Autonomous Exploration of Large-Scale Natural Environments

This thesis addresses issues which arise when using robotic platforms to explore large-scale, natural environments. Two main problems are identified: the volume of data collected by autonomous platforms and the complexity of planning surveys in large environments. Autonomous platforms are able to rapidly accumulate large data sets. The volume of data that must be processed is often too large for human experts to analyse exhaustively in a practical amount of time or in a cost-effective manner. This burden can create a bottleneck in the process of converting observations into scientifically relevant data. Although autonomous platforms can collect precisely navigated, high-resolution data, they are typically limited by finite battery capacities, data storage and computational resources. Deployments are also limited by project budgets and time frames. These constraints make it impractical to sample large environments exhaustively. To use the limited resources effectively, trajectories which maximise the amount of information gathered from the environment must be designed. This thesis addresses these problems. Three primary contributions are presented: a new classifier designed to accept probabilistic training targets rather than discrete training targets; a semi-autonomous pipeline for creating models of the environment; and an offline method for autonomously planning surveys. These contributions allow large data sets to be processed with minimal human intervention and promote efficient allocation of resources. In this thesis environmental models are established by learning the correlation between data extracted from a digital elevation model (DEM) of the seafloor and habitat categories derived from in-situ images. The DEM of the seafloor is collected using ship-borne multibeam sonar and the in-situ images are collected using an autonomous underwater vehicle (AUV). While the thesis specifically focuses on mapping and exploring marine habitats with an AUV, the research applies equally to other applications such as aerial and terrestrial environmental monitoring and planetary exploration

Prevalence of Disorders Recorded in Dogs Attending Primary-Care Veterinary Practices in England

Purebred dog health is thought to be compromised by an increasing occurence of inherited diseases but inadequate prevalence data on common disorders have hampered efforts to prioritise health reforms. Analysis of primary veterinary practice clinical data has been proposed for reliable estimation of disorder prevalence in dogs. Electronic patient record (EPR) data were collected on 148,741 dogs attending 93 clinics across central and south-eastern England. Analysis in detail of a random sample of EPRs relating to 3,884 dogs from 89 clinics identified the most frequently recorded disorders as otitis externa (prevalence 10.2%, 95% CI: 9.1-11.3), periodontal disease (9.3%, 95% CI: 8.3-10.3) and anal sac impaction (7.1%, 95% CI: 6.1-8.1). Using syndromic classification, the most prevalent body location affected was the head-and-neck (32.8%, 95% CI: 30.7-34.9), the most prevalent organ system affected was the integument (36.3%, 95% CI: 33.9-38.6) and the most prevalent pathophysiologic process diagnosed was inflammation (32.1%, 95% CI: 29.8-34.3). Among the twenty most-frequently recorded disorders, purebred dogs had a significantly higher prevalence compared with crossbreds for three: otitis externa (P = 0.001), obesity (P = 0.006) and skin mass lesion (P = 0.033), and popular breeds differed significantly from each other in their prevalence for five: periodontal disease (P = 0.002), overgrown nails (P = 0.004), degenerative joint disease (P = 0.005), obesity (P = 0.001) and lipoma (P = 0.003). These results fill a crucial data gap in disorder prevalence information and assist with disorder prioritisation. The results suggest that, for maximal impact, breeding reforms should target commonly-diagnosed complex disorders that are amenable to genetic improvement and should place special focus on at-risk breeds. Future studies evaluating disorder severity and duration will augment the usefulness of the disorder prevalence information reported herein

Verifying the ecological model of peer aggression on Croatian students

Using the ecological approach, the goal of this study was to determine the predictors of physical and verbal peer aggressive behavior. The participants were 880 school students from the fifth to eighth grade (48% boys and 52% girls) and the same number of parents (19% fathers and 61% mothers) as well as 107 teachers. The main analysis was performed using multivariate–multilevel modeling. The following significant predictors of physical peer aggression were obtained: Boys, a greater degree of impulsivity, more parental punishment, poorer school success, more time spent with the media, and the perception of great neighborhood dangerousness. For verbal peer aggression, the significant predictors were: A greater degree of impulsivity, lower level of affective empathy, more parental punishment, lack of parental supervision, lesser peer acceptance, large differences in family income, more time spent with the media, and the perception of great neighborhood dangerousness. A moderating effect of neighborhood dangerousness and parental supervision was found. The results were interpreted within Bronfenbrenner’s ecological model

Noble gas constraints on air-sea gas exchange and bubble fluxes

Author Posting. © American Geophysical Union, 2009. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 114 (2009): C11020, doi:10.1029/2009JC005396.Air-sea gas exchange is an important part of the biogeochemical cycles of many climatically and biologically relevant gases including CO2, O2, dimethyl sulfide and CH4. Here we use a three year observational time series of five noble gases (He, Ne, Ar, Kr, and Xe) at the Bermuda Atlantic Time series Study (BATS) site in tandem with a one-dimensional upper ocean model to develop an improved parameterization for air-sea gas exchange that explicitly includes separate components for diffusive gas exchange and bubble processes. Based on seasonal timescale noble gas data, this parameterization, which has a 1σ uncertainty of ±14% for diffusive gas exchange and ±29% for bubble fluxes, is more tightly constrained than previous parameterizations. Although the magnitude of diffusive gas exchange is within errors of that of Wanninkhof (1992), a commonly used parameterization, we find that bubble-mediated exchange, which is not explicitly included by Wanninkhof (1992) or many other formulations, is significant even for soluble gases. If one uses observed saturation anomalies of Ar (a gas with similar characteristics to O2) and a parameterization of gas exchange to calculate gas exchange fluxes, then the calculated fluxes differ by ∼240% if the parameterization presented here is used compared to using the Wanninkhof (1992) parameterization. If instead one includes the gas exchange parameterization in a model, then the calculated fluxes differ by ∼35% between using this parameterization and that of Wanninkhof (1992). These differences suggest that the bubble component should be explicitly included in a range of marine biogeochemical calculations that incorporate air-sea gas fluxes.Funding from the National Science Foundation Chemical Oceanography program (OCE-0221247 and OCE-0623034)

Eag and HERG potassium channels as novel therapeutic targets in cancer

Voltage gated potassium channels have been extensively studied in relation to cancer. In this review, we will focus on the role of two potassium channels, Ether à-go-go (Eag), Human ether à-go-go related gene (HERG), in cancer and their potential therapeutic utility in the treatment of cancer. Eag and HERG are expressed in cancers of various organs and have been implicated in cell cycle progression and proliferation of cancer cells. Inhibition of these channels has been shown to reduce proliferation both in vitro and vivo studies identifying potassium channel modulators as putative inhibitors of tumour progression. Eag channels in view of their restricted expression in normal tissue may emerge as novel tumour biomarkers

Ladybird Cobbitty 2017 Brassica Dataset

This data set contains weekly scans of cauliflower and broccoli covering a ten week growth cycle from transplant to harvest. The data set includes ground-truth, physical characteristics of the crop; environmental data collected by a weather station and a soil-senor network; and scans of the crop performed by an autonomous agricultural robot, which include stereo colour, thermal and hyperspectral imagery. The crop were planted at Lansdowne Farm, a University of Sydney agricultural research and teaching facility. Lansdowne Farm is located in Cobbitty, a suburb 70km south-west of Sydney in New South Wales (NSW), Australia. Four 80 metre raised crop beds were prepared with a North-South orientation. Approximately 144 Brassica were planted in each bed. Cauliflower were planted in the first and third bed (from west to east). Broccoli were planted in the second and fourth beds.2020-04-02 - Dataset Updated Update README.txt files for JFR reference. Include annotation and calibration files. Include example code. ChangeLog.txt: Changelog added. autonomous/annotations/: Added directory containing bounding-box image annotations of cauliflower and broccoli across all four beds and all ten weeks. autonomous/calibrations/: Added directory containing Ladybird calibration data. autonomous/example_code/: Added directory of Jupyter Notebooks (Python3) for loading and viewing Ladybird sensor data.2019-03-21 - Original uploa

Improved Cross-Ratio Invariant-Based Intrinsic Calibration of A Hyperspectral Line-Scan Camera

Hyperspectral line-scan cameras are increasingly being deployed on mobile platforms operating in unstructured environments. To generate geometrically accurate hyperspectral composites, the intrinsic parameters of these cameras must be resolved. This article describes a method for determining the intrinsic parameters of a hyperspectral line-scan camera. The proposed method is based on a cross-ratio invariant calibration routine and is able to estimate the focal length, principal point, and radial distortion parameters in a hyperspectral line-scan camera. Compared to previous methods that use similar calibration targets, our approach extends the camera model to include radial distortion. It is able to utilize calibration data recorded from multiple camera view angles by optimizing the re-projection error of all calibration data jointly. The proposed method also includes an additional signal processing step that automatically detects calibration points in hyperspectral imagery of the calibration target. These contributions result in accurate estimates of the intrinsic parameters with minimal supervision. The proposed method is validated through comprehensive simulation and demonstrated on real hyperspectral line-scans

Software-in-the-loop simulation as testing and visualisation framework for ITS algorithms

Testing intelligent transportation systems and their underlying algorithms in the field is a necessary step towards near market prototypes. Such tests, however, are always timeconsuming, costly and potentially cumbersome. It usually involves moving the prototype vehicle or vehicles to a certified test site, weather conditions need to be favourable, and the participating researchers need to be able to operate the vehicle. Running preliminary simulations in a realistic virtual environment can allow for faster prototyping of the underlying algorithms. Moreover, if recorded data from field trials can be replayed in simulation, the algorithms can be tested with realistic data as if they were being run on the actual physical systems. This paper presents the usage of a robotics simulator, MORSE, to address this issue. It also shows how real motion data from field experiments with autonomous vehicles is replayed in simulation, by combining it with virtual sensors and actuators. Additionally, it shows how a cooperative situation awareness application in which a set of intelligent vehicles, road side units and pedestrians on an intersection can be represented and pedestrian detection and motion flow estimation could be communicated.status: publishe