Work

Optimization of Autonomous Mobile Robot Navigation and Control Based on Slam in the Ros Environment: an Experimental Analysis Using Gazebo, Rviz, and Theconstruct.ai Simulations

Year: 2025

Type: conference-paper

Abstract: This thesis focuses on the optimization of autonomous mobile robot navigation and control based on Simultaneous Localization and Mapping (SLAM) in the Robot Operating System (ROS) environment. The research integrates state-of-the-art SLAM algorithms—EKF-SLAM, FastSLAM, ORB-SLAM, and GMapping—with classical and adaptive path planning methods such as Dijkstra, A*, D* Lite, and NMap. Experimental validation was conducted in Gazebo, RViz, and TheConstruct.ai simulation platforms, enabling analysis under both static and dynamic scenarios. The results demonstrate that ORB-SLAM provides the highest localization accuracy and mapping consistency, while NMap outperforms traditional planners in adaptability to dynamic environments. The findings suggest that a hybrid approach combining ORB-SLAM for localization with NMap for navigation offers optimal performance. The novelty of this work lies in the systematic integration of multiple SLAM and navigation algorithms within scalable simulation environments, contributing to more robust, efficient, and adaptable autonomous robot systems applicable in logistics, industrial automation, and intelligent transportation.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17263323

Authors: Khujayarov, Ilyos Shiralievich, Buriboyev, Abror Shavkatovich, Norqo'ziyev, Quvonchbek Komiljon o'g'li

Institutions: Tashkent University of Information Technology, Gachon University, Jizzakh branch of the National University of Uzbekistan named after Mirzo Ulugbek

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7084590763

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