Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

The advancements in the field of robotics, specifically in the aerial robotics, combined with technological improvements of the capability of drones, have increased dramatically the use of these devices as a valuable tool in a wide range of applications. From civil to commercial and military area, the requirements in the emerging application for monitoring complex scenarios that are potentially dangerous for operators give rise to the need of a more powerful and sophisticated approach. This work aims at proposing the use of swarm drones to increase plume detection, tracking and source declaration for chemical releases. The several advantages which this technology may lead to this research and application fields are investigated, as well as the research and technological activities to be performed to make swarm drones efficient, reliable, and accurate

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein

Enhancement of the Sensory Capabilities of Mobile Robots through Artificial Olfaction

La presente tesis abarca varios aspectos del olfato artificial u olfato robótico, la capacidad de percibir información sobre la composición del aire que rodea a un sistema automático. En primer lugar, se desarrolla una nariz electrónica, un instrumento que combina sensores de gas de bajas prestaciones con un algoritmo de clasificación para medir e identificar gases. Aunque esta tecnología ya existía previamente, se aplica un nuevo enfoque que busca reducir las dimensiones y consumo para poder instalarlas en robots móviles, a la vez que se aumenta el número de gases detectables mediante un diseño modular. Posteriormente, se estudia la estrategia óptima para encontrar fugas de gas con un robot equipado con este tipo de narices electrónicas. Para ello se llevan a cabos varios experimentos basados en teleoperación para entender como afectan los sensores del robot al éxito de la tarea, de lo cual se deriva finalmente un algoritmo para generar con robots autónomos mapas de gas de un entorno dado, el cual se inspira en el comportamiento humano, a saber, maximizar la información conocida sobre el entorno. La principal virtud de este método, además de realizar una exploración óptima del entorno, es su capacidad para funcionar en entornos muy complejos y sujetos a corrientes de vientos mediante un nuevo método que también se presenta en esta tesis. Finalmente, se presentan dos casos de aplicación en los que se identifica de forma automática con una nariz electrónica la calidad subjetiva del aire en entornos urbanos

The Multi-Chamber Electronic Nose—An Improved Olfaction Sensor for Mobile Robotics

One of the major disadvantages of the use of Metal Oxide Semiconductor (MOS) technology as a transducer for electronic gas sensing devices (e-noses) is the long recovery period needed after each gas exposure. This severely restricts its usage in applications where the gas concentrations may change rapidly, as in mobile robotic olfaction, where allowing for sensor recovery forces the robot to move at a very low speed, almost incompatible with any practical robot operation. This paper describes the design of a new e-nose which overcomes, to a great extent, such a limitation. The proposed e-nose, called Multi-Chamber Electronic Nose (MCE-nose), comprises several identical sets of MOS sensors accommodated in separate chambers (four in our current prototype), which alternate between sensing and recovery states, providing, as a whole, a device capable of sensing changes in chemical concentrations faster. The utility and performance of the MCE-nose in mobile robotic olfaction is shown through several experiments involving rapid sensing of gas concentration and mobile robot gas mapping

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Gaining information about an unknown gas source is a task of great importance with applications in several areas including: responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three dimensional location of the release, its emission rate, and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on-board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters in order to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed and areas for future research are identified

Design and development of wall climbing robot

This research work presents the design of a robot capable of climbing vertical and rough planes, such as stucco walls. Such a capacity offers imperative non military person and military preferences, for example, observation, perception, look and recover and actually for diversion and amusements. The robot's locomotion is performed using rack and pinion mechanism and adhesion to wall is performed by sticking using suction cups. The detailed design is modelled and fabrication is performed. It utilizes two legs, each with two degrees of freedom. And a central box containing the required mechanisms to perform the locomotion and adhesion is designed to carry any device to perform works on wall. A model of the robot is fabricated in a workshop using general tools. This model show how the mechanisms in the robot will work and how they are assembled together

Mobile robot operator for downstream Oil & Gas industrial facilities

This thesis considers the application of robots in downstream Oil & Gas facilities. Automation is important in industrial facilities where improvements in safety, efficiency, and environmental performance are becoming increasingly important. Here we present a mobile robotic platform for the performance of inspection and survey tasks in downstream facilities. We demonstrate a hybrid localization strategy using a novel metric which allows for long term autonomy in large distributed industrial environments. An environmental sensing package and gas source localization algorithm are deployed on the platform to identify and localize fugitive emission sources. Our mobile robotic platform, hybrid localization strategy, and gas source localization algorithm form an autonomous remote operator capable of meaningfully contributing to the advancement of automation in industrial facilities.Mechanical Engineerin

Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks

Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217 , MRC award no. MR/T046759/1 ), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539 ). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R , Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigación Carlos III, Share4Rare Project (Grant agreement 780262 ), and ACCIÓ (Innotec A CE014/20/000018 ). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra Húnter Program . B2SLab is certified as 2017 SGR 952. Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217, MRC award no. MR/T046759/1), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigaci?n Carlos III, Share4Rare Project (Grant agreement 780262), and ACCI? (Innotec ACE014/20/000018). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra H?nter Program. B2SLab is certified as 2017 SGR 952. Publisher Copyright: © 2022Metal oxide (MOx) gas sensors are a popular choice for many applications, due to their tunable sensitivity, space efficiency and low cost. Publicly available sensor datasets are particularly valuable for the research community as they accelerate the development and evaluation of novel algorithms for gas sensor data analysis. A dataset published in 2013 by Vergara and colleagues contains recordings from MOx gas sensor arrays in a wind tunnel. It has since become a standard benchmark in the field. Here we report a latent property of this dataset that limits its suitability for gas classification studies. Measurement timestamps show that gases were recorded in separate, temporally clustered batches. Sensor baseline response before gas exposure were strongly correlated with the recording batch, to the extent that baseline response was largely sufficient to infer the gas used in a given trial. Zero-offset baseline compensation did not resolve the issue, since residual short-term drift still contained enough information for gas/trial identification using a machine learning classifier. A subset of the data recorded within a short period of time was minimally affected by drift and suitable for gas classification benchmarking after offset-compensation, but with much reduced classification performance compared to the full dataset. We found 18 publications where this dataset was used without precautions against the circumstances we describe, thus potentially overestimating the accuracy of gas classification algorithms. These observations highlight potential pitfalls in using previously recorded gas sensor data, which may have distorted widely reported results.Peer reviewe