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

Bio-Inspired, Odor-Based Navigation

The ability of moths to locate a member of the opposite sex, by tracking a wind-borne plume of odor molecules, is an amazing reality. Numerous scenarios exist where having this capability embedded into ground-based or aerial vehicles would be invaluable. The main crux of this thesis investigation is the development of a navigation algorithm which gives a UAV the ability to track a chemical plume to its source. Inspiration from the male moth\u27s, in particular Manduca sexta, ability to successfully track a female\u27s pheromone plume was used in the design of both 2-D and 3-D navigation algorithms. The algorithms were developed to guide autonomous vehicles to the source of a chemical plume. The algorithms were implemented using a variety of fuzzy controllers and ad hoc engineering approaches. The fuzzy controller was developed to estimate the location of a vehicle relative to the plume: coming into the plume, in the plume, exiting the plume, or out of the plume. The 2-D algorithm had a 60% to 90% success rate in reaching the source while certain versions of 3-D algorithm had success rates from 50% to 100%

Robotic Olfactory-Based Navigation with Mobile Robots

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods. A. Adaptive Bio-inspired Navigation via Fuzzy Inference Systems. In nature, animals use olfaction to perform many life-essential activities, such as homing, foraging, mate-seeking, and evading predators. Inspired by the mate-seeking behaviors of male moths, this method presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source. Unlike traditional bio-inspired methods, which use fixed parameters to formulate robot search trajectories, a fuzzy inference system is designed to perceive the environment and adjust trajectory parameters based on the current search situation. The robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. B. Olfactory-based Navigation via Model-based Reinforcement Learning Methods. This method analogizes the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations. A hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimates are fed to the robot. A decision-making model based on a fuzzy inference system is designed to dynamically fuse information from two maps and balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration-based path planning algorithm solves the optimal action policy. C. Robotic Odor Source Localization via Deep Learning-based Methods. This method investigates the viability of implementing deep learning algorithms to solve the odor source localization problem. The primary objective is to obtain a deep learning model that guides a mobile robot to find an odor source without explicating search strategies. To achieve this goal, two kinds of deep learning models, including adaptive neuro-fuzzy inference system (ANFIS) and deep neural networks (DNNs), are employed to generate the olfactory-based navigation strategies. Multiple training data sets are acquired by applying two traditional methods in both simulation and on-vehicle tests to train deep learning models. After the supervised training, the deep learning models are verified with unseen search situations in simulation and real-world environments. All proposed algorithms are implemented in simulation and on-vehicle tests to verify their effectiveness. Compared to traditional methods, experiment results show that the proposed algorithms outperform them in terms of the success rate and average search time. Finally, the future research directions are presented at the end of the dissertation

Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully “translated” into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms

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

An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search

Robots have been used to model nature, while nature in turn can contribute to the real-world artifacts we construct. One particular domain of interest is chemical search where a number of efforts are underway to construct mobile chemical search and localization systems. We report on a project that aims at constructing such a system based on our understanding of the pheromone communication system of the moth. Based on an overview of the peripheral processing of chemical cues by the moth and its role in the organization of behavior we emphasize the multimodal aspects of chemical search, i.e. optomotor anemotactic chemical search. We present a model of this behavior that we test in combination with a novel thin metal oxide sensor and custom build mobile robots. We show that the sensor is able to detect the odor cue, ethanol, under varying flow conditions. Subsequently we show that the standard model of insect chemical search, consisting of a surge and cast phases, provides for robust search and localization performance. The same holds when it is augmented with an optomotor collision avoidance model based on the Lobula Giant Movement Detector (LGMD) neuron of the locust. We compare our results to others who have used the moth as inspiration for the construction of odor robot

匂い源探索における状態依存的な複数感覚統合に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 神崎 亮平, 東京大学教授 下山 勲, 東京大学教授 竹内 昌治, 東京大学特任講師 安藤 規泰, 総合研究大学院大学講師 木下 充代University of Tokyo(東京大学

Robotic Gas Source Localization in an Industrial Environment

Gas leaks are an important safety issue in oil and gas production. For example, natural gas often contains large portions of hydrogen sulfide, a gas that is lethal to humans in concentrations as low as 0.1%. In addition natural gas itself is explosive. During the past fifteen years, a considerable number of studies have been made into how to detect and localize gas leaks. Equipped with sensors measuring the point concentration of specific substances, a variety of mobile robots and algorithms have been looking for gas sources indoors and outdoors, underground and under water, in airless conditions and in windy dittos. Due to the complexity of turbulence and the limitations of gas sensors, robotic gas source localization has turned out to be complicated and so far it has not made its way to large scale real world applications. This study is an attempt to bring robotic gas source localization a bit closer to that. Three algorithms, carefully chosen from the literature, are adapted to an industrial environment. In addition, two novel strategies are derived from the original ones through combination of them. A comparative study between the five algorithms is made where their performances are evaluated and compared. This study has been conducted within a project of ABB in Oslo that investigates how industrial robots can be used in an oil and gas-context

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

Biomimetic set up for chemosensor-based machine olfaction

The thesis falls into the field of machine olfaction and accompanying experimental set up for chemical gas sensing. Perhaps more than any other sensory modality, chemical sensing faces with major technical and conceptual challenges: low specificity, slow response time, long term instability, power consumption, portability, coding capacity and robustness. There is an important trend of the last decade pushing artificial olfaction to mimic the biological olfaction system of insects and mammalians. The designers of machine olfaction devices take inspiration from the biological olfactory system, because animals effortlessly accomplish some of the unsolved problems in machine olfaction. In a remarkable example of an olfactory guided behavior, male moths navigate over large distances in order to locate calling females by detecting pheromone signals both rapidly and robustly. The biomimetic chemical sensing aims to identify the key blocks in the olfactory pathways at all levels from the olfactory receptors to the central nervous system, and simulate to some extent the operation of these blocks, that would allow to approach the sensing performance known in biological olfactory system of animals. New technical requirements arise to the hardware and software equipment used in such machine olfaction experiments. This work explores the bioinspired approach to machine olfaction in depth on the technological side. At the hardware level, the embedded computer is assembled, being the core part of the experimental set up. The embedded computer is interfaced with two main biomimetic modules designed by the collaborators: a large-scale sensor array for emulation of the population of the olfactory receptors, and a mobile robotic platform for autonomous experiments for guiding olfactory behaviour. At the software level, the software development kit is designed to host the neuromorphic models of the collaborators for processing the sensory inputs as in the olfactory pathway. Virtualization of the set up was one of the key engineering solutions in the development. Being a device, the set up is transformed to a virtual system for running data simulations, where the software environment is essentially the same, and the real sensors are replaced by the virtual sensors coming from especially designed data simulation tool. The proposed abstraction of the set up results in an ecosystem containing both the models of the olfactory system and the virtual array. This ecosystem can loaded from the developed system image on any personal computer. In addition to the engineering products released in the course of thesis, the scientific results have been published in three journal articles, two book chapters and conference proceedings. The main results on validation of the set up under the scenario of robotic odour localization are reported in the book chapters. The series of three journal articles covers the work on the data simulation tool for machine olfaction: the novel model of drift, the models to simulate the sensor array data based on the reference data set, and the parametrized simulated data and benchmarks proposed for the first time in machine olfaction. This thesis ends up with a solid foundation for the research in biomimetic simulations and algorithms on machine olfaction. The results achieved in the thesis are expected to give rise to new bioinspired applications in machine olfaction, which could have a significant impact in the biomedical engineering research area.Esta tesis se enmarca en el campo de bioingeneria, mas particularmente en la configuración de un sistema experimental de sensores de gases químicos. Quizás más que en cualquier otra modalidad de sensores, los sensores químicos representan un conjunto de retos técnicos y conceptuales ya que deben lidiar con problemas como su baja especificidad, su respuesta temporal lenta, su inestabilidad a largo plazo, su alto consumo enérgético, su portabilidad, así como la necesidad de un sistema de datos y código robusto. En la última década, se ha observado una clara tendencia por parte de los sistemas de machine olfaction hacia la imitación del sistema de olfato biológico de insectos y mamíferos. Los diseñadores de estos sistemas se inspiran del sistema olfativo biológico, ya que los animales cumplen, sin apenas esfuerzo, algunos de los escenarios no resueltos en machine olfaction. Por ejemplo, las polillas machos recorren largas distancias para localizar las polillas hembra, detectando sus feromonas de forma rápida y robusta. La detección biomimética de gases químicos tiene como objetivo identificar los elementos fundamentales de la vía olfativa a todos los niveles, desde los receptores olfativos hasta el sistema nervioso central, y simular, en cierta medida, el funcionamiento de estos bloques, lo que permitiría acercar el rendimiento de la detección al rendimiento de los sistemas olfativos conociodos de los animales. Esto conlleva nuevos requisitos técnicos a nivel de equipamiento tanto hardware como software utilizado en este tipo de experimentos de machine olfaction. Este trabajo propone un enfoque bioinspirado para la ¿machine olfaction¿, explorando a fondo la parte tecnológica. A nivel hardware, un ordenador embedido se ha ensamblado, siendo ésta la parte más importante de la configuración experimental. Este ordenador integrado está interconectado con dos módulos principales biomiméticos diseñados por los colaboradores: una matriz de sensores a gran escala y una plataforma móvil robotizada para experimentos autónomos. A nivel software, el kit de desarrollo software se ha diseñado para recoger los modelos neuromórficos de los colaboradores para el procesamiento de las entradas sensoriales como en la vía olfativa biológica. La virtualización del sistema fue una de las soluciones ingenieriles clave de su desarrollo. Al ser un dispositivo, el sistema se ha transformado en un sistema virtual para la realización de simulaciones de datos, donde el entorno de software es esencialmente el mismo, y donde los sensores reales se sustituyen por sensores virtuales procedentes de una herramienta de simulación de datos especialmente diseñada. La propuesta de abstracción del sistema resulta en un ecosistema que contiene tanto los modelos del sistema olfativo como la matriz virtual . Este ecosistema se puede cargar en cualquier ordenador personal como una imagen del sistema desarrollado. Además de los productos de ingeniería entregados en esta tesis, los resultados científicos se han publicado en tres artículos en revistas, dos capítulos de libros y los proceedings de dos conferencias internacionales. Los principales resultados en la validación del sistema en el escenario de la localización robótica de olores se presentan en los capítulos del libro. Los tres artículos de revistas abarcan el trabajo en la herramienta de simulación de datos para machine olfaction: el novedoso modelo de drift, los modelos para simular la matriz de sensores basado en el conjunto de datos de referencia, y la parametrización de los datos simulados y los benchmarks propuestos por primera vez en machine olfaction. Esta tesis ofrece una base sólida para la investigación en simulaciones biomiméticas y en algoritmos en machine olfaction. Los resultados obtenidos en la tesis pretenden dar lugar a nuevas aplicaciones bioinspiradas en machine olfaction, lo que podría tener un significativo impacto en el área de investigación en ingeniería biomédic