19 research outputs found

    Localization, Mapping and SLAM in Marine and Underwater Environments

    Get PDF
    The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

    Mobile Robots Navigation

    Get PDF
    Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

    Underwater Vehicles

    Get PDF
    For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

    Development and Analysis of Physics-based Models for Autonomous Underwater Vehicle Navigation and the Reconstruction of Underwater Images

    Get PDF
    Autonome Unterwasserfahrzeuge (AUVs) haben die Art wie die Meeresumwelt untersucht, ĂŒberwacht und kartographiert wird verĂ€ndert. Sie bieten eine breite Palette von Anwendungen in der Forschung, beim MilitĂ€r und in kommerziellen ZusammenhĂ€ngen. AUVs sollen nicht nur eine bestimmte Aufgabe erfĂŒllen, sondern sich auch an VerĂ€nderungen in der Umgebung anpassen. Typische EinflĂŒsse sind plötzliche Seitenströmungen, Fallströme und andere Effekte, welche extrem unberechenbar sind. Simultane Lokalisierung und Kartenerstellung (SLAM) ist ein bekanntes und gut verstandenes Problem in der Robotik. FĂŒr landgestĂŒtzte Roboter in 2D-Umgebungen wird dieses Problem im Allgemeinen als gelöst angesehen. SLAM-Algorithmen fĂŒr diese neigen dazu sich auf die optische Erkennung in Kombination mit Koppelnavigation und TrĂ€gheitsmesseinheiten zu verlassen. Die optischen Eigenschaften des Wassers und insbesondere Meerwassers verhindern die Nutzung etablierter optischer Erkennungsalgorithmen. Bilder in hoher QualitĂ€t mit der richtigen Farbgebung erleichtern die Erkennung von Objekten unter Wasser und können die Verwendung der fĂŒr landgestĂŒtzte Roboter entwickelten visuelle SLAM-Algorithmen unter Wasser ermöglichen. Daher ist geeignete Bildverarbeitung vor allem im tiefen Wasser erforderlich. In dieser Arbeit werden physikbasierte Modelle fĂŒr die Navigation autonomer Unterwasserfahrzeuge entwickelt mit einem Schwerpunkt auf schnellen Forschungs-AUVs mit Reisegeschwindigkeiten im Bereich von 5 kn bis 20 kn. Das System sollte fĂ€hig sein Störungen im Wasserfluss zu erkennen und in der Lage sein eine Kamera zur Objekterkennung, Bodenuntersuchung und vor allem fĂŒr Navigationszwecke zu verwenden. Des Weiteren sollte es möglich sein, das System in bestehende autonome Unterwasserfahrzeuge zu integrieren. Daher muss das System klein und leicht sein, so dass die Nutzlast des AUV nicht wesentlich reduziert wird. Die erforderliche Rechenleistung und der Leistungsverbrauch mĂŒssen ebenfalls klein sein, so dass die Einsatzdauer des Fahrzeugs nicht stark verringert wird. Die Algorithmen sollten außerdem schnell sein, um SLAM-Anwendung zu ermöglichen. Im ersten Teil der Arbeit wird die Anwendbarkeit verschiedener Lernverfahren zur Bestimmung der Strömungsparameter eines umgebenden Fluids mit Hilfe des Drucks auf einen AUV-Körper anhand zahlreicher numerischer Strömungssimulationen (CFD) und unter Verwendung von Druckdaten von festgelegten Punkten auf der OberflĂ€che des AUV getestet. Es wird gezeigt, dass eine Kombination von Support Vector Machines (SVM) eine ausgezeichnete Wahl ist, um diese Aufgabe auszufĂŒhren. Mit den Ergebnissen aus den Simulationen wird dann die Lage der Druckmessstellen optimiert, so dass die höchsten DruckĂ€nderungen aufgrund der Fließgeschwindigkeiten erfasst werden. Dies reduziert auch die Anzahl von Messpunkten. Es wird dann gezeigt, dass auch fĂŒr die optimierte Konfiguration Support Vector Machines die beste Wahl fĂŒr die gestellte Aufgabe sind. Jedoch sind in diesem Fall weniger Maschinen erforderlich. Im zweiten Teil der Arbeit werden verschiedene Lernmethoden fĂŒr die Rekonstruktion von Unterwasserbildern angewandt. Zuerst werden Labortests unter Verwendung einer speziellen Lichtquelle, welche die LichtverhĂ€ltnisse unter Wasser imitieren, durchgefĂŒhrt. Es wird gezeigt, dass eine Kombination aus der k-nĂ€chste-Nachbarn-Methode und Support Vector Machines hervorragende Ergebnisse liefert. Basierend auf diesen Ergebnissen wird eine experimentelle Verifikation unter erschwerten Bedingungen im trĂŒben Wasser eines Tauchbeckens durchgefĂŒhrt. Es wird gezeigt, dass die k-nĂ€chste-Nachbarn-Methode sehr gute Ergebnisse fĂŒr kleine AbstĂ€nde zwischen dem Objekt und der Kamera und fĂŒr kleine Wassertiefen im roten Kanal liefert. FĂŒr höhere Distanzen, Wassertiefen und fĂŒr die anderen FarbkanĂ€le ist eine Kombination von Support Vector Machines die beste Wahl fĂŒr die Rekonstruktion der Farbe, wie sie unter weißem Licht zu sehen sind, aus den Unterwasserbildern. Somit wird in dieser Arbeit ein neuer Ansatz zur Navigation autonomer Unterwasserfahrzeug und der Rekonstruktion von Unterwasserbildern vorgeschlagen und entwickelt.Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. They have a wide range of applications in research, military, and commercial settings. AUVs should not only perform a given task but also adapt to changes in the environment. Typical effects are sudden side currents, downdrafts, and other effects which are extremely unpredictable. Simultaneous localisation and mapping (SLAM) is a well-known and well-understood problem in robotics. For land-based robots in 2-D environments this problem is generally considered to be solved. SLAM algorithms for these tend to rely on optical recognition in combination with dead reckoning and inertial measurement units. The optical properties of water and especially seawater prevent the use of established optical recognition algorithms. High quality images with correct colouring simplify the detection of underwater objects and may allow the use of visual SLAM algorithms developed for land-based robots underwater. Hence, appropriate image processing is required especially in deep water. In this thesis physics-based models for autonomous underwater vehicle navigation are developed with an emphasis on fast exploratory AUVs with cruising speeds in the range of 5 kn to 20 kn. The system should be capable of detecting disturbances in the water flow and be able to use a camera for object detection, ground survey, and especially for navigational purposes. Furthermore, it should be possible to integrate the system into existing autonomous underwater vehicles. Therefore, the system must be small and lightweight such that the payload of the AUV is not reduced significantly. The required computational power and the power consumption must also be small such that the duration of the vehicle does not decrease strongly. The algorithms should also be fast to allow SLAM application. In the first part of the thesis the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested based on numerous computational fluid dynamical (CFD) simulations and using pressure data from specified points on the surface of the AUV. It is shown that a combination of support vector machines (SVM) is an excellent choice to perform this task. With the findings from the simulations the position of pressure measurement points is then optimised such that the most significant pressure changes due to changing flow velocities can be captured. This also reduces the number of measurement points. It is then shown that also for the optimised setup support vector machines are the best choices for the given task. However, fewer machines are required in this case. In the second part of the thesis different learning methods are applied for the reconstruction of underwater images. First laboratory tests are performed using a special light source imitating underwater lighting conditions. It is shown that a combination of the k-nearest neighbour method and support vector machines yields excellent results. Based on these results an experimental verification is performed under severe conditions in murky water of a diving basin. It is shown that the k-nearest neighbour method gives very good results for small distances between the object and the camera and for small water depths in the red channel. For higher distances, water depths, and for the other colour channels a combination of support vector machines is the best choice for the reconstruction of the colour as seen under white light from the underwater images. Thus, a novel approach to autonomous underwater vehicle navigation and the reconstruction of underwater images is proposed and developed in this thesis

    Mid-water current aided localization for autonomous underwater vehicles

    Get PDF
    Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Autonomous Robots 40 (2016): 1207–1227, doi:10.1007/s10514-016-9547-3.Survey-class Autonomous Underwater Vehi- cles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid- water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Cur- rent Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method dis- cussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Map- ping (SLAM) and on descents up to 3km deep with the Sentry AUV.This work is supported in part by NCRIS IMOS, the Australian Research Council (ARC), the New South Wales Government and the Woods Hole Oceanographic Institution.2017-02-1

    Plenoptic Signal Processing for Robust Vision in Field Robotics

    Get PDF
    This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

    Developing a Holonomic iROV as a Tool for Kelp Bed Mapping

    Get PDF
    corecore