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Robotics for Environment Sensing
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Robotics for Environment Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 3853

Special Issue Editor

Dr. Patrick P. Neumann

E-Mail Website
Guest Editor
Bundesanstalt für Materialforschung und prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany
Interests: chemical sensing; mobile robot olfaction; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,

In the last decades, robotics has evolved significantly, both in terms of technologies developed and applications, especially in the field of environmental monitoring. Robots have been developed in different shapes, sizes and with different capabilities becoming a fundamental data gathering tool for scientists studying – not only – our planet. Design and implementation of robotic systems for environmental research still present significant challenges to robotics researchers, especially in the field of Mobile Robot Olfaction (MRO) – the discipline that studies mobile robots with gas sensing capabilities. MRO requires the fusion of different disciplines, such as signal processing, machine perception, autonomous navigation, and pattern recognition, in order to address the challenges related to gas and environmental sensing in unstructured environments. Typical tasks addressed by MRO systems are trial guidance, gas distribution modeling/mapping, and gas source localization as well as gas detection/finding, odor discrimination and concentration estimation, gas plume tracking, and gas source declaration. To address these tasks, gas and other environmental sensors are deployed on a single robot, a robot swarm, or as a mobile (robotic) node in a much bigger heterogeneous sensor network. Aerial Robot Olfaction (ARO) is a subcategory of MRO that addresses MRO related tasks with aerial robots and deals with the challenges of aerial-based gas and environmental sensing. This special issue centers around contributions in the field of Mobile Robot Olfaction. Its focuses applications for the challenges of real-world gas and environmental sensing. Papers should address how robotic systems, perceptual algorithms, chemical and environmental sensors, approaches to sensor fusion, decision support, human-robot interaction, or adaptive sensor planning deal with real-world conditions. Research questions may address, the limited control of the environment, open sampling processes, rapidly fluctuating concentration levels, turbulent gas and dust dispersal, and related topics.

Dr. Patrick P. Neumann
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mobile Robot Olfaction
  • chemical and environmental sensors
  • in situ or remote gas sensing
  • electronic nose
  • open vs. closed sampling system
  • sensor networks
  • gas dispersal, detection or discrimination
  • gas distribution mapping
  • gas source localization
  • Artificial Intelligence
  • Machine Learning

Published Papers (3 papers)

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Research

29 pages, 1835 KiB  
Article
Exploration and Gas Source Localization in Advection–Diffusion Processes with Potential-Field-Controlled Robotic Swarms
by Patrick Hinsen, Thomas Wiedemann, Dmitriy Shutin and Achim J. Lilienthal
Sensors 2023, 23(22), 9232; https://doi.org/10.3390/s23229232 - 16 Nov 2023
Viewed by 681
Abstract
Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources [...] Read more.
Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources using concentration measurements, robots need to seek out informative sampling locations. For this, domain knowledge needs to be incorporated into their exploration strategy. We achieve this by means of partial differential equations incorporated into a probabilistic gas dispersion model that is used to generate a spatial uncertainty map of process parameters. Previously, we presented a potential-field-control approach for navigation based on this map. We build upon this work by considering a more realistic gas dispersion model, now taking into account the mechanism of advection, and dynamics of the gas concentration field. The proposed extension is evaluated through extensive simulations. We find that introducing fluctuations in the wind direction makes source localization a fundamentally harder problem to solve. Nevertheless, the proposed approach can recover the gas source distribution and compete with a systematic sampling strategy. The estimator we present in this work is able to robustly recover source candidates within only a few seconds. Larger swarms are able to reduce total uncertainty faster. Our findings emphasize the applicability and robustness of robotic swarm exploration in dynamic and challenging environments for tasks such as gas source localization. Full article
(This article belongs to the Special Issue Robotics for Environment Sensing)
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22 pages, 7980 KiB  
Article
3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation
by Chiara Ercolani, Wanting Jin and Alcherio Martinoli
Sensors 2023, 23(20), 8512; https://doi.org/10.3390/s23208512 - 17 Oct 2023
Viewed by 912
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Robotics for Environment Sensing)
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22 pages, 6079 KiB  
Article
Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots
by Andres Gongora, Javier Monroy, Faezeh Rahbar, Chiara Ercolani, Javier Gonzalez-Jimenez and Alcherio Martinoli
Sensors 2023, 23(12), 5387; https://doi.org/10.3390/s23125387 - 07 Jun 2023
Viewed by 1247
Abstract
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, [...] Read more.
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel. Full article
(This article belongs to the Special Issue Robotics for Environment Sensing)
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