Imad-Eddine NACIRI
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Intelligent Mobile Robot for Localization and Mapping

Designed and implemented an intelligent mobile robot capable of autonomously navigating a known indoor environment. The robot employs the Block City (Manhattan) algorithm for localization and mapping, utilizing an Arduino board and LabVIEW for graphical interface development.

3/3/2023
Live Demo View Code
LocalizationMappingLabVIEWBlock City Algorithm
Mobile Robot Project
GitHub Repository
Overview

This project involves the development of an intelligent mobile robot that navigates autonomously within a known indoor environment. The system utilizes a pre-generated map for navigation and a line-tracking localization method (Suivi de ligne). Task planning and execution are based on the Block City (Manhattan) algorithm. The graphical user interface (GUI) for real-time monitoring and control is built using LabVIEW.

Implementation Details

  1. Localization and Navigation

    • The robot uses a line-tracking localization technique to follow predefined paths.
    • Odometry and sensor data, including ultrasonic sensors, help with localization and obstacle avoidance.

  2. Mapping

    • The robot operates in a known environment with a pre-generated map using Cartesian coordinates.
    • Sensors and algorithms continuously update the map during navigation to account for environmental changes.

  3. Task Planning

    • The Block City (Manhattan) algorithm is employed for calculating distances and optimal paths for task execution.
    • Tasks include simple object manipulation like picking and placing objects within the environment.
  1. User Interface
    • LabVIEW is used to create a real-time GUI that displays the map, robot’s location, and task progress.
    • The GUI allows for intuitive control and monitoring of the robot’s status.
  1. Hardware
    • The robot is built on an Arduino platform using motors and sensors to interact with the environment.
    • It includes modules for communication via RF and is capable of receiving commands from external devices.
Technologies Used
  • Arduino: Control and communication, sensor integration.
  • LabVIEW: For building the graphical user interface (GUI).
  • Block City (Manhattan) Algorithm: For task planning and navigation.
  • Line-Tracking Localization: For position estimation in indoor environments.
  • Ultrasonic Sensors: For obstacle detection and environment sensing.
Results & Findings
  • The mobile robot demonstrated effective indoor navigation, successfully completing tasks such as picking and placing objects.
  • The real-time GUI built using LabVIEW significantly enhanced the usability and monitoring of the robot’s activities.
  • The robot’s localization method provided accurate navigation within the predefined map.
Future Improvements
  • Advanced Localization: Implement SLAM (Simultaneous Localization and Mapping) for dynamic environments.
  • Task Expansion: Integrate more complex tasks and improve task planning algorithms.
  • Energy Efficiency: Implement power management techniques for better autonomy.

Contributors

  • Imad-Eddine NACIRI
  • Achraf Berriane
  • Errouji Oussama