Environmental field estimation with hybrid-mobility sensor networks

Evans, William C.; Dias, Deodália; Roelofsen, Steven; Martinoli, Alcherio

doi:10.1109/icra.2016.7487741

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…In IPP algorithms, informative quantities, such as entropy or mutual information, are used to steer the movement of the robots towards areas rich in information content. There are several examples of such techniques being employed in environmental monitoring missions [11], such as temperature field estimation [12] or weed monitoring [1]. In the context of gas mapping missions, characterized by a stochastic and highly dynamic underlying phenomenon, employing IPP strategies for navigation is quite challenging.…”

Section: Gas Distribution Mapping and Source Localizationmentioning

confidence: 99%

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

Ercolani,

Jin,

Martinoli

2023

Sensors

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Within the scope of the ongoing efforts to fight climate change, the application of multi-robot systems to environmental mapping and monitoring missions is a prominent approach aimed at increasing exploration efficiency. However, the application of such systems to gas sensing missions has yet to be extensively explored and presents some unique challenges, mainly due to the hard-to-sense and expensive-to-model nature of gas dispersion. For this paper, we explored the application of a multi-robot system composed of rotary-winged nano aerial vehicles to a gas sensing mission. We qualitatively and quantitatively analyzed the interference between different robots and the effect on their sensing performance. We then assessed this effect, by deploying several algorithms for 3D gas sensing with increasing levels of coordination in a state-of-the-art wind tunnel facility. The results show that multi-robot gas sensing missions can be robust against documented interference and degradation in their sensing performance. We additionally highlight the competitiveness of multi-robot strategies in gas source location performance with tight mission time constraints.

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Section: Gas Distribution Mapping and Source Localizationmentioning

confidence: 99%

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

Ercolani,

Jin,

Martinoli

2023

Sensors

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“…With a similar research motivation, nonmyopic informationbased planning has been investigated to optimize a sampling frame by integrating the global information over the study area [21]. In a recent work, Evans et al [22] planned informative paths by leveraging a priori knowledge about the GP model of the spatial field. Ma et al [23] proposed an information-based planner for a single robot, which generated the waypoints that were maximally informative based on the MI criterion.…”

Section: Related Workmentioning

confidence: 99%

“…Afterward, the next sampling site is generated by finding the sampling location that is the most informative locally. This strategy has been studied in past works (e.g., [15], [22], [16]) to achieve online planning for adaptive sampling and field mapping. Specifically, a circular arc (with the current location as the circle center) is discretized to multiple points, among which, the most informative site is selected as the next sampling location.…”

Section: A Synthetic Datasetmentioning

confidence: 99%

Information-Based Hierarchical Planning for a Mobile Sensing Network in Environmental Mapping

Tong

Xia

et al. 2020

IEEE Systems Journal

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This article investigates the problem of informationbased sampling design and path planning for a mobile sensing network to predict scalar fields of monitored environments. A hierarchical framework with a built-in Gaussian Markov random field model is proposed to provide adaptive sampling for efficient field reconstruction. In the proposed framework, a nonmyopic planner is operated at a sink to navigate the mobile sensing agents in the field to the sites that are most informative. Meanwhile, a myopic planner is carried out on board each agent. A tradeoff between computationally intensive global optimization and efficient local greedy search is incorporated into the system. The mobile sensing agents can be scheduled online through an anytime algorithm to visit and observe the high-information sites. Experiments on both synthetic and real-world datasets are used to demonstrate the feasibility and efficiency of the proposed planner in model exploitation and adaptive sampling for environmental field mapping. Index Terms-Adaptive sampling, environmental field mapping, Gaussian Markov random fields (GMRFs), information-driven planning, mobile sensing networks (MSNs). I. INTRODUCTIONM OBILE sensing networks (MSNs) can provide unprecedented flexibility, efficiency, and effectiveness in information gathering. They can improve the performance of an environmental monitoring process. With the support of mobile sensing, measurements of the monitored variables over both spatial and temporal scales can be collected to estimate, interpret, and reconstruct the environmental field of interest. Due to these advantages, MSNs have been developed and deployed to provide in situ measurements and field maps in many environmental monitoring programs [1]-[3].When implementing an MSN, owing to the limited number and mobility of the sensing agents (e.g., mobile sensors or T. Li and C. W. de Silva are with the

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“…In many circumstances, the assumptions may not be practically feasible. For instance, the assumption of a known and constant mean of the environmental model (e.g., [8]) can cause inferior estimation performance in a practical situation where unknown spatial trends exist.…”

Section: Related Workmentioning

confidence: 99%

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

Teng

Wang

Q.-H.

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Unmanned Aerial Vehicles (UAVs) have been implemented for environmental monitoring by using their capabilities of mobile sensing, autonomous navigation, and remote operation. However, in real-world applications, the limitations of on-board resources (e.g., power supply) of UAVs will constrain the coverage of the monitored area and the number of the acquired samples, which will hinder the performance of field estimation and mapping. Therefore, the issue of constrained resources calls for an efficient sampling planner to schedule UAV-based sensing tasks in environmental monitoring. This paper presents a mission planner of coverage sampling and path planning for a UAV-enabled mobile sensor to effectively explore and map an unknown environment that is modeled as a random field. The proposed planner can generate a coverage path with an optimal coverage density for exploratory sampling, and the associated energy cost is subjected to a power supply constraint. The performance of the developed framework is evaluated and compared with the existing state-of-the-art algorithms, using a real-world dataset that is collected from an environmental monitoring program as well as physical field experiments. The experimental results illustrate the reliability and accuracy of the presented coverage sampling planner in a prior survey for environmental exploration and field mapping.

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Environmental field estimation with hybrid-mobility sensor networks

Cited by 9 publications

References 9 publications

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation

Information-Based Hierarchical Planning for a Mobile Sensing Network in Environmental Mapping

Coverage Sampling Planner for UAV-enabled Environmental Exploration and Field Mapping

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