Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

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

Using a mobile robot for hazardous substances detection in a factory environment

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndustries that work with toxic materials need extensive security protocols to avoid accidents. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning through a defined path is cheaper, configurable and adaptable. This work describes a mobile robot, equipped with several gas sensors and LIDAR, that follows a trajectory based on waypoints, simulating a working Autonomous Guided Vehicle (AGV). At the same time, the robot keeps measuring for toxic gases. In other words, the robot follows the trajectory while the gas concentration is under a defined value. Otherwise, it starts the autonomous leakage search based on a search algorithm that allows to find the leakage position avoiding obstacles in real time. The proposed methodology is verified in simulation based on a model of the real robot. Therefore, three path plannings were developed and their performance compared. A Light Detection And Ranging (LIDAR) device was integrated with the path planning to propose an obstacle avoidance system with a dilation technique to enlarge the obstacles, thus, considering the robot’s dimensions. Moreover, if needed, the robot can be remotely operated with visual feedback. In addition, a controller was made for the robot. Gas sensors were embedded in the robot with Finite Impulse Response (FIR) filter to process the data. A low cost AGV was developed to compete in Festival Nacional de Robótica (Portuguese Robotics Open) 2019 - Gondomar, describing the robot’s control and software solution to the competition.As indústrias que trabalham com materiais tóxicos necessitam de extensos protocolos de segurança para evitar acidentes. Ao invés de ter sensores estáticos, o conceito de instalar sensores em um robô móvel que inspeciona através de um caminho definido é mais barato, configurável e adaptável. O presente trabalho descreve um robô móvel, equipado com vários sensores de gás e LIDAR, que percorre uma trajetória baseada em pontos de controle, simulando um AGV em trabalho. Em simultâneo são efetuadas medidas de gases tóxicos. Em outras palavras, o robô segue uma trajetória enquanto a concentração de gás está abaixo de um valor definido. Caso contrário, inicia uma busca autônoma de vazamento de gás com um algoritmo de busca que permite achar a posição do gás evitando os obstáculos em tempo real. A metodologia proposta é verificada em simulação. Três algoritmos de planejamento de caminho foram desenvolvidos e suas performances comparadas. Um LIDAR foi integrado com o planejamento de caminho para propôr um sistema de evitar obstáculos. Além disso, o robô pode ser operado remotamente com auxílio visual. Foi feito um controlador para o robô. Sensores de gás foram embarcados no robô com um filtro de resposta ao impulso finita para processar as informações. Um veículo guiado automático de baixo custo foi desenvolvido para competir no Festival Nacional de Robótica 2019 - Gondomar. O controle do veículo foi descrito com o programa de solução para a competição

Using wireless sensors and networks program for chemical particle propagation mapping and chemical source localization

Chemical source localization is a challenge for most of researchers. It has extensive applications, such as anti-terrorist military, Gas and oil industry, and environment engineering. This dissertation used wireless sensor and sensor networks to get chemical particle propagation mapping and chemical source localization. First, the chemical particle propagation mapping is built using interpolation and extrapolation methods. The interpolation method get the chemical particle path through the sensors, and the extrapolation method get the chemical particle beyond the sensors. Both of them compose of the mapping in the whole considered area. Second, the algorithm of sensor fusion is proposed. It smooths the chemical particle paths through integration of more sensors\u27 value and updating the parameters. The updated parameters are associated with including sensor fusion among chemical sensors and wind sensors at same positions and sensor fusion among sensors at different positions. This algorithm improves the accuracy and efficiency of chemical particle mapping. Last, the reasoning system is implemented aiming to detect the chemical source in the considered region where the chemical particle propagation mapping has been finished. This control scheme dynamically analyzes the data from the sensors and guide us to find the goal. In this dissertation, the novel algorithm of modelling chemical propagation is programmed using Matlab. Comparing the results from computational fluid dynamics (CFD) software COMSOL, this algorithm have the same level of accuracy. However, it saves more computational times and memories. The simulation and experiment of detecting chemical source in an indoor environment and outdoor environment are finished in this dissertation --Abstract, page iii

Design and Implementation of a Mobile Robot for Carbon Monoxide Monitoring

The gas detection problem is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. The mobile robot used for gas detection has several advantages and can reduce danger for humans. In this study, we proposed an integration system for a mobile robot that can be used for carbon monoxide (CO) monitoring with different operating temperatures. The design and implementation of a mobile robot system that proposed consists of the onboard and ground stations. The proposed system can read CO gas concentration and temperature then send it wirelessly using an XBee module to the ground station. This system was also able to receive the command from the ground station to move the robot. The system provided real-time acquisition data that believed can be a useful tool for monitoring and can be applied for various purposes. The experimental results show that a combination of a mobile robot and environmental sensors can be used for environmental monitoring

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

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