Autonomous, Collaborative, Unmanned Aerial Vehicles for Search and Rescue

Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue

Who wins when the competition heats up? Effects of climate change on interactions among three Antarctic penguin species

This thesis sought to elucidate the mechanisms driving the large-scale population changes observed in Pygoscelis penguins in the Western Antarctic Peninsula (WAP)/Scotia Sea region since the 1970s, with particular focus on the interactions between the species. During this period the climate in this region has changed dramatically, with rapid warming and sea ice declines occurring until the late 20th century to be followed by a pause in the warming. These changes have altered biotic and abiotic conditions in the penguins’ ecosystem and researchers widely agree that this is driving their population changes. In order to elucidate the exact mechanisms of population change, we attempted to fill crucial knowledge gaps, including foraging ecology, migration and breeding success, throughout their annual cycle and all with particular focus on the interactions between the three Pygoscelis species. Direct tracking and isotope analysis provided novel insights into foraging behaviour and the role of niche partitioning between the species throughout the annual cycle, and its importance for reducing interspecific competition. During the breeding season, allochrony between Adélie and chinstrap penguins was found to reduce competitive overlap in foraging areas by 54%, compared to synchronous breeding, and to be resilient to climate change. The migration routes and over-winter sites of chinstrap penguins from the South Orkney Islands were identified for the first time and were found to be segregated from birds from the neighbouring South Shetland Islands archipelago. The environmental conditions at the two over-winter sites differed but the population trends at the two archipelagos were similar, suggesting that winter conditions are not likely to be a major driver. Developing on our findings of contrasting environmental conditions across the chinstrap over-wintering sites, we investigated the effect of multiple environmental variables on population trends in the final two thesis chapters. Sea ice has been shown to be a major driver of Adélie penguin breeding success, and thereby population trends, and birds in our study region experience particularly dramatic seasonal changes in sea ice concentration (SIC), as it is located near the northern extent of winter ice. The three Pygoscelis species are widely cited as having different ice tolerances, termed the ‘sea ice hypothesis’, with Adélies being described as ‘ice-loving’, chinstraps as ‘ice tolerant’ and gentoos as ‘ice averse’. These differing ice tolerances are thought to be a major factor in the species’ contrasting population changes in this region and these hypothesised preferences could theoretically induce a sea ice optima for breeding and forging success. However, no evidence was found for a sea ice optima at the study colony, despite previous studies finding a 20% optima for Adélies in East Antarctica, and SIC was found to have no significant effect on breeding productivity or diet composition but some effect was found for fledging mass and foraging trip duration. The combined influence of environmental conditions and interspecific interactions on the three species’ population trends was investigated for the first time in this system. Data from large and local scale climate and a long time period (more than 25 years) were investigated at the two study archipelagos using a multi-species Gompertz population model. The model failed to identify any of the modelled variables as major drivers of the population variation, suggesting that other factors, such as predation and prey availability were potentially important drivers. This thesis also identified a number of priorities for future research and identified the need for a greater emphasis on modelling the effects of Antarctic krill biomass, rather than climate variables, upon penguin demographic variables

MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications

Learning spatiotemporal patterns for monitoring smart cities and infrastructure

Recent advances in the Internet of Things (IoT) have changed the way we interact with the world. The ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision making to new realms of city infrastructure and management. Large volumes of spatiotemporal data have been collected from pervasive sensors in both indoor and outdoor environments, and this data reveals dynamic patterns in cities, infrastructure, and public property. In light of the need for new approaches to analysing such data, in this thesis, we propose present relevant data mining techniques and machine learning approaches to extract knowledge from spatiotemporal data to solve real-world problems. Many challenges and problems are under-addressed in smart cities and infrastructure monitoring systems such as indoor person identification, evaluation of city regions segmentation with parking events, fine collection from cars in violations, parking occupancy prediction and airport aircraft path map reconstruction. All the above problems are associated with both spatial and temporal information and the accurate pattern recognition of these spatiotemporal data are essential for determining problem solutions. Therefore, how to incorporate spatiotemporal data mining techniques, artificial intelligence approaches and expert knowledge in each specific domain is a common challenge. In the indoor person identification area, identifying the person accessing a secured room without vision-based or device-based systems is very challenging. In particular, to distinguish time-series patterns on high-dimensional wireless signal channels caused by different activities and people, requires novel time-series data mining approaches. To solve this important problem, we established a device-free system and proposed a two-step solution to identify a person who has accessed a secure area such as an office. Establishing smart parking systems in cities is a key component of smart cities and infrastructure construction. Many sub-problems such as parking space arrangements, fine collection and parking occupancy prediction are urgent and important for city managers. Arranging parking spaces based on historical data can improve the utilisation rate of parking spaces. To arrange parking spaces based on collected spatiotemporal data requires reasonable region segmentation approaches. Moreover, evaluating parking space grouping results needs to consider the correlation between the spatial and temporal domains since these are heterogeneous. Therefore, we have designed a spatiotemporal data clustering evaluation approach, which exploits the correlation between the spatial domain and the temporal domain. It can evaluate the segmentation results of parking spaces in cities using historical data and similar clustering results that group data consisting of both spatial and temporal domains. For fine collection problem, using the sensor instrumentation installed in parking spaces to detect cars in violation and issue infringement notices in a short time-window to catch these cars in time is significantly difficult. This is because most cars in violation leave within a short period and multiple cars are in violation at the same time. Parking officers need to choose the best route to collect fines from these drivers in the shortest time. Therefore, we proposed a new optimisation problem called the Travelling Officer Problem and a general probability-based model. We succeeded in integrating temporal information and the traditional optimisation algorithm. This model can suggest to parking officers an optimised path that maximise the probability to catch the cars in violation in time. To solve this problem in real-time, we incorporated the model with deep learning methods. We proposed a theoretical approach to solving the traditional orienteering problem with deep learning networks. This approach could improve the efficiency of similar urban computing problems as well. For parking occupancy prediction, a key problem in parking space management is with providing a car parking availability prediction service that can inform car drivers of vacant parking lots before they start their journeys using prediction approaches. We proposed a deep learning-based model to solve this parking occupancy prediction problem using spatiotemporal data analysis techniques. This model can be generalised to other spatiotemporal data prediction problems also. In the airport aircraft management area, grouping similar spatiotemporal data is widely used in the real world. Determining key features and combining similar data are two key problems in this area. We presented a new framework to group similar spatiotemporal data and construct a road graph with GPS data. We evaluated our framework experimentally using a state-of-the-art test-bed technique and found that it could effectively and efficiently construct and update airport aircraft route map. In conclusion, the studies in this thesis aimed to discover intrinsic and dynamic patterns from spatiotemporal data and proposed corresponding solutions for real-world smart cities and infrastructures monitoring problems via spatiotemporal pattern analysis and machine learning approaches. We hope this research will inspire the research community to develop more robust and effective approaches to solve existing problems in this area in the future

Personal Wayfinding Assistance

We are traveling many different routes every day. In familiar environments it is easy for us to find our ways. We know our way from bedroom to kitchen, from home to work, from parking place to office, and back home at the end of the working day. We have learned these routes in the past and are now able to find our destination without having to think about it. As soon as we want to find a place beyond the demarcations of our mental map, we need help. In some cases we ask our friends to explain us the way, in other cases we use a map to find out about the place. Mobile phones are increasingly equipped with wayfinding assistance. These devices are usually at hand because they are handy and small, which enables us to get wayfinding assistance everywhere where we need it. While the small size of mobile phones makes them handy, it is a disadvantage for displaying maps. Geographic information requires space to be visualized in order to be understandable. Typically, not all information displayed in maps is necessary. An example are walking ways in parks for car drivers, they are they are usually no relevant route options. By not displaying irrelevant information, it is possible to compress the map without losing important information. To reduce information purposefully, we need information about the user, the task at hand, and the environment it is embedded in. In this cumulative dissertation, I describe an approach that utilizes the prior knowledge of the user to adapt maps to the to the limited display options of mobile devices with small displays. I focus on central questions that occur during wayfinding and relate them to the knowledge of the user. This enables the generation of personal and context-specific wayfinding assistance in the form of maps which are optimized for small displays. To achieve personalized assistance, I present algorithmic methods to derive spatial user profiles from trajectory data. The individual profiles contain information about the places users regularly visit, as well as the traveled routes between them. By means of these profiles it is possible to generate personalized maps for partially familiar environments. Only the unfamiliar parts of the environment are presented in detail, the familiar parts are highly simplified. This bears great potential to minimize the maps, while at the same time preserving the understandability by including personally meaningful places as references. To ensure the understandability of personalized maps, we have to make sure that the names of the places are adapted to users. In this thesis, we study the naming of places and analyze the potential to automatically select and generate place names. However, personalized maps only work for environments the users are partially familiar with. If users need assistance for unfamiliar environments, they require complete information. In this thesis, I further present approaches to support uses in typical situations which can occur during wayfinding. I present solutions to communicate context information and survey knowledge along the route, as well as methods to support self-localization in case orientation is lost

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Dyadic Route Planning and Navigation in Collaborative Wayfinding

The great majority of work in spatial cognition has taken an individual approach to the study of wayfinding, isolating the planning and decision-making process of a single navigating entity. The study we present here expands our understanding of human navigation as it unfolds in a social context, common to real-world scenarios. We investigate pedestrian navigation by pairs of people (dyads) in an unfamiliar, real-world environment. Participants collaborated on a task to plan and enact a route between a given origin and destination. Each dyad had to devise and agree upon a route to take using a paper map of the environment, and was then taken to the environment and asked to navigate to the destination from memory alone. We video-recorded and tracked the dyad as they interacted during both planning and navigation. Our results examine explanations for successful route planning and sources of uncertainty in navigation. This includes differences between situated and prospective planning - participants often modify their route-following on the fly based on unexpected challenges. We also investigate strategies of social role-taking (leading and following) within dyads

Visual Odometry and Mapping in Natural Environments for Arbitrary Camera Motion Models

This is a thesis on outdoor monocular visual SLAM in natural environments. The techniques proposed herein aim at estimating camera pose and 3D geometrical structure of the surrounding environment. This problem statement was motivated by the GPS-denied scenario for a sea-surface vehicle developed at Plymouth University named Springer. The algorithms proposed in this thesis are mainly adapted for the Springer’s environmental conditions, so that the vehicle can navigate on a vision based localization system when GPS is not available; such environments include estuarine areas, forests and the occasional semi-urban territories. The research objectives are constrained versions of the ever-abiding problems in the fields of multiple view geometry and mobile robotics. The research is proposing new techniques or improving existing ones for problems such as scene reconstruction, relative camera pose recovery and filtering, always in the context of the aforementioned landscapes (i.e., rivers, forests, etc.). Although visual tracking is paramount for the generation of data point correspondences, this thesis focuses primarily on the geometric aspect of the problem as well as with the probabilistic framework in which the optimization of pose and structure estimates takes place. Besides algorithms, the deliverables of this research should include the respective implementations and test data for these algorithms in the form of a software library and a dataset containing footage of estuarine regions taken from a boat, along with synchronized sensor logs. This thesis is not the final analysis on vision based navigation. It merely proposes various solutions for the localization problem of a vehicle navigating in natural environments either on land or on the surface of the water. Although these solutions can be used to provide position and orientation estimates when GPS is not available, they have limitations and there is still a vast new world of ideas to be explored.UTC Aerospace System

Fin whales of the Great Bear Rainforest : Balaenoptera physalus velifera in a Canadian Pacific fjord system

Funding: This research was supported by a Mitacs Accelerate Internship (IT21479); the Save Our Seas Foundation; Willow Grove Foundation; Donner Canadian Foundation; Tides Canada; LUSH Charity Pot; private donations to North Coast Cetacean Society; Fisheries and Oceans Canada; and the Canada Nature Fund for Aquatic Species at Risk (CANAFSAR 2019-2021).Fin whales (Balaenoptera physalus) are widely considered an offshore and oceanic species, but certain populations also use coastal areas and semi-enclosed seas. Based upon fifteen years of study, we report that Canadian Pacific fin whales (B. p. velifera) have returned to the Kitimat Fjord System (KFS) in the Great Bear Rainforest, and have established a seasonally resident population in its intracoastal waters. This is the only fjord system along this coast or elsewhere in which fin whales are known to occur regularly with strong site fidelity. The KFS was also the only Canadian Pacific fjord system in which fin whales were commonly found and killed during commercial whaling, pointing to its long-term importance. Traditional knowledge, whaling records, and citizen science databases suggest that fin whales were extirpated from this area prior to their return in 2005-2006. Visual surveys and mark-recapture analysis documented their repopulation of the area, with 100-120 whales using the fjord system in recent years, as well as the establishment of a seasonally resident population with annual return rates higher than 70%. Line transect surveys identified the central and outer channels of the KFS as the primary fin whale habitat, with the greatest densities occurring in Squally Channel and Caamano Sound. Fin whales were observed in the KFS in most months of the year. Vessel- and shore-based surveys (27,311 km and 6,572 hours of effort, respectively) indicated regular fin whale presence (2,542 detections), including mother-calf pairs, from June to October and peak abundance in late August-early September. Seasonal patterns were variable year-to-year, and several lines of evidence indicated that fin whales arrived and departed from the KFS repeatedly throughout the summer and fall. Additionally, we report on the population's social network and morphometrics. These findings offer insights into the dynamics of population recovery in an area where several marine shipping projects are proposed. The fin whales of the Great Bear Rainforest represent a rare exception to general patterns in this species' natural history, and we highlight the importance of their conservation.Publisher PDFPeer reviewe