29 research outputs found
Sonar systems for object recognition
The deep sea exploration and exploitation is one of the biggest challenges
of the next century. Military, oil & gas, o shore wind farming,
underwater mining, oceanography are some of the actors interested
in this eld. The engineering and technical challenges to perform
any tasks underwater are great but the most crucial element in any
underwater systems has to be the sensors. In air numerous sensor
systems have been developed: optic cameras, laser scanner or radar
systems. Unfortunately electro magnetic waves propagate poorly in
water, therefore acoustic sensors are a much preferred tool then optical
ones. This thesis is dedicated to the study of the present and
the future of acoustic sensors for detection, identi cation or survey.
We will explore several sonar con gurations and designs and their
corresponding models for target scattering. We will show that object
echoes can contain essential information concerning its structure
and/or composition
Underwater robotics in the future of arctic oil and gas operations
Master's thesis in Petroleum engineeringArctic regions have lately been in the centre of increasing attention due to high vulnerability to climate change and the retreat in sea ice cover. Commercial actors are exploring the Arctic for new shipping routes and natural resources while scientific activity is being intensified to provide better understanding of the ecosystems. Marine surveys in the Arctic have traditionally been conducted from research vessels, requiring considerable resources and involving high risks where sea ice is present. Thus, development of low-cost methods for collecting data in extreme areas is of interest for both industrial purposes and environmental management.
The main objective of this thesis is to investigate the use of underwater vehicles as sensor platforms for oil and gas industry applications with focus on seabed mapping and monitoring. Theoretical background and a review of relevant previous studies are provided prior to presentation of the fieldwork, which took place in January 2017 in Kongsfjorden (Svalbard). The fieldwork was a part of the Underwater Robotics and Polar Night Biology course offered at the University Centre in Svalbard. Applied unmanned platforms included remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs) and an autonomous surface vehicle (ASV). They were equipped with such sensors as side-scan sonar, multi-beam echo sounder, camera and others. The acquired data was processed and used to provide information about the study area.
The carried out analysis of the vehicle performance gives an insight into challenges specific to marine surveys in the Arctic regions, especially during the period of polar night. The discussion is focused on the benefits of underwater robotics and integrated platform surveying in remote and harsh environment. Recommendations for further research and suggestions for application of similar vehicles and sensors are also given in the thesis
A New Coastal Crawler Prototype to Expand the Ecological Monitoring Radius of OBSEA Cabled Observatory
The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories perform such monitoring with fixed cameras, the ecological value of their data is limited to a narrow field of view, possibly not representative of the local habitat heterogeneity. Docked mobile robotic platforms could be used to extend data collection to larger, and hence more ecologically representative areas. Among the various state-of-the-art underwater robotic platforms available, benthic crawlers are excellent candidates to perform ecological monitoring tasks in combination with cabled observatories. Although they are normally used in the deep sea, their high positioning stability, low acoustic signature, and low energetic consumption, especially during stationary phases, make them suitable for coastal operations. In this paper, we present the integration of a benthic crawler into a coastal cabled observatory (OBSEA) to extend its monitoring radius and collect more ecologically representative data. The extension of the monitoring radius was obtained by remotely operating the crawler to enforce back-and-forth drives along specific transects while recording videos with the onboard cameras. The ecological relevance of the monitoring-radius extension was demonstrated by performing a visual census of the species observed with the crawler’s cameras in comparison to the observatory’s fixed cameras, revealing non-negligible differences. Additionally, the videos recorded from the crawler’s cameras during the transects were used to demonstrate an automated photo-mosaic of the seabed for the first time on this class of vehicles. In the present work, the crawler travelled in an area of 40 m away from the OBSEA, producing an extension of the monitoring field of view (FOV), and covering an area approximately 230 times larger than OBSEA’s camera. The analysis of the videos obtained from the crawler’s and the observatory’s cameras revealed differences in the species observed. Future implementation scenarios are also discussed in relation to mission autonomy to perform imaging across spatial heterogeneity gradients around the OBSEA
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring techn
The Norway lobster, Nephrops norvegicus, supports a key European fishery.
Stock assessments for this species are mostly based on trawling and
UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are
burrowing organisms and these survey methods are unable to sample or
observe individuals in their burrows. To account for this, UWTV surveys
generally assume that “1 burrow system = 1 animal”, due to the territorial
behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ
validation. Here, we outline how to improve the accuracy of current stock
assessments for N. norvegicus with novel ecological monitoring technologies,
including: robotic fixed and mobile camera-platforms, telemetry,
environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline
the present status and threat for overexploitation in N. norvegicus stocks. Then,
we discuss how the burrowing behavior of N. norvegicus biases current stock
assessment methods. We propose that state-of-the-art stationary and mobile
robotic platforms endowed with innovative sensors and complemented with AI
tools could be used to count both animals and burrows systems in-situ, as well
as to provide key insights into burrowing behavior. Next, we illustrate how
multiparametric monitoring can be incorporated into assessments of
physiology and burrowing behavior. Finally, we develop a flowchart for the
appropriate treatment of multiparametric biological and environmental data
required to improve current stock assessment methods
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies
The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that "1 burrow system = 1 animal", due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies
The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods
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Augmented Deep Learning Techniques for Robotic State Estimation
While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount of data these robots must utilize can quickly become intractable. Additional challenges include environmental noise, dynamic obstacles, and inherent sensor non-linearities. Deep learning techniques have emerged as a way to efficiently deal with these challenges. While end-to-end deep learning can be convenient, challenges such as validation and training requirements can be prohibitive to its use.
In order to address these issues, we propose augmenting the power of deep learning techniques with tools such as optimization methods, physics based models, and human expertise. In this work, we present a principled framework for approaching a problem that allows a user to identify the types of augmentation methods and deep learning techniques best suited to their problem. To validate our framework, we consider three different domains: LIDAR based odometry estimation, hybrid soft robotic control, and sonar based underwater mapping.
First, we investigate LIDAR based odometry estimation which can be characterized with both high data precision and availability; ideal for augmenting with optimization methods. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. Our proposed approach is comprised of two stages: a novel pre-processing stage for robust feature identification and a scan matching stage for motion estimation. Using real-world data from the University of Michigan North Campus long-term vision and LIDAR dataset (NCLT dataset) as well as the KITTI dataset, we show that our approach generalizes across domains; is capable of reducing the per-estimate error of standard ICP methods on average by 25.5% for the translational component and 57.53% for the rotational component; and is capable of reducing the computation time of state-of-the-art ICP methods by a factor of 7.94 on average while achieving competitive performance.
Next, we consider hybrid soft robotic control which has lower data precision due to real-world noise (e.g., friction and manufacturing imperfections). Here, augmenting with model based methods is more appropriate. We present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Our proposed approach is comprised of two stages: an LSTM calibration routine used to identify the current load state and a control input generation step that combines a generalized quasistatic model with the learned load model. We show our method is capable of classifying between different arm configurations at a rate greater than 95%. Additionally, our method is capable of reducing the end-effector error of quasistatic model only control to within 1 cm of our controller baseline.
Finally, we examine sonar based underwater mapping. Here, data is so noisy that augmenting with human experts and incorporating some global context is required. We develop a novel framework that enables the real-time 3D reconstruction of underwater environments using features from 2D sonar images. In our approach, a convolutional neural network (CNN) analyzes sonar imagery in real-time and only proposes a small subset of high-quality frames to the human expert for feature annotation. We demonstrate that our approach provides real-time reconstruction capability without loss in classification performance on datasets captured onboard our underwater vehicle while operating in a variety of environments
Robot Navigation in Distorted Magnetic Fields
This thesis investigates the utilization of magnetic field distortions for the localization and navigation of robotic systems. The work comprehensively illuminates the various aspects that are relevant in this context. Among other things, the characteristics of magnetic field environments are assessed and examined for their usability for robot navigation in various typical mobile robot deployment scenarios. A strong focus of this work lies in the self-induced static and dynamic magnetic field distortions of complex kinematic robots, which could hinder the use of magnetic fields because of their interference with the ambient magnetic field. In addition to the examination of typical distortions in robots of different classes, solutions for compensation and concrete tools are developed both in hardware (distributed magnetometer sensor systems) and in software. In this context, machine learning approaches for learning static and dynamic system distortions are explored and contrasted with classical methods for calibrating magnetic field sensors. In order to extend probabilistic state estimation methods towards the localization in magnetic fields, a measurement model based on Mises-Fisher distributions is developed in this thesis. Finally, the approaches of this work are evaluated in practice inside and outside the laboratory in different environments and domains (e.g. office, subsea, desert, etc.) with different types of robot systems