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June 3, 20260 citationsOpen Access

Development of a Low-Cost Autonomous Mobile Robot Utilizing ROS 2 and LiDAR-Based Navigation

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MKMokshagna Anurag KankatiSPS S S Hari Chandra Hlada Markonda PSPSuryaprasad Potnuru

Key Points

  • This research aims to develop a cost-effective autonomous mobile robot using ROS 2 for navigation and mapping.
  • Designed a mobile robot based on a Raspberry Pi 4 and RPLiDAR sensor.
  • Implemented SLAM Toolbox for 2D occupancy grid mapping and utilized the Nav2 stack for autonomous navigation.
  • Incorporated a differential drive mechanism with closed-loop PID control for precise odometry.
  • Achieved an average hardware cost of ~$219 for the robotic platform.
  • Demonstrated consistent autonomous navigation performance with real-time mapping capabilities.
  • Maintained average CPU utilization between 60-70% with effective thermal management.

Abstract

This paper presents the design, development, and implementation of a compact, cost-effective autonomous mobile robot built around a Raspberry Pi 4 microcomputer and an RPLiDAR sensor for LiDAR-based navigation. The entire software architecture is built on ROS 2 Humble Hawksbill, integrating SLAM Toolbox for 2D occupancy grid mapping and the Nav2 stack for autonomous navigation with dynamic obstacle avoidance. The system incorporates a four-wheel differential drive mechanism with optical wheel encoders and closed-loop PID control for precise odometry tracking. Key Highlights: Total hardware cost: ~219 using off-the-shelf components Raspberry Pi 4B as the central processing unit running ROS 2 RPLiDAR A1 for 360° laser-based distance measurement SLAM Toolbox for real-time map generation Nav2 stack with DWA local planner for autonomous navigation Closed-loop PID control with optical wheel encoders Average CPU utilization: 60–70% with no thermal throttling Experimental results demonstrate that affordable hardware, when paired with state-of-the-art open-source software, can yield a robotic platform capable of consistent autonomous navigation in indoor environments.

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Cite This Study

Kankati et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc6cddee9eb8c0dce7aa5https://doi.org/10.5281/zenodo.20484853
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  4. 4ROS-based Multi-sensor Integrated Localization System for Cost-effective and Accurate Indoor Navigation System2024
  5. 5Evaluation of Robot Motion Trajectory Based on Selected Mapping Algorithms2024