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 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

Sensing array for coherence analysis of modulated aquatic chemical plumes

An electrochemical sensor array can provide information about the spatial and temporal distribution of chemicals in liquid turbulent plumes. Planar laser induced fluorescence (PLIF) and amperometric sensor arrays were used to record signals from modulated chemical plumes released into a recirculating aquatic flume. Coherence analysis was applied to extract the frequency components contained in the sensor response. Effects due to release distance, modulation frequency, and array orientation were investigated. This study has demonstrated that frequency encoded information can be extracted from a turbulent chemical plume using an array of amperometric sensors with optimized three-dimensional geometry and tuning.M.S.Committee Chair: Janata, Jiri; Committee Member: Lyon, Andrew; Committee Member: Weissburg, Mar

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

Cluster Space Gradient Contour Tracking for Mobile Multi-robot Systems

Multi-robot systems have the potential to exceed the performance of many existing robotic systems by taking advantage of the cluster’s redundancy, coverage and flexibility. These unique characteristics of multi-robot systems allow them to perform tasks such as distributed sensing, gradient climbing, and collaborative work more effectively than any single robot system. The purpose of this research was to augment the existing cluster space control technique in order to demonstrate effective gradient-based functionality, specifically, that of tracking gradient contours of specified concentration levels. To do this, we needed first to estimate the direction of the gradient and/or contour based on the real-time measurements made by sensors on the distributed robots, and second, to steer the cluster in the appropriate direction. Successful simulation, characterization, and experimental testing with the developed testbed have validated this approach. The controller enabled the cluster to sense and follow a contour-based trajectory in a parameter field using both a kayak cluster formation and also the land based Pioneer robots. The positive results of this research demonstrate the robustness of the cluster space control while using the contour following technique and suggest the possibility of further expansion with field applications

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

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

Chemical source localization fusing concentration information in the presence of chemical background noise

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presente

Composite random search strategies based on non-directional sensory cues

Many foraging animals find food using composite random search strategies, which consist of intensive and extensive search modes. Models of composite search can generate predictions about how optimal foragers should behave in each search mode, and how they should determine when to switch between search modes. Most of these models assume that foragers use resource encounters to decide when to switch between search modes. Empirical observations indicate that a variety of organisms use non-directional sensory cues to identify areas that warrant intensive search. These cues are not precise enough to allow a forager to directly orient itself to a resource, but can be used as a criterion to determine the appropriate search mode. As a potential example, a forager might use olfactory information, which could help it determine if an area is worth searching carefully. We developed a model of composite search based on non-directional sensory cues. With simulations, we compared the search efficiencies of composite foragers that use resource encounters as their mode-switching criterion with those that use non-directional sensory cues. We identified optimal search patterns and mode-switching criteria on a variety of resource distributions, characterized by different levels of resource aggregation and density. On all resource distributions, foraging strategies based on the non-directional sensory criterion were more efficient than those based on the resource encounter criterion. Strategies based on the non-directional sensory criterion were also more robust to changes in resource distribution. Our results suggest that current assumptions about the role of resource encounters in models of optimal composite search should be re-examined. The search strategies predicted by our model can help bridge the gap between random search theory and traditional patch-use foraging theory