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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.
In recent decades, robotics, particularly in environmental monitoring, has made significant advances. Robots of various forms and sizes have become essential tools for data collection in environmental research. Mobile Robot Olfaction (MRO) involves mobile robots equipped with gas sensors and requires the integration of multiple disciplines, including signal processing, machine perception, autonomous navigation, and pattern recognition. Common applications of MRO include mapping gas distributions, locating and detecting gas sources, and tracking gas plumes. Aerial Robot Olfaction (ARO) is a specialized branch of MRO that adapts these concepts to aerial robots, addressing the challenges of airborne gas sensing.
This presentation highlights recent developments and results from ongoing research projects in MRO and ARO, with a focus on real-world deployment scenarios and the challenges encountered in practice.
Rapid detection and localization of liquid fuel spills is critical for first responders assessing fire and health hazards, yet current methods require ground-based sampling or specialized instrumentation, limiting their practicality for wide-area emergency response. We present a drone-based passive colorimetric sensor system using test strips impregnated with Nile red, similar to colored confetti. Nile red is a solvatochromic dye that undergoes distinct visible color transitions upon exposure to different liquids. The dye is embedded within a polymer matrix that minimizes leaching while providing high optical contrast between dry, water-exposed, and fuel-exposed states. The sensor strips exhibit solvent-specific colorimetric responses within one minute of exposure, readily detectable by standard RGB cameras mounted on unmanned aerial vehicles (UAV) at altitudes up to 50 m. Automated classification was validated at 20 m altitude, enabling remote surveillance of contaminated surfaces without specialized equipment. Color-corrected image analysis using Calibrite ColorChecker calibration ensures reliable interpretation under variable field illumination (625–77,000 lux). Systematic laboratory evaluation of twelve fossil and bio-derived fuels revealed characteristic hue shifts that clearly discriminate ethanol-containing gasoline blends from diesel-range fuels. Field validation confirmed localization and classification of fuel-exposed sensors, achieving F1 scores of 0.94 for gasoline and 0.98 for diesel detection with no false positives in the tested scenarios. This cost-effective and scalable approach provides actionable information on both contamination location and fuel type, crucial for rapid hazard assessment in emergency response scenarios.
Durch einen bestehenden hohen Bedarf an der Fernerkundung gefährlicher Substanzen bieten kostengünstige, biokompatible Cellulose-Papierstücke eine effektive Lösung. Sie dienen als passive optische Chemosensoren und können per Kamerasystem ausgelesen werden. Die Modifikation mit chemisch sensiblen Farbstoffen ermöglicht optisch detektierbare Reaktionen auf Zielsubstanzen. Autonomes Abfliegen großer Gebiete und KI-gestützte Auswertungen mit anpassbaren Gefahrenklassen eröffnen ein breites Einsatzspektrum. Bei Unfällen mit gefährlichen Stoffen kann so eine automatisierte Gefahrenerkennung inklusive Kartierung und exakter Positionsanzeige des kontaminierten Bereichs erfolgen, ohne dass sich Einsatzkräfte riskant nähern müssen.
The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as smart dust. Several efforts focused primarily on computing and networking capabilities, but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data were processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.
The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as Smart Dust. Several efforts focused primarily on computing and networking capabilities but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data was processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.
Remote sensing of hazardous substances is a key task that can be achieved with the help of remotely operated platforms equipped with specific sensors. A huge variety of methods and used vehicles have been developed for different purposes in recent years. The term smart dust refers to a science fiction novel and develop shortly after into a research proposal at UC Berkley funded by DARPA. Subsequently, the topic gained attraction but was overall considered as to complex for the technologies available at that time. In the launched passive smart dust project, we shift to a simple “chemical intelligent” passive sensor particle on the ground combined a read-out active sensor attached to an Unmanned Aerial Vehicle (UAV). The reactive particle surface can be preadjusted in the lab for exact desired properties regarding certain reactions to hazardous substances.
Moreover, the aimed interaction with the active sensor can be modified. Planed applications allow for different materials e.g., for short time measurement, being ecologically degradable, or weather stable for long time monitoring.
High demand for remote sensing of hazardous substances. Possible solution: Use of distributed, low cost, and environmentally safe particles as passive sensors that can be read out remotely
Chemical intelligence on the particle surface can be easily modified Particles enable optically quantifiable response and inference of target substances (also no maintenance or power supply required
We introduce a passive smart dust concept as a novel solution for environmental monitoring. Utilizing chemical reagents like colorimetric indicators and other chemosensors, these particles detect varying environmental conditions. We developed paper-based sensors that are both cost-effective and eco-friendly. In practical tests, these sensors, dispersed over a designated area, successfully identified hazardous substances by changing their color when exposed to acids or bases. This color change was remotely detectable using a drone-mounted color camera. The data thus obtained was processed through specialized software, accurately pinpointing areas of contamination. This method proves the efficacy and scalability of passive smart dust technology for real-time, environmentally sustainable remote sensing of hazardous materials
