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Paper des Monats
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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.
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.
One of the main unsolved issues of under-ground storages for, e.g., CO2, H2, and natural gas is the comprehensive surveillance of these areas with reasonable effort and costs. Conventional sensors (e.g., soil air probes or borehole probes), however, can only be used for punctual or locally limited measurements; further their application can cause structural influences (invasive application).
In this paper, we describe in detail the setup of a CO2 injection soil test field. This test field will be used to enhance and validate an innovative ap-proach for distributed subsurface monitoring of gas storage areas. To the author’s knowledge, this is the first time that, for this purpose, a test field is built in an application relevant scale.
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.
A 400 m² soil test field with gas injection system was built up for the purpose of large-scale validation, optimization, and characterization of a novel comprehensive monitoring method for underground gas storage areas. The method combines gas sensing technology with linear form factor for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct time-series of gas distribution maps based on samples of orthogonally-aligned linear gas sensors. Several injection experiments with carbon dioxide (CO2) at different days with varying boundary conditions indicates the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.
A permanent-magnet synchronous motor (PMSM) servo drive for lightweight robotic platforms that have a high torque demand at low rotational speeds has been developed. First, a current-independent torque controller is derived and cascaded with a speed and position controller, while merely an encoder is used as a sensor device in combination with a speed estimator. Finally, the speed estimator output is compared to gyroscope measurements and the overall functioning is verified on a real system.
A current-independent torque equation for the permanent-magnet synchronous motor (PMSM) aiming at direct-drive servo applications is derived from a first principles model. Instead of measuring currents, all required control parameters are derived from optical incremental encoder measurements. The results are verified on a real system in test series showing the effect of static friction and proving the obtained torque model.
A 400 m2 soil test field with gas injection system was built up, which enables an experimental validation of linear membrane-based gas sensors – a sensor technology which was developed based on the selective permeation of gases through membranes. Several soil watering and injection experiments with carbon dioxide at different days with varying boundary conditions were performed showing that the sensor behaves mostly insensitive to the environmental conditions investigated, i.e., barometric pressure fluctuations, soil temperature, air temperature and humidity, sun duration, and wind speed. Furthermore, depending on water infiltration, a gas phase displacement could be observed in-situ based on the changed measurement signal. The results of the validation experiments highlight the potential of the method for rapid leak detection and localization qualifying the sensor particularly for safety applications, e.g., in underground gas storage areas.
One of the main unsolved issues of CCS is the comprehensive surveillance of CO2 storage areas with reasonable effort and costs. This study presents an approach for distributed subsurface monitoring of gas storage areas. The concept combines different measurement technologies to one multifunctional sensor: membrane based measurement technology for in situ monitoring of gases in soil and fibre optical sensing of temperature and strain (as a measure for structural change). A test field of application-relevant dimensions is built up to validate and optimize the technology.
Carbon capture & storage (CCS) is one of the most promising technologies for greenhouse gas (GHG) management. However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leak detection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and to increase the granularity of gas concentration measurements, a quickly deployable, mobile measurement device is needed. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHG emissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, the use of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone. Alternatively, we present an adaptive strategy, which suggests sampling points based on an artificial potential field (APF). The results of real-world experiments demonstrate the feasibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.