TY - CONF A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Hüllmann, Dino A1 - Lilienthal, A.J. A1 - Kluge, Martin T1 - Bringing Mobile Robot Olfaction to the Next Dimension – UAV-based Remote Sensing of Gas Clouds and Source Localization T2 - Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) N2 - This paper introduces a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we introduce and present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile hexacopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing the gas sensing and aiming capabilities under realistic conditions. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Tunable Diode Laser Absorption Spectroscopy (TDLAS) KW - 3-axis gimbal KW - Mobile Robot Olfaction KW - Tomographic reconstruction of gas plumes KW - Localization of gas sources KW - Aerial platform PY - 2017 SN - 978-1-5090-4632-4 SP - 3910 EP - 3916 PB - IEEE AN - OPUS4-40545 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bennetts, V.H. A1 - Kucner, T.P. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. A1 - Fan, H. A1 - Lilienthal, A.J. T1 - Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots T2 - Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) N2 - For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Mapping KW - Field Robots KW - Environment Monitoring and Management KW - Aerial Systems KW - Perception and Autonomy PY - 2017 SN - 978-1-5090-4632-4 SP - 1 EP - 7 PB - IEEE AN - OPUS4-40544 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bennetts, V.H. A1 - Kucner, T.P. A1 - Schaffernicht, E. A1 - Neumann, Patrick P. A1 - Fan, H. A1 - Lilienthal, A.J. T1 - Probabilistic air flow modelling using turbulent and laminar characteristics for ground and aerial robots JF - IEEE Robotics and Automation Letters N2 - For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts. T2 - IEEE International Conference on Robotics and Automation (ICRA) CY - Singapore DA - 29.05.2017 KW - Mapping KW - Field robots KW - Environment monitoring and management KW - Aerial systems KW - Perception and autonomy PY - 2017 DO - https://doi.org/10.1109/LRA.2017.2661803 SN - 2377-3766 VL - 2 IS - 2 SP - 1117 EP - 1123 PB - IEEE AN - OPUS4-39218 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -