TY - CONF A1 - Neumann, Patrick P. A1 - Asadi, S. A1 - Schiller, J.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - An artificial potential field based sampling strategy for a gas-sensitive micro-drone T2 - IEEE/RSJ International conference on intelligent robots and systems (IROS '11) / Workshop on robotics for environmental monitoring (WREM2011) N2 - This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments with a gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization. T2 - IEEE/RSJ International conference on intelligent robots and systems (IROS '11) / Workshop on robotics for environmental monitoring (WREM2011) CY - San Francisco, CA, USA DA - 25.09.2011 KW - Autonomous UAV KW - Chemical sensing KW - Gas distribution modelling KW - Gas source localization KW - Gas sensors KW - Mobile sensing system KW - Quadrocopter KW - Sensor planning KW - Artificial potential field PY - 2011 SP - 34 EP - 38 AN - OPUS4-24537 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Bennetts, V. H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias T1 - From Insects to Micro Air Vehicles - A Comparison of Reactive Plume Tracking Strategies T2 - 13th International Conference on Intelligent Autonomous Systems (IAS) N2 - Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state of the art robotics systems. As a step towards practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail towards the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real world environments. T2 - 13th International Conference on Intelligent Autonomous Systems (IAS) CY - Padova, Italy DA - 15.07.2014 KW - Autonomous micro UAV KW - Mobile robot olfaction KW - Gas source localization KW - Reactive plume tracking KW - Biologically inspired robots PY - 2014 SP - 1 EP - 12 AN - OPUS4-43920 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 - 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 -