizes the risks and costs compared to real-time testing. The outcomes of this research are anticipated to advance the field of robotics and hold significant value for applications in autonomous systems, including engineering, urban mobility, and domestic assistance.