Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Cooperative strategies for the detection and localization of odorants with robots and artificial noses

En este trabajo de investigación se aborda el diseño de una plataforma robótica orientada a la implementación de estrategias de búsqueda cooperativa bioinspiradas. En particular, tanto el proceso de diseño de la parte electrónica como hardware se han enfocado hacia la validación en entornos reales de algoritmos capaces de afrontar problemas de búsqueda con incertidumbre, como lo es la búsqueda de fuentes de olor que presentan variación espacial y temporal. Este tipo de problemas pueden ser resueltos de forma más eficiente con el empleo de enjambres con una cantidad razonable de robots, y por tanto la plataforma ha sido desarrollada utilizando componentes de bajo coste. Esto ha sido posible por la combinación de elementos estandarizados -como la placa controladora Arduino y otros sensores integrados- con piezas que pueden ser fabricadas mediante una impresora 3D atendiendo a la filosofía del hardware libre (open-source). Entre los requisitos de diseño se encuentran además la eficiencia energética -para maximizar el tiempo de funcionamiento de los robots-, su capacidad de posicionamiento en el entorno de búsqueda, y la integración multisensorial -con la inclusión de una nariz electrónica, sensores de luminosidad, distancia, humedad y temperatura, así como una brújula digital-. También se aborda el uso de una estrategia de comunicación adecuada basada en ZigBee. El sistema desarrollado, denominado GNBot, se ha validado tanto en los aspectos de eficiencia energética como en sus capacidades combinadas de posicionamiento espacial y de detección de fuentes de olor basadas en disoluciones de etanol. La plataforma presentada -formada por el GNBot, su placa electrónica GNBoard y la capa de abstracción software realizada en Python- simplificará por tanto el proceso de implementación y evaluación de diversas estrategias de detección, búsqueda y monitorización de odorantes, con la estandarización de enjambres de robots provistos de narices artificiales y otros sensores multimodales.This research work addresses the design of a robotic platform oriented towards the implementation of bio-inspired cooperative search strategies. In particular, the design processes of both the electronics and hardware have been focused towards the real-world validation of algorithms that are capable of tackling search problems that have uncertainty, such as the search of odor sources that have spatio-temporal variability. These kind of problems can be solved more efficiently with the use of swarms formed by a considerable amount of robots, and thus the proposed platform makes use of low cost components. This has been possible with the combination of standardized elements -as the Arduino controller board and other integrated sensors- with custom parts that can be manufactured with a 3D printer attending to the open-source hardware philosophy. Among the design requirements is the energy efficiency -in order to maximize the working range of the robots-, their positioning capability within the search environment, and multiple sensor integration -with the incorporation of an artificial nose, luminosity, distance, humidity and temperature sensors, as well as an electronic compass-. Another subject that is tackled is the use of an efficient wireless communication strategy based on ZigBee. The developed system, named GNBot, has also been validated in the aspects of energy efficiency and for its combined capabilities for autonomous spatial positioning and detection of ethanol-based odor sources. The presented platform -formed by the GNBot, the GNBoard electronics and the abstraction layer built in Python- will thus simplify the processes of implementation and evaluation of various strategies for the detection, search and monitoring of odorants with conveniently standardized robot swarms provided with artificial noses and other multimodal sensors

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

Environmental Monitoring using Autonomous Vehicles: A Survey of Recent Searching Techniques

Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles

Bio-inspired cooperative exploration of noisy scalar fields

A fundamental problem in mobile robotics is the exploration of unknown fields that might be inaccessible or hostile to humans. Exploration missions of great importance include geological survey, disaster prediction and recovery, and search and rescue. For missions in relatively large regions, mobile sensor networks (MSN) are ideal candidates. The basic idea of MSN is that mobile robots form a sensor network that collects information, meanwhile, the behaviors of the mobile robots adapt to changes in the environment. To design feasible motion patterns and control of MSN, we draw inspiration from biology, where animal groups demonstrate amazingly complex but adaptive collective behaviors to changing environments. The main contributions of this thesis include platform independent mathematical models for the coupled motion-sensing dynamics of MSN and biologically-inspired provably convergent cooperative control and filtering algorithms for MSN exploring unknown scalar fields in both 2D and 3D spaces. We introduce a novel model of behaviors of mobile agents that leads to fundamental theoretical results for evaluating the feasibility and difficulty of exploring a field using MSN. Under this framework, we propose and implement source seeking algorithms using MSN inspired by behaviors of fish schools. To balance the cost and performance in exploration tasks, a switching strategy, which allows the mobile sensing agents to switch between individual and cooperative exploration, is developed. Compared to fixed strategies, the switching strategy brings in more flexibility in engineering design. To reveal the geometry of 3D spaces, we propose a control and sensing co-design for MSN to detect and track a line of curvature on a desired level surface.Ph.D

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

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

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the 'bout' detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments