Human Follower Using Visual SLAM: An Innovative Solution by MechFusion AI
Overview:
At MechFusion AI, we have developed an advanced human-following robot leveraging cutting-edge Visual SLAM (Simultaneous Localization and Mapping) technology. This solution is designed to enhance human-robot interaction across various domains, including healthcare, logistics, and personal assistance.
Project Highlights:
Introduction: Our human-following robot technology combines Visual SLAM with sophisticated tracking algorithms to detect and follow individuals accurately in diverse environments.
Problem Statement: Traditional methods often fall short in ensuring efficient human tracking and safe navigation, especially in complex settings with obstacles.
Objectives:
- Develop a human-following robot using Gazebo simulation.
- Create and implement a QR code-based following algorithm.
- Evaluate the performance of this algorithm against alternative tracking methods.
- Scope: The project encompasses mobile robot navigation, image recognition, and simulation of robotic systems using Gazebo and ROS (Robot Operating System).
Methodology:
- Software Setup: Installation and configuration of Linux, ROS framework, and SLAM algorithms.
- Simulation Environment: Utilized Gazebo for virtual robot testing and Rviz for visualizing sensor data.
- Tracking Algorithms: Implemented QR code, color, and human shape tracking using advanced machine vision techniques.
- Experiments: Conducted extensive testing in environments with varying complexity to compare the effectiveness of QR tracking, color tracking, and shape tracking methods.
Results:
- QR Tracking: Demonstrated high accuracy and reliability across all tested environments.
- Color Tracking: Showed improved performance with better lighting conditions.
- Human Shape Tracking: Achieved robust results using the YOLOv3 algorithm.
- Conclusion: The QR code-based tracking method proved superior in accuracy and adaptability, enhancing robot navigation and safety. Our findings offer valuable insights for advancing human-robot interaction technologies.
- Future Directions: We plan to refine our algorithms, expand testing scenarios, and explore further applications to continue innovating in the field of robotics.
