Next generation mine countermeasures for the very shallow water zone in support of amphibious operations

This report describes system engineering efforts exploring next generation mine countermeasure (MCM) systems to satisfy high priority capability gaps in the Very Shallow Water (VSW) zone in support of amphibious operations. A thorough exploration of the problem space was conducted, including stakeholder analysis, MCM threat analysis, and current and future MCM capability research. Solution-neutral requirements and functions were developed for a bounded next generation system. Several alternative architecture solutions were developed that included a critical evaluation that compared performance and cost. The resulting MCM system effectively removes the man from the minefield through employment of autonomous capability, reduces operator burden with sensor data fusion and processing, and provides a real-time communication for command and control (C2) support to reduce or eliminate post mission analysis.http://archive.org/details/nextgenerationmi109456968N

Deep neural networks for marine debris detection in sonar images

Garbage and waste disposal is one of the biggest challenges currently faced by mankind. Proper waste disposal and recycling is a must in any sustainable community, and in many coastal areas there is significant water pollution in the form of floating or submerged garbage. This is called marine debris. It is estimated that 6.4 million tonnes of marine debris enter water environments every year [McIlgorm et al. 2008, APEC Marine Resource Conservation WG], with 8 million items entering each day. An unknown fraction of this sinks to the bottom of water bodies. Submerged marine debris threatens marine life, and for shallow coastal areas, it can also threaten fishing vessels [Iñiguez et al. 2016, Renewable and Sustainable Energy Reviews]. Submerged marine debris typically stays in the environment for a long time (20+ years), and consists of materials that can be recycled, such as metals, plastics, glass, etc. Many of these items should not be disposed in water bodies as this has a negative effect in the environment and human health. Encouraged by the advances in Computer Vision from the use Deep Learning, we propose the use of Deep Neural Networks (DNNs) to survey and detect marine debris in the bottom of water bodies (seafloor, lake and river beds) from Forward-Looking Sonar (FLS) images. This thesis performs a comprehensive evaluation on the use of DNNs for the problem of marine debris detection in FLS images, as well as related problems such as image classification, matching, and detection proposals. We do this in a dataset of 2069 FLS images that we captured with an ARIS Explorer 3000 sensor on marine debris objects lying in the floor of a small water tank. We had issues with the sensor in a real world underwater environment that motivated the use of a water tank. The objects we used to produce this dataset contain typical household marine debris and distractor marine objects (tires, hooks, valves, etc), divided in 10 classes plus a background class. Our results show that for the evaluated tasks, DNNs area superior technique than the corresponding state of the art. There are large gains particularly for the matching and detection proposal tasks. We also study the effect of sample complexity and object size in many tasks, which is valuable information for practitioners. We expect that our results will advance the objective of using Autonomous Underwater Vehicles to automatically survey, detect and collect marine debris from underwater environments

Sensor-driven online coverage planning for autonomous underwater vehicles

Abstract-At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally pre-planned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependant on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method. Index Terms-autonomous underwater vehicles, coverage path planning, information gain, hardware-in-the-loop, mine countermeasure, sidescan sonar, adaptive mission plannin

The design and implementation of a multi-agent architecture to increase coordination efficiency in multi-AUV operations

This research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An intelligent mission executive has been created that uses multi-agent technology to control and coordinate multiple AUVs in communication deficient environments. By incorporating real time vehicle prediction, blackboardbased hierarchical mission plans and mission optimisation in conjunction with a simple broadcast communication system this system aims to handle the limitations inherent in underwater operations and intelligently control multiple vehicles. In this research efficiency is evaluated and then compared to the current state of the art in multiple AUV control. The research is then validated in real AUV coordination trials. Results will show that compared to the state of the art the control system developed and implemented in this research coordinates multiple vehicles more efficiently and is able to function in a range of poor communication environments. These findings are supported by in water validation trials with heterogeneous AUVs. This thesis will first present an in depth state of the art of the related research topics including multi-agent systems, collaborative robotics and autonomous underwater vehicles. The development and functionality of this research will then be explained followed by a detailed description of the experiments. Results are then presented both for the simulated and real world trials followed by a discussion of the findings

Service-oriented agent architecture for autonomous maritime vehicles

Advanced ocean systems are increasing their capabilities and the degree of autonomy more and more in order to perform more sophisticated maritime missions. Remotely operated vehicles are no longer cost-effective since they are limited by economic support costs, and the presence and skills of the human operator. Alternatively, autonomous surface and underwater vehicles have the potential to operate with greatly reduced overhead costs and level of operator intervention. This Thesis proposes an Intelligent Control Architecture (ICA) to enable multiple collaborating marine vehicles to autonomously carry out underwater intervention missions. The ICA is generic in nature but aimed at a case study where a marine surface craft and an underwater vehicle are required to work cooperatively. They are capable of cooperating autonomously towards the execution of complex activities since they have different but complementary capabilities. The architectural foundation to achieve the ICA lays on the flexibility of service-oriented computing and agent technology. An ontological database captures the operator skills, platform capabilities and, changes in the environment. The information captured, stored as knowledge, enables reasoning agents to plan missions based on the current situation. The ICA implementation is verified in simulation, and validated in trials by means of a team of autonomous marine robots. This Thesis also presents architectural details and evaluation scenarios of the ICA, results of simulations and trials from different maritime operations, and future research directions

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Umgebungskartenschätzung aus Sidescan-Sonardaten für ein autonomes Unterwasserfahrzeug

Für die Schätzung der Höhenkarten aus Sidescan-Sonardaten liefert die Arbeit mehrere Beiträge: Ein neues Schätzverfahren, das Sonarmessungen aus vorberechneten Sonarantworten von Basiselementen, sog. Kerneln, zusammensetzt und so zu einer Höhenschätzung gelangt. Des Weiteren ein dreidimensionales Verfahren, das auf Markov Random Fields basiert und eine Sidescan-Sonarsimulationsumgebung für beliebige dreidimensionale Szenen, die auch verschiedene Sonaraufnahmemodi und Terraingeneratoren bietet