Vision-Driven Robot for Worker Monitoring in Industrial Environments

Abstract

Industrial workplaces are dynamic and pose significant safety risks due to heavy machinery, complex workflows, and human errors. This work presents a vision-driven robotic system for real-time monitoring of workers to enhance workplace safety and support proactive risk management. The system combines navigation, computer vision, and edge-based computing to detect unsafe behaviors, posture deviations, and PPE compliance violations. High-resolution cameras and depth sensors capture visual data, processed using deep learning algorithms for human detection, pose estimation, and activity recognition. By performing inference on edge devices, the system ensures low-latency alerting while reducing dependency on network connectivity. The robot navigates complex workspaces, monitors multiple workers, and provides supervisors with contextual alerts and analytics. This approach offers a scalable and efficient solution for industrial safety monitoring, reducing workplace accidents and improving operational efficiency. Experimental evaluations demonstrate robust detection accuracy, reliable performance under varying lighting and environmental conditions, and superior coverage compared to static monitoring systems. This integrated approach provides a scalable, adaptable, and efficient solution for industrial safety monitoring, contributing to reduced workplace accidents, improved operational efficiency, and data-driven decision-making.

Introduction

The text describes a vision-driven robotic system for worker monitoring in industrial environments. Its main goal is to automatically detect whether workers are active or idle, improving productivity tracking, safety, and reducing the need for manual supervision in places like factories, warehouses, and offices.

The system uses an ESP32 microcontroller combined with an ESP32-CAM module to capture real-time video and process worker activity using computer vision techniques. It also integrates sensors such as ultrasonic, IR, DHT11 (temperature and humidity), and smoke sensors to monitor environmental conditions and ensure workplace safety.

The architecture includes three main parts:

• Data acquisition layer: collects environmental and motion data using sensors
• Vision processing unit: analyzes video feed to detect worker presence and activity status
• Actuation system: controls robot movement using motors and displays status on an LCD screen

The working principle involves continuous video capture, real-time processing, and AI-based analysis to determine whether a worker is working or idle. If unsafe or abnormal behavior is detected, the system triggers alerts and moves the robot to monitor different areas.

The system uses low-cost, widely available hardware components and is designed for real-time monitoring with minimal manual intervention.

Its advantages include improved safety, reduced human effort, real-time surveillance, cost-effectiveness, and easy scalability with AI enhancements.

It can be applied in factories, warehouses, construction sites, hazardous zones, and industrial IoT systems for automated worker monitoring and safety management.

Conclusion

This work presents a vision-driven robotic system for real-time monitoring of workers in industrial environments, designed to enhance workplace safety, ensure compliance with safety protocols, and support proactive risk management. The system integrates autonomous navigation, computer vision based human detection, posture analysis, and PPE compliance monitoring with edge-based processing to provide immediate alerts. Experimental evaluation demonstrates that the proposed system achieves high detection accuracy, low latency, and reliable performance under diverse environmental conditions, outperforming conventional fixed-camera and wearable-based monitoring systems.

References

[1] DFRobot. (2025). Robot Chassis Design and Assembly Guide. DFRobot Documentation. This guide explains the structure and mechanical design of the robot chassis. [2] Espressif Systems. (2025). ESP32 Series Datasheet. Espressif Systems Documentation. Provides detailed specifications of the ESP32 microcontroller used for control and processing. [3] Arduino. (2025). Embedded System Development Using ESP32 and Sensors. Arduino Official Documentation. Explains integration of ESP32 with multiple sensors for real-time applications. [4] MaxBotix Inc. (2024). Ultrasonic Sensors for Distance Measurement Applications. Sensor Application Notes. Describes ultrasonic sensor usage for obstacle detection.

Copyright

Copyright © 2026 Mr. C. Jerlin Ajith Davidson, Anu Saranya A, Harini K, Lohita Sruti J C. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.