Enhanced foraging in robot swarms using collective Lévy walks

A key aspect of foraging in robot swarms is optimizing the search efficiency when both the environment and target density are unknown. Hence, designing optimal exploration strategies is desirable. This paper proposes a novel approach that extends the individual Lévy walk to a collective one. To achieve this, we adjust the individual motion through applying an artificial potential field method originating from local communication. We demonstrate the effectiveness of the enhanced foraging by confirming that the collective trajectory follows a heavy-tailed distribution over a wide range of swarm sizes. Additionally, we study target search efficiency of the proposed algorithm in comparison with the individual Lévy walk for two different types of target distributions: homogeneous and heterogeneous. Our results highlight the advantages of the proposed approach for both target distributions, while increasing the scalability to large swarm sizes. Finally, we further extend the individual exploration algorithm by adapting the Lévy walk parameter α, altering the motion pattern based on a local estimation of the target density. This adaptive behavior is particularly useful when targets are distributed in patches

Exploration of human search behaviour: a multidisciplinary perspective

The following work presents an exploration of human search behaviour both from biological and computational perspectives. Search behaviour is defined as the movements made by an organism while attempting to find a resource. This work describes some of the principal procedures used to record movement, methods for analysing the data and possible ways of interpreting the data. In order to obtain a database of searching behaviour, an experimental setup was built and tested to generate the search paths of human participants. The test arena occupied part of a football field and the targets consisted of an array of 20 golf balls. In the first set of experiments, a random and regular distribution of targets were tested. For each distribution, three distinct conspicuity levels were constructed: a cryptic level, in which targets were painted the same colour as the grass, a semi-conspicuous level in which targets were left white and a conspicuous condition in which the position of each target was marked by a red flag, protruding one metre from the ground. The subjects tested were 9-11 year old children and their search paths were collected using a GPS device. Subjects did not recognise the spatial cues regarding the way targets were spatially distributed. A minimal decision model, the bouncing search model, was built based on the characteristics of the childrens search paths. The model produced an outstanding fit of the children’s behavioural data. In the second set of experiments, a new group of children were tested for two new distributions obtained by arranging the targets in patches. Again, children appeared unable to recognise spatial information during the collection processes. The children’s behaviour once again produced a good match with that of the bouncing search model. This work introduces several new methodological aspects to be explored to further understand the decision processes involved when humans search. Also, it illustrates that integrating biology and computational science can result in innovative research

Landscape scale mapping of tundra vegetation structure at ultra-high resolution using UAVs and computer vision

Ilmastomuutoksella on voimakkain vaikutus suurten leveysasteiden ekosysteemeissä, jotka ovat sopeutuneet viileään ilmastoon. Jotta suurella mittakaavalla havaittuja muutoksia tundrakasvillisuudessa ja niiden takaisinkytkentävaikutuksia ilmastoon voidaan ymmärtää ja ennustaa luotettavammin, on syytä tarkastella mitä tapahtuu pienellä mittakaavalla; jopa yksittäisissä kasveissa. Lähivuosikymmenten aikana tapahtunut teknologinen kehitys on mahdollistanut kustannustehokkaiden, kevyiden ja pienikokoisten miehittämättömien ilma-alusten (UAV) yleistymisen. Erittäin korkearesoluutioisten aineistojen (pikselikoko <10cm) lisääntyessä ja tullessa yhä helpommin saataville, ympäristön tarkastelussa käytetyt kaukokartoitusmenetelmät altistuvat paradigmanmuutokselle, kun konenäköön ja -oppimiseen perustuvat algoritmit ja analyysit yleistyvät. Menetelmien käyttöönotto on houkuttelevaa, koska ne mahdollistavat joustavan ja pitkälle automatisoidun aineistonkeruun ja erittäin tarkkojen kaukokartoitustuotteiden tuottamisen vaikeasti tavoitettavilta alueilta, kuten tundralla. Luotettavien tulosten saaminen vaatii kuitenkin huolellista suunnittelua sekä prosessointialgoritmien ja -parametrien pitkäjänteistä testaamista. Tässä tutkimuksessa tarkasteltiin, kuinka tarkasti tavallisella digitaalikameralla kerätyistä ilmakuvista johdetuilla muuttujilla voidaan kartoittaa kasvillisuuden rakennetta maisemamittakaavalla. Kilpisjärvellä Pohjois-Fennoskandiassa kerättiin dronella kolmensadan hehtaarin kokoiselta alueelta yhteensä noin 10 000 ilmakuvasta koostuva aineisto. Lisäksi alueella määritettiin 1183 pisteestä dominantti putkilokasvillisuus, sekä kasvillisuuden korkeus. Ilmakuvat prosessoitiin tiheiksi kolmiulotteisiksi pistepilviksi konenäköön ja fotogrammetriaan perustuvalla SfM (Structure from Motion) menetelmällä. Pistepilvien pohjalta interpoloitiin maastomalli sekä kasvillisuuden korkeusmalli. Lisäksi tuotettiin koko alueen kattava ilmakuvamosaiikki. Näiden aineistojen pohjalta laskettiin muuttujia, joita käytettiin yhdessä maastoreferenssiaineiston kanssa kasvillisuuden objektipohjaisessa analyysissä (GEOBIA, Geographical Object-Based Image Analysis). Suodatetut maanpintapisteet vastasivat luotettavasti todellista maanpinnan korkeutta koko alueella ja tuotetut korkeusmallit korreloivat voimakkaasti maastoreferenssiaineiston kanssa. Maastomallin virhe oli suurin alueilla, joilla oli korkeaa kasvillisuutta. Valaistusolosuhteissa ja kasvillisuudessa tapahtuneet muutokset ilmakuvien keruun aikana aiheuttivat haasteita objektipohjaisen analyysin molemmissa vaiheissa: segmentoinnissa ja luokittelussa. mutta kokonaistarkkuus parani 0,27:stä 0,,54:n kun luokitteluun lisättiin topografiaa, kasvillisuuden korkeutta ja tekstuuria kuvaavia muuttujia ja kohdeluokkien lukumäärää vähennettiin. Konenäköön ja –oppimiseen perustuvat menetelmät pystyvät tuottamaan tärkeää tietoa tundran kasvillisuuden rakenteesta, erityisesti kasvillisuuden korkeudesta, maisemassa. Lisää tutkimusta kuitenkin tarvitaan parhaiden algoritmien ja parametrien määrittämiseksi tundraympäristössä, jossa ympäristöolosuhteet muuttuvat nopeasti ja kasvillisuus on heterogeenistä ja sekoittunutta, mikä aiheuttaa eroja ilmakuvien välillä ja lisää vaikeuksia analyyseissä.Climate change has the strongest impact on high-latitude ecosystems that are adapted to cool climates. In order to better understand and predict the changes in tundra vegetation observed on large scales as well as their feedbacks onto climate, it is necessary to look at what is happening at finer scales; even in individual plants. Technological developments over the past few decades have enabled the spread of cost-effective, light and small unmanned aerial vehicles (UAVs). As very high-resolution data (pixel size <10cm) becomes more and more available, the remote sensing methods used in environmental analysis become subject to a paradigm shift as algorithms and analyzes based on machine vision and learning turn out to be more common. Harnessing new methods is attractive because they allow flexible and highly automated data collection and the production of highly accurate remote sensing products from hard-to-reach areas such as the tundra. However, obtaining reliable results requires careful planning and testing of processing algorithms and parameters. This study looked at how accurately variables derived from aerial images collected with an off-the-shelf digital camera can map the vegetation structure on a landscape scale. In Kilpisjärvi, northern Fennoscandia, a total of ~ 10,000 aerial photographs were collected by drone covering an area of three hundred hectares. In addition, dominant vascular plants were identified from 1183 points in the area, as well as vegetation height. Aerial images were processed into dense three-dimensional point clouds by using SfM (Structure from Motion) method, which is based on computer vision and digital photogrammetry. From the point clouds terrain models and vegetation height models were interpolated. In addition, image mosaic covering the entire area was produced. Based on these data, predictive variables were calculated, which were used together with the terrain reference data in Geographical Object-Based Image Analysis (GEOBIA). The filtered ground points corresponded to observations throughout the region, and the produced elevation models strongly correlated with the ground reference data. The terrain model error was greatest in areas with tall vegetation. Changes in lighting conditions and vegetation during aerial image surveys posed challenges in both phases of object-based analysis: segmentation and classification. but overall accuracy improved from 0.27 to 0.54 when topography, vegetation height and texture variables were added to the classifier and the number of target classes was reduced. Methods based on machine vision and learning can produce important information about vegetation structure, vegetation height, in a landscape. However, more research is needed to determine the best algorithms and parameters in a tundra environment where environmental conditions change rapidly and vegetation is heterogeneous and mixed, causing differences between aerial images and difficulties in analyses

Evolutionary potential of a dispersal-restricted species in response to climate change

Habitat replacement and fragmentation associated with projected climate change pose a critical threat to global biodiversity. Edaphically limited plant species with restricted dispersal abilities will be especially handicapped to track their optimal climate spatially. Instead, the persistence of these species will depend on their capacity to adapt in situ to novel climate regimes. Here I evaluated the evolutionary potential of Lasthenia fremontii, an annual plant species restricted to ephemeral wetlands called vernal pools in California to adapt to the projected patterns of climate change. Across L. fremontii distribution there is a latitudinal gradient in precipitation which, combined with reduced gene flow rates, might be driving adaptive divergence in climate tolerances among populations of this species. Accordingly, I estimated (1) the spatial distribution of genetic variation and gene flow across the species range, (2) the extent to which the climate variability experienced by the vernal pools has selected for seed dormancy in L. fremontii populations, and (3) the degree of local adaptation and additive genetic variation in response to a simulated spectrum of precipitation conditions. My analyses revealed an isolation-by-distance model of genetic differentiation among vernal pools and a low to moderate degree of genetic differentiation among pools within a single complex. Germination time was faster in the northernmost (historically wettest) population than in the southernmost (historically driest) population but with mixed responses in others. I observed a significant positive relationship between the historical variability in autumn precipitation and extent of seed dormancy in a population. These findings were consistent with the patterns of adaptation to local rainfall conditions observed among three of the populations reciprocally exposed to local but extreme precipitation conditions. Unexpectedly, however, populations expressed higher levels of additive genetic variation but reduced fitness under extreme drought events in comparison with moderate and extreme rainfall conditions. Further, both peripheral populations expressed optimal fitness in their native conditions but the central population did not. Taken together, these results revealed that restricted gene flow, coupled with differences in the history of local selection pressures, have led to significant divergence in the climatic tolerances and relative evolutionary potential of populations. Contrary to intuitive expectations, central range populations with less predictable climate regimes may not preserve adaptive potential for more extreme environments. That potential may only be present at the current environmental extremes

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Comparing Recent Advances in Estimating and Measuring Oil Slick Thickness: An MPRI Technical Report

Characterization of the degree and extent of surface oil during and after an oil spill is a critical part of emergency response and Natural Resource Damage Assessment (NRDA) activities. More specifically, understanding floating oil thickness in real-time can guide response efforts by directing limited assets to priority cleanup areas; aid in ‘volume released’ estimates; enhance fate, transport and effects modeling capabilities; and support natural resource injury determinations. An international workshop brought researchers from agencies, academia and industry who were advancing in situ and remote oil characterization tools and methods together with stake holders and end users who rely on information about floating oil thickness for mission critical assignments (e.g., regulatory, assessment, cleanup, research). In total, over a dozen researchers presented and discussed their findings from tests using various different sensors and sensor platforms. The workshop resulted in discussions and recommendations for better ways to leverage limited resources and opportunities for advancing research and developing tools and methods for oil spill thickness measurements and estimates that could be applied during spill responses. One of the primary research gaps identified by the workshop participants was the need for side-by-side testing and validation of these different methods, to better understand their respective strengths, weaknesses and technical readiness levels, so that responders would be better able to make decisions about what methods are appropriate to use under what conditions, and to answer the various questions associated with response actions. Approach: 1) Convene a more in-depth multi day researcher workshop to discuss and develop specific workplan to conduct side-by-side validation and verification experiments for testing oil thickness measurements. 2) Conduct the validation and verification experiments in controlled environments: the Coastal Response Research Center (CRRC) highbay at the University of New Hampshire (UNH); and the Ohmsett National Oil Spill Response Research & Renewable Energy Test Facility

Identificación y aplicación en la conservación de los efectos señal, ruido y taxonómicos en patrones de diversidad

Ongoing research on butterflies and birds in the Great Basin has identified biogeographic patterns while elucidating how dynamic measures of diversity (species richness and turnover) affect inferences for conservation planning and adaptive management. Nested subsets analyses suggested that processes influencing predictability of assemblage composition differ among taxonomic groups, and the relative importance of those processes may vary spatially within a taxonomic group. There may be a time lag between deterministic environmental changes and a detectable faunal response, even for taxonomic groups that are known to be sensitive to changes in climate and land cover. Measures of beta diversity were sensitive to correlations between sampling resolution and local environmental heterogeneity. Temporal and spatial variation in species composition indicated that spatially extensive sampling is more effective for drawing inferences about biodiversity responses to environmental change than intensive sampling at relatively few, smaller sites.Los estudios de mariposas y aves en el Great Basin han identificado patrones biogeográficos que permiten evaluar cómo las medidas dinámicas de biodiversidad (riqueza específica y renovación de especies) pueden afectar la planificación y la gestión adaptativa de la conservación. El análisis de subgrupos anidados sugiere que los procesos que influyen en la predicibilidad de la composición de los grupos difieren entre los distintos grupos taxonómicos. Asimismo la importancia relativa de estos procesos puede variar espacialmente dentro de un grupo taxonómico. Puede haber un retraso en el tiempo entre los cambios ambientales deterministas y una respuesta faunística detectable, incluso para los grupos taxonómicos que se sabe que son sensibles a los cambios del clima y de la cubierta del suelo. Las medidas de diversidad beta eran sensibles a las correlaciones entre la resolución del muestreo y la heterogeneidad ambiental local. La variación espacial y temporal en la composición de especies indicó que el muestreo extensivo en el espacio es más efectivo, para obtener inferencias sobre cómo responde la biodiversidad a cambios ambientales, que el muestreo intensivo, en relativamente pocos sitios y más pequeños

Identification and conservation application of signal, noise, and taxonomic effects in diversity patterns

Ongoing research on butterflies and birds in the Great Basin has identified biogeographic patterns while elucidating how dynamic measures of diversity (species richness and turnover) affect inferences for conservation planning and adaptive management. Nested subsets analyses suggested that processes influencing predictability of assemblage composition differ among taxonomic groups, and the relative importance of those processes may vary spatially within a taxonomic group. There may be a time lag between deterministic environmental changes and a detectable faunal response, even for taxonomic groups that are known to be sensitive to changes in climate and land cover. Measures of beta diversity were sensitive to correlations between sampling resolution and local environmental heterogeneity. Temporal and spatial variation in species composition indicated that spatially extensive sampling is more effective for drawing inferences about biodiversity responses to environmental change than intensive sampling at relatively few, smaller sites

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

Spatial Heterogeneity in Ecology

This project predominantly investigated the implications of spatial heterogeneity in the ecological processes of competition and infection. Empirical analysis of spatial heterogeneity was carried out using the lepidopteran species Plodia interpunctella. Using differently viscous food media, it was possible to alter the movement rate of larvae. Soft Foods allow the movement rate of larvae to be high, so that individuals can disperse through the environment and avoid physical encounters with conspecifics. Harder foods lower the movement rate of larvae, restricting the ability of individuals to disperse away from birth sites and avoid conspecifics encounters. Increasing food viscosity and lowering movement rate therefore has the effect of making uniform distributed larval populations more aggregated and patchy. Different spatial structures changed the nature of intraspecific competition, with patchy populations characterised by individuals experiencing lower growth rates and greater mortality because of the reduced food and space available within densely packed aggregations. At the population scale, the increased competition for food individuals experience in aggregations emerges as longer generational cycles and reduced population densities. Aggregating individuals also altered the outcome of interspecific competition between Plodia and Ephestia cautella. In food media that allowed high movement rates, Plodia had a greater survival rate than Ephestia because the larger movement rate of Plodia allowed it to more effectively avoid intraspecific competition. Also the faster growth rate, and so larger size, of Plodia allowed it to dominate interspecific encounters by either predating or interfering with the feeding of Ephestia. In food that restricts movement, the resulting aggregations cause Plodia to experience more intraspecific encounters relative to interspecific, reducing its competitive advantage and levelling the survival of the two species. Spatial structure also affected the dynamics of a Plodia-granulosis virus interaction and the evolution of virus infectivity. Larval aggregation forced transmission to become limited to within host patches, making the overall prevalence of the virus low. However potentially high rates of cannibalism and multiple infections within overcrowded host aggregations caused virus-induced mortality to be high, as indicated by the low host population density when virus is presented. Also aggregated host populations cause the evolution of lower virus infectivity, where less infective virus strains maintain more susceptible hosts within the aggregation and so possess a greater transmission rate. The pattern of variation in resistance of Plodia interpunctella towards its granulosis virus was found using two forms of graphical analysis. There was a bimodal pattern of variation, with most individuals exhibiting either low or high levels of resistance. This pattern was related to a resistance mechanism that is decreasingly costly to host fitness