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Obstacle Avoidance Technique for Mobile Robots at Autonomous Human-Robot Collaborative Warehouse Environments.

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Summary
This summary is machine-generated.

This study introduces an integrated fuzzy logic and convolutional neural network (CNN) technique for mobile robot obstacle avoidance in human-robot collaboration (HRC). The method enhances autonomous navigation and safety in dynamic industrial settings.

Keywords:
human-robot coexistenceindustrial environmentrobot navigationsafety collaborationshareable workspace

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Human-robot collaboration (HRC) necessitates advanced navigation for mobile robots in dynamic environments.
  • Ensuring safety and productivity in HRC tasks is a significant challenge, requiring intelligent obstacle avoidance.
  • Existing methods may struggle with real-time adaptation to complex, changing workspaces.

Purpose of the Study:

  • To develop and evaluate an integrated fuzzy logic and convolutional neural network (CNN) based obstacle avoidance technique for mobile robots.
  • To enhance autonomous navigation capabilities and ensure the safety of personnel and equipment during HRC tasks.
  • To provide a robust solution for real-time adaptation and safe interaction in industrial settings.

Main Methods:

  • An integrated approach combining fuzzy logic rules and a convolutional neural network (CNN) for object detection during robot movement.
  • Utilizing the Robot Operating System (ROS) and Gazebo for simulation-based testing of the obstacle avoidance system.
  • Implementing a control system that adjusts robot velocity and yaw for dynamic obstacle avoidance.

Main Results:

  • The proposed technique effectively detects objects and enables autonomous navigation for mobile robots.
  • The system demonstrated real-time adaptation capabilities in simulated dynamic and complex industrial environments.
  • Successful avoidance of both static and moving obstacles, including humans, was achieved.

Conclusions:

  • The integrated fuzzy logic and CNN approach provides an effective solution for mobile robot obstacle avoidance in HRC.
  • The developed technique enhances operational safety without compromising productivity in collaborative robotics.
  • The framework offers a reliable method for safe human-robot interaction in dynamic industrial workspaces.