Regulatory and Safety Standards for ML-Driven IoT Robots Operating in Hazardous Industrial Environments

IoT-Based Remote Monitoring and Control of Robots for Hazardous Environments Using Machine Learning

Robots that are used in dangerous places, including chemical plants, mines and disaster-stricken areas, must work reliably and keep the human operator away from the danger. This study proposes and tests a system based on Internet of Things (IoT) that allows remote monitoring and control of robots in hazardous environments, complemented by a machine learning (ML) algorithm to automatically classify the hazard. The proposed system combined the environmental sensors (battery level, motor current, and vibration) with the environmental sensors (temperature, humidity, gas concentration, and smoke) and an edge-computing/microcontroller unit with an IoT communication module and a remote monitoring-and-control interface installed on a mobile robotic platform. A supervised machine learning (ML) model was trained, to classify the sensed environment into three safety classes: Safe, Caution and Hazardous with 4 candidate algorithms (Random Forest, Support Vector Machine (SVM), Decision Tree, Artificial Neural Network (ANN)) evaluated on accuracy, precision, recall, F1 score and inference time with 3000 simulated sensor data. The SVM classifier had the highest overall accuracy (90.0%) and F1-score (.890) compared to the Random Forest (89.5% accuracy) and ANN (88.9% accuracy) classifiers, with the Decision Tree also providing the fastest inference time (0.09 ms) at a relatively small cost in accuracy (86.5%). Overall, experimental tests conducted at the system level revealed that the end-to-end communication latency was highly meaningful with hazard intensity (p < 0.001; F = 335.1), ranging from a mean of 119 ms for the lowest hazard intensity to 210 ms for the highest hazard intensity, and that command-execution success rate and real-time monitoring accuracy decreased moderately as hazard intensity increased (p < 0.001; F = 205.5 and 72.9, respectively), but remained above 83% and 92% for the highest hazard intensity, respectively. These results have shown that the combination of IoT communication, edge sensing, and the classification of hazards by machine learning can realize low-latency and reliable monitoring and control of robots in hazardous environments and also show that there is a measurable performance trade-off when increasing the intensity of the environment hazards.

Smart Robotic Systems for Hazardous Industrial Environments

Overall, intelligent robotics can significantly reduce human exposure, improve inspection quality, and enable early fault detection, while future research must focus on certifiable AI, resilient perception, and standardized benchmarking in hazardous environments.

An Integrated Safety Management Framework for Working at Height Using Smart PPE, Artificial Intelligence and Risk Assessment

The development of an AI and Internet of Things (IoT) enabled Smart Safety Harness System for enhancing working at height safety in industrial applications with significant potential for application in construction, manufacturing, petrochemical, power, mining, and infrastructure industries.

Development of an Embedded IoT Board for Real-Time Floor Estimation of Autonomous Robots

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward compatibility with older buildings, and increased points of failure in the system architecture. This thesis presents the design and evaluation of a non-invasive IoT board for floor-level estimation that requires no modification to existing elevator systems. Developed in collaboration with Rocky Mountain Robotech LLC, the device is intended to assist service robots by providing floor-awareness using barometric pressure sensing. The system operates in two primary modes: a training mode, where it identifies characteristic pressure changes between building floors, and a normal operation mode, where it references this data to estimate floor position in real-time. Testing was conducted in buildings between two and four stories tall in Dallas, Texas, and Denver, Colorado. During training mode, the device correctly queued incoming pressure data and applied both a moving average filter and the Ramer-Douglas-Peucker (RDP) algorithm to isolate plateaus corresponding to distinct floor levels. After training, the board reliably transitioned to normal operation mode, continuing to collect and compare pressure data to stored floor values. Bluetooth communication with a tablet on the robot enabled the transmission of commands to initiate training and other actions, while data stored in non-volatile memory was preserved across power cycles. These results confirm that the system can distinguish between floors and maintain robust communication without requiring elevator integration. It demonstrates a low-cost, modular approach to floor estimation that avoids common barriers to adoption, such as infrastructure modification or reliance on high-precision sensors. However, while initial testing validates core functionality, further testing is needed to assess long-term reliability, sensitivity to weather and environmental changes, and generalizability across a wider variety of building types and layouts. This work shows that thoughtfully designed, non-invasive IoT hardware can meet key needs in service robotics—enhancing autonomy and safety without compromising existing infrastructure.

DEVELOPMENT OF AN IOT-BASED GAS MONITORING SYSTEM USING ARTIFICIAL NEURAL NETWORKS ON ESP32 FOR INDUSTRIAL SAFETY APPLICATIONS

Utilization of AI for Predictive Maintenance in IoT-Enabled Industrial Systems

This research highlights the potential benefits of AI-based predictive maintenance, including proactive equipment failure detection, maintenance schedule optimization, and reduced downtime, and identifies emerging trends and future directions in AI-powered predictive maintenance.