Effectiveness and Robustness of Robot Infotaxis for Searching in Dilute Conditions

Tracking scents and locating odor sources is a major challenge in robotics. The odor plume is not a continuous cloud but consists of intermittent odor patches dispersed by the wind. Far from the source, the probability of encountering one of these patches vanishes. In such dilute conditions, a good strategy is to first ‘explore’ the environment and gather information, then ‘exploit’ current knowledge and direct toward the estimated source location. Infotactic navigation has been recently proposed to strike the balance between exploration and exploitation. Infotaxis was tested in simulation and produced trajectories similar to those observed in the flight of moths attracted by a sexual pheromone. In this paper, we assess the performance of infotaxis in dilute conditions by combining robotic experiments and simulations. Our results indicate that infotaxis is both effective (seven detections on average were sufficient to reach the source) and robust (the source is found in presence of inaccurate modeling by the searcher). The biomimetic characteristic of infotaxis is also preserved when searching with a robot in a real environment

Search strategies and the automated control of plant diseases

We propose the use of the "infotaxis" search strategy as the navigation system of a robotic platform, able to search and localize infectious foci by detecting the changes in the profile of volatile organic compounds emitted by and infected plant. We builded a simple and cost effective robot platform that substitutes odour sensors in favour of light sensors and study their robustness and performance under non ideal conditions such as the exitence of obstacles due to land topology or weeds

Design and Performance Evaluation of an Infotaxis-Based Three-Dimensional Algorithm for Odor Source Localization

In this paper we tackle the problem of finding the source of a gaseous leak with a robot in a three-dimensional (3-D) physical space. The proposed method extends the operational range of the probabilistic Infotaxis algorithm [1] into 3-D and makes multiple improvements in order to increase its performance in such settings. The method has been tested systematically through high-fidelity simulations and in a wind tunnel emulating realistic conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments. The algorithm shows good performance in various environmental conditions, particularly in high wind speeds and different source release rates

Information-based search for an atmospheric release using a mobile robot: algorithm and experiments

Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring, thus it has been an active research area for several years known as source term estimation. This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with a low-cost gas sensor responds to the hazardous source stimulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for source term estimation based on a mobile robot and a low-cost sensor

Optimal Gas Sensors Arrangement in Odor Searching Robot

This paper presents an analysis of an optimal sensor arrangement in Odor Searching Robot (OSR). 5 gas sensors integrated in OSR can help the OSR to navigate to the source. Since low cost, low computation and robust robot is preferred in swarm robot application, the OSR, as an individual robot of swarm in this study, is designed to be able to switch into the mode 3 or the mode 5 in order to analyze the optimal distance of the gas sensors arrangement that can be integrated in the OSR. By knowing the optimal sensor arrangement, the low cost and or the low computation OSR can be established. Algorithms of fuzzy logic for 3 and 5 gas sensors are tested in open environment. The concentration of gas is used as the input of the fuzzy logic. The robot uses the concentration, as its parameters in determining which way that it should take. From this research, it can be concluded that there was no significant difference between using 3 gas sensors or 5 gas sensors

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

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

Infotaxie collective : coordonner une équipe d'agents pour trouver une source

Rapport de stage de Licence 3National audienceCe stage a pour objectif de concevoir et d’étudier un système dynamique discret,construit à partir d’automates cellulaires et de systèmes multi-agents, qui répond au problèmede l’infotaxie. Le problème de l’infotaxie consiste pour des individus sans mémoire et avec unperception limitée de leur environnement à retrouver une source qui émet un signal. Ce signala la particularité que les agents ne peuvent ni mesurer son intensité, ni déterminer la directionde laquelle il vient lorsqu’ils le détectent. Ce modèle s’inspire du comportement d’unecertaine espèce d’amibes sociales et de leur stratégie de regroupement

Characterization of aminergic neurons controlling behavioral persistence and motivation in Drosophila melanogaster

Deprivation is at odds with survival. To obliterate their condition of hunger animals engage in costly foraging behavior. This conundrum demands unceasing integration of external sensory processing and internal metabolic monitors. Unsurprisingly, such critical behaviors are translated to strong impulses. If unchecked, however, impulsivity can trap animals in unfavorable behavioral states and prevent them from exploiting other valuable opportunities. Categorically, motivational mechanisms have been proposed as the conduit to comply with or decline a response to a strong impulse. Thus, motivation emerges as a critical determinant for observed animal behavioral variability at a given time. Although neuronal circuit diagrams may be deceptively static, neuromodulation can implement behavioral variability in the nervous systems. Bioamines, such as dopamine and norepinephrine, mediate modulatory impact on intrinsic motivational circuits that govern feeding and reward. Across model organisms, however, how animals integrate and update decision-making based on the current motivational and internal states are still poorly understood at the molecular and circuitry levels. Due to its extensive toolbox and amenable miniature nervous systems, Drosophila melanogaster is poised to enrich the current perspective for these concepts. For Drosophila melanogaster, certain odors are salient cues for long distance foraging events. To explore how starved flies make goal-directed decisions, I developed a novel spherical treadmill paradigm. Through the utilization of high-resolution behavioral analyses and tight control of, otherwise highly turbulent, odor delivery, I found that food-deprived flies tracked vinegar persistently even in the repeated absence of a food reward. Combining this behavioral paradigm with immediate neuronal manipulations revealed that this innate persistence recruited circuits that are traditionally linked with learning and memory in an internal state-dependent manner. TH+ cluster dopaminergic neurons, operators of punishment learning, and Dop1R2 signaling enabled this olfactory-driven persistence. Downstream of these dopaminergic neurons, a single mushroom body output neuron, MVP2 was crucial for persistence. MVP2 was necessary and sufficient to integrate hunger state as the underlying motivational drive for food-seeking persistence. Furthermore, I investigated how this strong impulse is counteracted when a fly reaches its goal, nutritious food. A change from odor tracking to food consumption demands the coordination of different sensory systems and motor control subunits. Norepinephrine is implemented in such global switches; such as fight or flight transitions. Using optogenetic manipulation, I demonstrated that the food-seeking drive was suppressed by, an insect norepinephrine analog, octopaminergic input, via VPM4 neurons. Being connected to MVP2 synaptically, which we showed using high-resolution tracing techniques, and a surrogate for feeding at the neuronal level, VPM4 neurons acted as the inhibitory brake on persistent odor tracking to allow feeding related behavior. As a culmination of novel paradigm development, thermo/optogenetic neuronal manipulations and connectomics, this work presents a neuronal microcircuit that recapitulates the alterations of animal behavior faithfully from odor tracking to olfactory suppression during feeding. Specific subsets of dopaminergic and octopaminergic neurons are found to be mediators of motivationally driven events. My findings provide fresh mechanistic insights on how multimodal integration can occur in the brain, how such systems are prone to the internal states, and offers several plausible explanations on how persistence emerges. Finally, this work might serve as a template to better understand the roles and the functional diversity of mammalian aminergic neurons

Characterization of aminergic neurons controlling behavioral persistence and motivation in Drosophila melanogaster

Deprivation is at odds with survival. To obliterate their condition of hunger animals engage in costly foraging behavior. This conundrum demands unceasing integration of external sensory processing and internal metabolic monitors. Unsurprisingly, such critical behaviors are translated to strong impulses. If unchecked, however, impulsivity can trap animals in unfavorable behavioral states and prevent them from exploiting other valuable opportunities. Categorically, motivational mechanisms have been proposed as the conduit to comply with or decline a response to a strong impulse. Thus, motivation emerges as a critical determinant for observed animal behavioral variability at a given time. Although neuronal circuit diagrams may be deceptively static, neuromodulation can implement behavioral variability in the nervous systems. Bioamines, such as dopamine and norepinephrine, mediate modulatory impact on intrinsic motivational circuits that govern feeding and reward. Across model organisms, however, how animals integrate and update decision-making based on the current motivational and internal states are still poorly understood at the molecular and circuitry levels. Due to its extensive toolbox and amenable miniature nervous systems, Drosophila melanogaster is poised to enrich the current perspective for these concepts. For Drosophila melanogaster, certain odors are salient cues for long distance foraging events. To explore how starved flies make goal-directed decisions, I developed a novel spherical treadmill paradigm. Through the utilization of high-resolution behavioral analyses and tight control of, otherwise highly turbulent, odor delivery, I found that food-deprived flies tracked vinegar persistently even in the repeated absence of a food reward. Combining this behavioral paradigm with immediate neuronal manipulations revealed that this innate persistence recruited circuits that are traditionally linked with learning and memory in an internal state-dependent manner. TH+ cluster dopaminergic neurons, operators of punishment learning, and Dop1R2 signaling enabled this olfactory-driven persistence. Downstream of these dopaminergic neurons, a single mushroom body output neuron, MVP2 was crucial for persistence. MVP2 was necessary and sufficient to integrate hunger state as the underlying motivational drive for food-seeking persistence. Furthermore, I investigated how this strong impulse is counteracted when a fly reaches its goal, nutritious food. A change from odor tracking to food consumption demands the coordination of different sensory systems and motor control subunits. Norepinephrine is implemented in such global switches; such as fight or flight transitions. Using optogenetic manipulation, I demonstrated that the food-seeking drive was suppressed by, an insect norepinephrine analog, octopaminergic input, via VPM4 neurons. Being connected to MVP2 synaptically, which we showed using high-resolution tracing techniques, and a surrogate for feeding at the neuronal level, VPM4 neurons acted as the inhibitory brake on persistent odor tracking to allow feeding related behavior. As a culmination of novel paradigm development, thermo/optogenetic neuronal manipulations and connectomics, this work presents a neuronal microcircuit that recapitulates the alterations of animal behavior faithfully from odor tracking to olfactory suppression during feeding. Specific subsets of dopaminergic and octopaminergic neurons are found to be mediators of motivationally driven events. My findings provide fresh mechanistic insights on how multimodal integration can occur in the brain, how such systems are prone to the internal states, and offers several plausible explanations on how persistence emerges. Finally, this work might serve as a template to better understand the roles and the functional diversity of mammalian aminergic neurons