TY - JOUR A1 - Neumann, Patrick P. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms N2 - Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions. KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2013 U6 - https://doi.org/10.1080/01691864.2013.779052 SN - 0169-1864 SP - 725 EP - 738 PB - VNU Sciences Pr. CY - Utrecht AN - OPUS4-28010 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Neumann, Patrick P. A1 - Trincavelli, M. T1 - Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go? N2 - Roboticists often take inspiration from animals for designing sensors, actuators or algorithms that control the behaviour of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odour plume. In particular the task of tracking an odour plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behaviour of moths, dungbeetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is at the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behaviour of animals, can be usefully 'translated' into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically-inspired algorithms. KW - Mobile robotics KW - Mobile robot olfaction KW - Biologically inspired robots KW - Landfill surveillance PY - 2011 UR - http://journal.frontiersin.org/Journal/10.3389/fneng.2011.00020/full U6 - https://doi.org/10.3389/fneng.2011.00020 SN - 1662-6443 SP - 1 EP - 14 PB - Frontiers Research Foundation CY - Lausanne AN - OPUS4-25037 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Schnürmacher, M. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - A probabilistic gas patch path prediction approach for airborne gas ource localization in non-uniform wind fields N2 - In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach for GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms. T2 - ISOEN 2013 - 15th International symposium on olfaction and electronic nose CY - Deagu, South Korea DA - 02.07.2013 KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2013 IS - Symposia / Applications of remote and local gas sensing ... SP - 15 EP - 16 AN - OPUS4-28878 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Neumann, Patrick P. A1 - Schnürmacher, M. A1 - Bennetts, V.H. A1 - Lilienthal, A.J. A1 - Bartholmai, Matthias A1 - Schiller, J.H. T1 - A probabilistic gas patch path prediction approach for airborne gas source localization in non-uniform wind fields N2 - In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms. KW - Autonomous micro UAV KW - Chemical and wind sensing KW - Gas source localization KW - Particle filter PY - 2014 U6 - https://doi.org/10.1166/sl.2014.3168 SN - 1546-198X SN - 1546-1971 VL - 12 IS - 6/7 SP - 1113 EP - 1118 PB - American Scientific Publishers (ASP) CY - Stevenson Ranch, CA, USA AN - OPUS4-31525 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 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 toward 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 toward 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 - IAS13 - 13th International conference on intelligent autonomous systems CY - Padova, Italy DA - 2014-07-15 KW - Autonomous micro UAV KW - Mobile robot olfaction KW - Gas source localization KW - Reactive plume tracking KW - Biologically inspired robots PY - 2016 SN - 978-3-319-08338-4; 978-3-319-08337-7 U6 - https://doi.org/10.1007/978-3-319-08338-4_110 SN - 2194-5357 SP - 1533 EP - 1548 PB - Springer Verlag CY - Berlin, Germany AN - OPUS4-31526 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 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 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 U6 - 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 -