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Updated: Dec 21, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature.

Redhwan Algabri1, Mun-Taek Choi1

  • 1School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)
|May 14, 2020
PubMed
Summary
This summary is machine-generated.

This study presents a novel robot framework for reliable human following, even with visual challenges like occlusion and changing light. The system effectively tracks targets and navigates safely to destinations, demonstrating practical application.

Keywords:
color featuredeep learninghuman followingmobile robotperson identification

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Human following is crucial for mobile robot interaction.
  • Challenges include occlusion, illumination changes, and obstacle avoidance.
  • Existing methods often struggle with dynamic environments.

Purpose of the Study:

  • To develop a robust human following framework for mobile robots.
  • To enable target tracking through occlusions and illumination variations.
  • To ensure safe navigation with obstacle avoidance during human following.

Main Methods:

  • Utilized a state-machine control framework.
  • Employed deep learning (Single Shot MultiBox Detector) for person detection and tracking.
  • Used hue-saturation-value histogram for target identification.
  • Implemented simultaneous localization and mapping (SLAM) with LIDAR for navigation and obstacle avoidance.

Main Results:

  • The robot successfully tracked the target person in an indoor environment.
  • The system demonstrated effective performance despite moderate illumination changes and multiple people.
  • The robot navigated safely to the destination while maintaining target following.

Conclusions:

  • The proposed framework is effective and practical for mobile robot human following.
  • The integration of deep learning and SLAM enables robust performance in challenging conditions.
  • This system enhances human-robot interaction capabilities for mobile robots.