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Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS.

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This study introduces a four-wheel drive adaptive robot system for improved indoor mobile robot mapping and navigation. It enhances path planning efficiency and accuracy using ROS, Karto SLAM, and A* algorithms for precise autonomous navigation.

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Computer Vision

Background:

  • Existing mobile robot systems face challenges in indoor mapping and navigation, including low accuracy, inefficient path planning, and high sensor requirements.
  • The need for robust and efficient autonomous navigation systems is critical for various applications, from logistics to exploration.

Purpose of the Study:

  • To develop and validate a four-wheel drive adaptive robot positioning and navigation system using the Robot Operating System (ROS).
  • To address limitations in mapping accuracy and path planning efficiency for indoor mobile robots.
  • To achieve precise autonomous navigation and obstacle avoidance capabilities.

Main Methods:

  • Comparative analysis of 2D-SLAM algorithms (Gmapping, Karto SLAM, Hector SLAM) to select Karto SLAM for map building.
  • Implementation of the A* algorithm for efficient path planning and the Dynamic Window Approach (DWA) for real-time local path planning and obstacle avoidance.
  • Development of a mathematical model for four-wheel adaptive robot steering and a URDF model within the ROS framework; simulation in Gazebo integrating Lidar and odometer data.

Main Results:

  • The proposed system demonstrated high precision in environment map building.
  • The integration of Karto SLAM and A* algorithms significantly improved path planning efficiency and accuracy.
  • The system successfully achieved autonomous obstacle avoidance navigation and accurate localization, with measured slip rates enhancing chassis pose accuracy.

Conclusions:

  • The developed ROS-based four-wheel drive adaptive robot system offers a viable solution for accurate indoor mobile robot mapping and navigation.
  • The chosen algorithms and system integration provide a robust platform for autonomous tasks, overcoming previous limitations in efficiency and precision.
  • The system's performance was validated through simulation and experimental verification, confirming its effectiveness in real-world navigation scenarios.