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In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode’s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. 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).
In this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.
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.
This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.
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.
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.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms
(2013)
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.
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.
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.