Work

Intelligent Mobile Robot for Localization and Mapping

Localization
Mapping
LabVIEW
Block City Algorithm

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.

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