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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using

Kahiomba Sonia Kiangala¹, Zenghui Wang²

¹College of Science, Engineering and Technology (CSET), University of South Africa, Johannesburg 1710, South Africa.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

This summary is machine-generated.

This study introduces a safety system for autonomous moving robots (AMR) in manufacturing. Using reinforcement learning and speech recognition, robots learn emergency exit paths, enhancing safety in Industry 4.0 environments.

Keywords:

Q-learning algorithm autonomous moving robot obstacle-free path planning reinforcement learning (RL)safety response smart manufacturing speech recognition

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Area of Science:

Robotics and Automation
Artificial Intelligence
Industrial Safety

Background:

Industry 4.0 (I40) integrates autonomous moving robots (AMR) as active workforces alongside humans.
Ensuring safety for both humans and robots during plant evacuations is critical.
Current safety protocols primarily focus on human operators, neglecting robot safety induction.

Purpose of the Study:

To develop an experimental safety response mechanism for autonomous robots in manufacturing.
To enable robots to learn obstacle-free emergency exit trajectories.
To integrate human voice commands for emergency plant shutdown.

Main Methods:

Implemented a Q-learning reinforcement learning algorithm for robot path learning.
Developed a rule-based system for safety response based on learned paths.
Integrated a speech recognition system for operator voice commands to a Siemens PLC.

Main Results:

The safety response mechanism successfully generated obstacle-free paths to safety exits for robots.
The system demonstrated effective emergency signal transmission via voice command or emergency stop button.
Functionality was validated on real hardware (Siemens S7-1200 PLC) in a simulated environment.

Conclusions:

The developed mechanism enhances safety for AMRs in manufacturing SMEs.
It provides a strategy for integrating modern technologies like speech recognition with legacy PLCs.
Empowers small and medium-sized enterprises (SMEs) to adopt advanced robotics and safety concepts.