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Special Issue "Emerging Robots and Sensing Technologies in Geosciences"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. João Valente
Website
Guest Editor
Information Technology Group, Wageningen University and Research, Wageningen, Netherlands
Interests: field robotics, UAV, remote sensing, multi-robot systems, machine vision and olfactation
Dr. Duarte Antunes
Website
Guest Editor
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, the Netherlands
Interests: optimal control; stochastic control; networked control systems; approximated dynamic programming; precision farming; systems biology
Special Issues and Collections in MDPI journals
Dr. Rolf Hut
Website
Guest Editor
Faculty of Civil Engineering and Geoscience, chair of Water Resources Engineering, Delft University of Technology, Delft, Netherlands
Interests: cross discipline designed sensor systems, maker movement, impact of the arduino on geoscience, geoscientific sensor systems
Dr. Beril Sirmacek
Website
Guest Editor
School of Engineering, Computer Science and Informatics, Jönköping University, 553 18 Jönköping, Sweden
Interests: computer vision; remote sensing; artificial intelligence; machine learning; pattern recognition
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We are proud to announce this Special Issue addressing the state-of-the-art of robotic systems and related sensors technologies applied to geosciences.

Sustainable agriculture and environment conservation are top priorities for all governments and nations worldwide. Our population is growing fast, and our resources are becoming ever scarcer. Sensor technology will play a key role in providing fast solutions and recovery plans for these challenges. However, data acquisition is still a challenge, mainly when dealing with complex and dynamic environments such as agricultural fields and natural ecosystems. Gathering reliable data in situ, in a fast, cost-effective, and long-lasting manner is often not possible in many contexts without using robotic platforms endowed with different sensors.

This Special Isue will consider for publication all new advances in autonomous remote sensing systems that are developed to sense closely the earth. We want to give room to ideas that are beyond your imagination, that are unconventional, but at the same time bring scientific soundness.

Topics within the domain of this Special Issue include but are not limited to the following:

  • Air, ground, and maritime robotic systems;
  • Novel perception systems, event-triggered sensing;
  • Sensors (of all kinds) and (data-driven) sensor fusion;
  • Open-source software/hardware and mechatronics;
  • Informative path planning and adaptive sampling;
  • Internet of Things;
  • Augmented reality/virtual reality applications;
  • Big data and machine learning;
  • Open-issues and challenges;
  • Precision agriculture, nature conservation, and land degradation assessment;
  • Applications and case studies are very welcome!

Note: priority will be given to open-source tools and methods (e.g., software, datasets, and ros{bags, nodes}[1]) that are released upon the acceptance of the publication.

[1]  http://www.ros.org

Dr. João Valente
Dr. Duarte Antunes
Dr. Rolf Hut
Dr. Beril Sirmaçek
Guest Editors

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 papers will be 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 2200 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.

Published Papers (2 papers)

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Research

Open AccessArticle
Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles
Sensors 2020, 20(24), 7332; https://doi.org/10.3390/s20247332 - 20 Dec 2020
Abstract
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO2. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the [...] Read more.
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO2. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO2 measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO2 measurements in future outdoor experiments that include electrochemical sensor integration with UAV’s. Full article
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
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Open AccessArticle
Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
Sensors 2020, 20(2), 439; https://doi.org/10.3390/s20020439 - 13 Jan 2020
Cited by 5
Abstract
Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially [...] Read more.
Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities. Full article
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
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