2,294 research outputs found
Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms.
This cumulative thesis introduces a novel field for service robotics: the inspection of marine vessels using mobile inspection robots. In this thesis, three scientific contributions are provided and experimentally verified in the field of marine inspection, but are not limited to this type of application. The inspection scenario is merely a golden thread to combine the cumulative scientific results presented in this thesis. The first contribution is an adaptive, proprioceptive control approach for hybrid leg-wheel robots, such as the robot ASGUARD described in this thesis. The robot is able to deal with rough terrain and stairs, due to the control concept introduced in this thesis. The proposed system is a suitable platform to move inside the cargo holds of bulk carriers and to deliver visual data from inside the hold. Additionally, the proposed system also has stair climbing abilities, allowing the system to move between different decks. The robot adapts its gait pattern dynamically based on proprioceptive data received from the joint motors and based on the pitch and tilt angle of the robot's body during locomotion. The second major contribution of the thesis is an independent ship inspection system, consisting of a magnetic wall climbing robot for bulkhead inspection, a particle filter based localization method, and a spatial content management system (SCMS) for spatial inspection data representation and organization. The system described in this work was evaluated in several laboratory experiments and field trials on two different marine vessels in close collaboration with ship surveyors. The third scientific contribution of the thesis is a novel approach to structural classification using semantic perception approaches. By these methods, a structured environment can be semantically annotated, based on the spatial relationships between spatial entities and spatial features. This method was verified in the domain of indoor perception (logistics and household environment), for soil sample classification, and for the classification of the structural parts of a marine vessel. The proposed method allows the description of the structural parts of a cargo hold in order to localize the inspection robot or any detected damage. The algorithms proposed in this thesis are based on unorganized 3D point clouds, generated by a LIDAR within a ship's cargo hold. Two different semantic perception methods are proposed in this thesis. One approach is based on probabilistic constraint networks; the second approach is based on Fuzzy Description Logic and spatial reasoning using a spatial ontology about the environment
Seabed Surveillance and Underwater Structures Inspection with Remotely Operated Vehicle − Power Ray
The marine ecosystem is necessary to be monitored as it is exposed to externalities and pollutants that affect biodiversity and the state of the underwater structures. There is a demand for a better, more dynamic, and safe monitoring approach to underwater research and inspection. The unmanned underwater vehicles are becoming a reachable and intuitive tool for underwater inspection, such as for the inspection of the marine hull of vessels, bridges, foundations, piers, pylons, and other support structures in ports. The main advantage of the use of the remotely operated underwater drone is cost and time-efficiency, as they allow to obtain information in a fast and safe way in real-time. In this paper we investigate the possibility of the use of a remotely operated underwater drone Power Ray for seabed observation and underwater structures inspection. It describes the re-sults of the field research collected from the use of low-cost underwater drone Power Ray. The data collected with an underwater drone presents footages of different underwater structures and areas in order to document the seabed state and underwater structures. Additionally, this article provides an overview of the problems in underwater inspection and monitoring, and possibilities offered by remotely operated vehicle Power Ray in solv-ing them. The results of the paper are not unique to working with a low-cost drone, but are illustrative of the challenges and problems that new users are likely to encounter when using this technology
Intervention AUVs: The Next Challenge
While commercially available AUVs are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities. The maintenance of: permanent underwater observatories, submerged oil wells, cabled sensor networks, pipes and the deployment and recovery of benthic stations are a few of them. These tasks are addressed nowadays using manned submersibles or work-class ROVs, equipped with teleoperated arms under human supervision. Although researchers have recently opened the door to future I-AUVs, a long path is still necessary to achieve autonomous underwater interventions. This paper reviews the evolution timeline in autonomous underwater intervention systems. Milestone projects in the state of the art are reviewed, highlighting their principal contributions to the field. To the best of the authors knowledge, only three vehicles have demonstrated some autonomous intervention capabilities so far: ALIVE, SAUVIM and GIRONA 500, being the last one the lightest one. In this paper GIRONA 500 I-AUV is presented and its software architecture discussed. Recent results in different scenarios are reported: 1) Valve turning and connector plugging/unplugging while docked to a subsea panel, 2) Free floating valve turning using learning by demonstration, and 3) Multipurpose free-floating object recovery. The paper ends discussing the lessons learned so far
Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions
Visual perception is an important component for autonomous navigation of
unmanned surface vessels (USV), particularly for the tasks related to
autonomous inspection and tracking. These tasks involve vision-based navigation
techniques to identify the target for navigation. Reduced visibility under
extreme weather conditions in marine environments makes it difficult for
vision-based approaches to work properly. To overcome these issues, this paper
presents an autonomous vision-based navigation framework for tracking target
objects in extreme marine conditions. The proposed framework consists of an
integrated perception pipeline that uses a generative adversarial network (GAN)
to remove noise and highlight the object features before passing them to the
object detector (i.e., YOLOv5). The detected visual features are then used by
the USV to track the target. The proposed framework has been thoroughly tested
in simulation under extremely reduced visibility due to sandstorms and fog. The
results are compared with state-of-the-art de-hazing methods across the
benchmarked MBZIRC simulation dataset, on which the proposed scheme has
outperformed the existing methods across various metrics.Comment: IEEE/RSJ International Conference on Intelligent Robots (IROS-2023
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
Advancing Climate Change Research and Hydrocarbon Leak Detection : by Combining Dissolved Carbon Dioxide and Methane Measurements with ADCP Data
With the emergence of largescale, comprehensive environmental monitoring projects, there is an increased need to combine state-of-the art technologies to address complicated problems such as ocean acidifi cation and hydrocarbon leak
detection
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