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Organisationseinheit der BAM
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
This seminar presents the key research activities of ARO Lab@BAM, focusing on five main areas:
• Learning-based Gas Distribution Mapping utilizes machine learning to accurately model and predict spatial gas concentrations, enhancing environmental monitoring and safety.
• Mimose-A develops autonomous systems using artificial intelligence to enable the early detection of leaks in industrial environments.
• AGATO (Gastomography) introduces a novel robotic system for high-resolution gas distribution mapping.
• Passive Smart Dust detects chemically hazardous substances using drones equipped to distribute and detect particles carrying selective dyes, enabling rapid and reliable monitoring without complex components.
• HyAirLogic Lab advances hydrogen (H₂) research by testing the entire value chain in various Berlin-Brandenburg quarters, addressing technological challenges, public acceptance, and sustainable energy solutions for H₂-cargo drones.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
Long-Term Study of Low-Cost Sensor Network in Heavy Industry Environment - Potentials and Pitfalls
(2026)
Occupational health in industrial environments requires continuous monitoring of airborne pollutants, such as dust and gases, to ensure worker safety. Traditional monitoring systems often encounter practical and economic limitations, resulting in sparse data. This paper introduces a cost-effective solution by deploying a low-cost sensor network in a hot rolling mill, enabling continuous 24/7 gas and particle distribution monitoring. Based on a 19-month measurement campaign, we evaluated the network’s long-term performance. The results reveal significant drift and challenges in field calibration, emphasizing the need for regular maintenance and recalibration to ensure data accuracy. Despite these challenges, the study demonstrates the potential of heterogeneous sensor networks for industrial air quality monitoring, offering valuable insights into the balance between cost, performance, and long-term reliability. These findings encourage further exploration of sensor technologies and calibration strategies to enhance future monitoring systems in dynamic industrial environments.
Gas distribution mapping (GDM) reconstructs continuous concentration fields from sparse sensor measurements, which is a critical task for industrial safety and environmental monitoring. Recent advances in AI, particularly diffusion models and neural operators, promise faster inference and higher accuracy.
But do these capabilities translate to practical value? We evaluate diffusion models and Fourier neural operators (FNO) against Kernel DM+V and DARES on synthetic and real-world data. Our findings reveal that FNO achieves 33% lower RMSE than classical methods at 2x faster inference, providing clear practical value. Diffusion models enable uncertainty quantification but at 49x computational cost compared to FNO. However, both AI approaches require ∼3.5 GPU-hours of training and fundamentally learn to replicate simulation artifacts rather than ground truth physics. We conclude that data availability, not algorithmic sophistication, remains the critical bottleneck for practical learning-based GDM deployment.
Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only.
The project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios” (RASEM) addresses this issue by bringing together the benefits of both – low- and high-cost – measuring technologies enabling costefficient long-term air quality monitoring in realtime: A stationary network of low-cost sensors that is augmented by mobile units carrying high-quality sensors. By mapping the distribution of gases and particles in industrial environments with the proposed RASEM system, measures can be identified to improve on-site working conditions much faster than using traditional methods.
In this paper, we detail the technical aspects of RASEM and introduce the mobile platforms used.
This seminar presents the key research activities of ARO Lab@BAM, focusing on three main areas:
• Mimose-A develops autonomous systems using artificial intelligence to enable the early detection of leaks in industrial environments.
• AGATO (Gastomography) introduces a novel robotic system for high-resolution gas distribution mapping.
• Passive Smart Dust detects chemically hazardous substances using drones equipped to distribute and detect particles carrying selective dyes, enabling rapid and reliable monitoring without complex components.
Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only.
Recent developments in sensor technology enable cost-efficient gas monitoring in real-time for long-term intervals. This knowledge of contaminant distribution inside the industrial environment would provide means for better and more economic control of air impurities, e.g., the possibility to regulate the workspace’s ventilation exhaust locations, can reduce the concentration of airborne contaminants by 50%.
This paper describes a concept proposed in the project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios“ (RASEM). RASEM aims to bring together the benefits of both – low- and high-cost – measuring technologies: A stationary network of low-cost sensors shall be augmented by mobile units carrying high-quality sensors. Additionally, RASEM will develop procedures and algorithms to map the distribution of gases and particles in industrial environments.
Long-Term Study of Low-Cost Sensor Network in Heavy Industry Environment - Potentials and Pitfalls
(2026)
Occupational health in industrial environments requires continuous monitoring of airborne pollutants, such as dust and gases, to ensure worker safety. Traditional monitoring systems often encounter practical and economic limitations, resulting in sparse data. This paper introduces a cost-effective solution by deploying a low-cost sensor network in a hot rolling mill, enabling continuous 24/7 gas and particle distribution monitoring. Based on a 19-month measurement campaign, we evaluated
the network’s long-term performance. The results reveal significant drift and challenges in field calibration, emphasizing the need for regular maintenance and recalibration to ensure data accuracy. Despite these challenges, the study demonstrates the potential of heterogeneous sensor networks for industrial air quality monitoring, offering valuable insights into the balance between cost, performance, and long-term reliability. These findings encourage further exploration of sensor technologies and calibration strategies to enhance future monitoring systems in dynamic industrial environments.
Monitoring airborne pollutants is critical for occupational health, particularly in industrial environments where workers are exposed to hazardous emissions. Traditional measurements are typically limited to single-day campaigns, resulting in extremely sparse temporal data. Low-cost sensor networks offer a way to increase spatial and temporal resolution but are limited by issues of accuracy and reliability. To address this, we present a wireless heterogeneous sensor network that integrates low-cost stationary nodes with high-quality sensors on mobile platforms, including ground and aerial robots. We deploy this system in a hot rolling mill facility and evaluate its performance under real-world conditions. Field experiments reveal dynamic pollutant patterns, such as altitude-dependent PM2.5 gradients and temperature fluctuations. By introducing synchronized “rendezvous” events between mobile and stationary nodes, we demonstrate correlation capabilities of sensors. Our spatiotemporal analysis shows that, despite limitations of mobile sensing, strategically combining heterogeneous data sources enables capturing pollutant dynamics in complex industrial settings.
