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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.
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.