Orientation estimation is a fundamental aspect of navigation and motion control of Autonomous Underwater Vehicles (AUVs). This concept is especially true when position sensors are unavailable and, consequently, navigation and control rely on dead reckoning strategies; in this case, orientation estimation is used in conjunction with speed measurements to update position estimation. When unknown magnetic disturbances are present, the magnetometers of the Inertial Measurement Unit (IMU) are unusable and do not provide an accurate initialization of the vehicle heading angle. This issue can be faced by applying a generalization of the Extended Kalman Filter in which the system state and measurements evolve on matrix Lie groups. The filter is used when the AUV is moving on the sea surface and it provides an estimate of the heading offset by comparing the speed measurements acquired by the Global Positioning System (GPS) and the Doppler Velocity Log (DVL) and by fusing the data coming from the IMU and the Fiber Optic Gyroscope (FOG). The initialization procedure has been validated with a dataset acquired by FeelHippo AUV in Cecina, Italy (September 2021).
A software framework, “ros_acomms,” has been developed to enable transport of ROS messages and other data across low-throughput and high-latency underwater acoustic links. Messages are efficiently marshalled using user-provided configuration data, if available, or automatically via message introspection. A modular set of modem drivers, media-access-control engines, and message queues transport messages from one system to another via a modem. It supports message fragmentation, positive acknowledgment, and custody-transfer routing. It also includes an acoustic link simulator that uses a raytracing model to estimate link performance and latency. While it targets the WHOI Micromodem family of acoustic modems, the modular modem driver implementation has been leveraged to support low-throughput Iridium satellite links and other acoustic modems. It has been tested and used operationally at sea for remote redirection of autonomous underwater vehicles while providing operators with near real time vehicle telemetry and sensor data.
Recent advances in differentiable rendering, which allow calculating the gradients of 2D pixel values with respect to 3D object models, can be applied to estimation of the model parameters by gradient-based optimization with only 2D supervision. It is easy to incorporate deep neural networks into such an optimization pipeline, allowing the leveraging of deep learning techniques. This also largely reduces the requirement for collecting and annotating 3D data, which is very difficult for applications, for example when constructing geometry from 2D sensors. In this work, we propose a differentiable renderer for sidescan sonar imagery. We further demonstrate its ability to solve the inverse problem of directly reconstructing a 3D seafloor mesh from only 2D sidescan sonar data.
Oceans preservation and protection have become increasingly relevant topics to tackle climate change. To this end, Autonomous Underwater Vehicles (AUVs) provide a useful means to carry out inspection and monitoring operations in full autonomy. A particular scenario in which AUVs are crucial involves the detection and mapping of underwater gas leaks, whether these are due to damaged offshore structures or naturally released from the seafloor. In this context, the proposed work investigates the effects of gas seeps on the navigation performance of AUVs. Indeed, the navigation of underwater vehicles mostly relies on acoustic sensors, as Doppler Velocity Log (DVL), which can be negatively affected by the presence of gas bubbles. The paper explores two solutions, based on two different acoustic sensors working at distinct frequencies: a DVL sensor and an Ultra-Short BaseLine (USBL) device. Both strategies have been implemented and tested during at-sea experiments, where gas leaks have been artificially reproduced. Results showed that both methods suffer from the presence of gas bubbles, causing erroneous DVL measurements and lost of USBL connectivity, respectively.
Underwater exploration and monitoring are particularly challenging for the absence of GPS, limited communications, high hydrodynamic pressure and harsh environmental conditions. Autonomous swarms of underwater robots can play a crucial role for missions such as wide area underwater exploration, environmental monitoring and inspection of existing engineering infrastructures, like oil and gas, and archaeological or historical sites, given their properties of scalability, robustness, flexibility, adaptability, enlarged perception and tasks’ parallelization. Driven by the need to understand the state of art and develop new solutions within the realization of a new swarm of underwater fishes 1 , we provide here a critical review of past and current projects of underwater swarms, focusing on sensors, mission tasks, algorithms, simulation environments and real life proofs of concept. Moreover, we analyze the research directions that can improve the impact of autonomous underwater swarms on environmental preservation and marine sustainable development, also considering the limiting factors imposed on these prospects. 1 This work is part of a new project “Heterogeneous Swarm of Underwater Autonomous Vehicles” funded by the Technology Innovation Institute and developed with Khalifa University, UAE
We present DAVE Aquatic Virtual Environment (DAVE)1, an open source simulation stack for underwater robots, sensors, and environments. Conventional robotics simulators are not designed to address unique challenges that come with the marine environment, including but not limited to environment conditions that vary spatially and temporally, impaired or challenging perception, and the unavailability of data in a generally unexplored environment. Given the variety of sensors and platforms, wheels are often reinvented for specific use cases that inevitably resist wider adoption.Building on existing simulators, we provide a framework to help speed up the development and evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea. The framework includes basic building blocks (e.g., new vehicles, water-tracking Doppler Velocity Logger, physics-based multibeam sonar) as well as development tools (e.g., dynamic bathymetry spawning, ocean currents), which allows the user to focus on methodology rather than software infrastructure. We demonstrate usage through example scenarios, bathymetric data import, user interfaces for data inspection and motion planning for manipulation, and visualizations.1DAVE is available at https://github.com/Field-Robotics-Lab/dave
Larsen & Toubro, INDIA in conjunction with Edgelab Srl, ITALY have developed the Amogh Survey System, a new 1000m class autonomous underwater vehicle aimed at conducting planned hydrographic surveys using multiple sensors (payloads) namely, Multi-Beam Echo Sounder (MBES), Side Scan Sonar (SSS), Sub-Bottom Profiler (SBP), Underwater High resolution Camera and Conductivity Temperature and Depth (CTD) / Sound Velocity Profiler (SVP). Amogh’s survey system complies with the International Hydrographic Organization (IHO) Standard for Hydrographic Surveys S44 and IMO regulations for safety of navigation. The Amogh Survey System includes an electro-hydraulic telescopic launch frame and launch ramp capable of conducting AUV recovery operations by Nose-Line Recovery. The system can be installed on-board commercial and military survey ships, as well as vessels of opportunity such as barges and pontoons with minimal modifications. It can also be launched and recovered from the pier. It consists of a self-contained, air conditioned storage container with adequate space to additionally conduct maintenance of the vehicle.We present the vehicle design, system architecture, realization and results of sea trials performed in the Mediterranean Sea, off the west coast of Italy.
Time synchronization in autonomous underwater vehicle (AUV) formations is significant for joint underwater survey tasks. Maintaining a common time scale can improve the efficiency of cooperative localization, formation control, and data fusion. Instead of using atomic clocks to limit the offset and drift of time, we propose an acoustic and optical cooperative method to synchronize the clocks. Acoustic communication is used to guide the establishment of the optical link and to share the states of the AUVs, while optical communication is used to measure the time difference between the clocks of the two AUVs. The field experiments demonstrated that our proposed method can perform time synchronization in real scenarios.
Autonomous Underwater Vehicles (AUVs) are proving to be a key component in the global observing system, with their ability to provide unique data sets particularly at abyssal depths or under ice. Autosub5 is the latest in a line of large work class AUVs developed by the National Oceanography Centre specifically tailored for oceanographic science applications. This paper describes the work currently being undertaken to transition the vehicle from an engineering prototype through to a science ready platform. The 18 months process saw the AUV assembled in early 2021 and then undertake a series of trials and incremental payload integrations through to a science rehearsal trial planned for summer 2022.
The coordination and control of autonomous underwater vehicle (AUV) fleets in ocean exploration is a widely researched topic with much groundwork for traditional AUVs. Depending on the mission, AUV fleets can relax mission constraints on individual vehicles and improve a number of performance objectives (e.g. duration, sampling rate, area coverage). As missions begin to require navigation within more confined areas such as caves and coral reefs, however, safe interaction with such environments becomes more difficult for typical rigid AUVs and more feasible for soft, compliant underwater robots that can adaptively deform to their surroundings. Moreover, soft underwater robots show great promise as biomimetic vehicles that can take inspiration from nature’s swimmers and help answer questions about their behavior, for instance about the schooling capabilities observed in many fish species. Unfortunately, few fully autonomous, self-contained underwater soft robots have been developed, let alone fleets of such robots. To address this, we present a milestone towards formation control of a fully autonomous, multi-soft robotic fleet inspired by fish schooling. We present a vision-based, leader-follower formation strategy using an untethered soft robotic fish (SoFi) platform that enables one SoFi robot to pursue another via a visual servoing behavior. Our system demonstrates basic formation control of a pair of fully autonomous, self-contained soft robotic fish without external input.
In-situ calibration of marine sensors requires close-range positioning. In turn, localization relative to a given object of interest is necessary. This paper deals with the detection of a vertical cable hanging from a marine observatory implemented by means of a moored buoy. An algorithm composed of sequential image filtering, segmentation and template matching is proposed. Two approaches for generating the cable’s acoustic image template are introduced. The performance of the approaches, obtained by comparison with ground-truth measurements, are illustrated over challenging cluttered acoustic images collected in a test tank. The results indicate a performance better than 74% of the best candidate to match the actual cable.
Marine growth affects offshore structures, causing additional weight and roughened surfaces, increasing wave load. In order to reduce these issues, regular inspection and cleaning can be carried out using various methods, of which one is Remotely Operated Vehicle-based (ROV) operations. In the work presented here, the design of a task-specific ROV for marine-growth cleaning is described, which is differentiated from the normal general-purpose ROVs currently used for this purpose by specialized construction and the use of a simple yet flexible framework. Compared to existing solutions, the proposed framework requires limited low-level programming, which heavily simplifies the implementation and thus reduces the associated practical overhead. The presented ROV prototype design has been demonstrated in a test tank facility and will be validated in an offshore scenario in a future offshore campaign.
This paper presents experimental ground truth data validation of the ability of Underwater Gliders (UGs) to maneuver in constrained environments through starting, stopping, and maintaining turning motions on demand. This capability has been validated in a pool on a custom made highly maneuverable underwater glider, ROUGHIE, using an underwater motion capture system for ground truth pose tracking. The experiments indicate that ROUGHIE is capable of robust and repeatable operation on complex paths due to its ability to effectively transition between stable flights and follow concatenated flight patterns. These maneuvers are accomplished on ROUGHIE through the injection of a neutrally buoyant vehicle state that enables ROUGHIE to maintain stability while transitioning between stable flights. Other internally actuated gliders can perform similar operations if they rapidly and efficiently start, stop, and maintain turns at different moments during the operation. The ground truth data presented here forms a basis for future work on data-driven modelling of UGs to enable complex mission operations.
Seabed mapping is a common application for marine robots, and it is often framed as a coverage path planning problem in robotics. During a robot-based survey, the coverage of perceptual sensors (e.g., cameras, LIDARS and sonars) changes, especially in underwater environments. Therefore, online path planning is needed to accommodate the sensing changes in order to achieve the desired coverage ratio. In this paper, we present a sensing confidence model and a uncertainty-driven sampling-based online coverage path planner (SO-CPP) to assist in-situ robot planning for seabed mapping and other survey-type applications. Different from conventional lawnmower pattern, the SO-CPP will pick random points based on a probability map that is updated based on in-situ sonar measurements using a sensing confidence model. The SO-CPP then constructs a graph by connecting adjacent nodes with edge costs determined using a multi-variable cost function. Finally, the SO-CPP will select the best route and generate the desired waypoint list using a multi-variable objective function. The SO-CPP has been evaluated in a simulation environment with an actual bathymetric map, a 6-DOF AUV dynamic model and a ray-tracing sonar model. We have performed Monte Carlo simulations with a variety of environmental settings to validate that the SO-CPP is applicable to a convex workspace, a non-convex workspace, and unknown occupied workspace. So-CPP is found outperform regular lawnmower pattern survey by reducing the resulting traveling distance by upto 20%. Besides that, we observed that the prior knowledge about the obstacles in the environment has minor effects on the overall traveling distance. In the paper, limitation and real-world implementation are also discussed along with our plan in the future.
This paper introduces an innovative methodology for enabling AUVs to explore an area of interest while simultaneously look for and localize OPIs. A probabilistic semantic occupancy mapping solution that fuses an FLS-based mapping solution and a CNN-based ATR strategy has been designed. In detail. it permits to includes the knowledge about the presence of the OPIs by using the ATR findings. The semantic map enables the Informative Path Planning algorithm to generate paths that cover the area of interest and simultaneously reduces the target localization uncertainty. Therefore, this methodology allows an AUV to meaningfully perceive and model the solution surroundings and autonomously conduct inspections surveys. The proposed solution has been validated with realistic simulations made by means of the Unmanned Underwater Vehicle Simulator, where a dynamic model of FeelHippo AUV was implemented.
Object detection is one of necessary techniques for autonomous underwater vehicles (AUVs) to automate their missions. However, underwater object detection requires a large number of data images of target object. This paper proposes a method to generate highly reliable training images through sonar simulator and background noise templates. Sonar simulator has been developed to generate ideal images of target by modeling imaging mechanism of sonar sensor. To make the image realistic, background noise acquired in the blank water tank are added to the simulated images. Finally, the AUV could detect the target objects at sea using a convolutional neural network trained with the generated images without any field data which is difficult to obtain.
Accurate identification of an uncertain underwater environment is one of the challenges of underwater robotics. Autonomous Underwater Vehicle (AUV) needs to understand its environment accurately to achieve autonomous tasks. The method proposed in this paper is a real-time automatic target recognition based on Side Scan Sonar images to detect and localize a harbor’s wall. This paper explains real-time Side Scan Sonar image generation and compares three Deep Learning object detection algorithms (YOLOv5, YOLOv5-TR, and YOLOX) using transfer learning. The YOLOv5-TR algorithm has the most accurate detection with 99% during training, whereas the YOLOX provides the best accuracy of 91.3% for a recorded survey detection. The YOLOX algorithm realizes the flow chart validation’s real-time detection and target localization.
This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.
AUVs have been employed in underwater surveys for several years. These kinds of missions were mostly based on pre-defined waypoints in safe distances from obstacles and the seabed to generate maps of the environment. Current developments in the industry take the next step, long range AUVs are being designed by several manufacturers. The mission types range from survey missions to autonomous on-the-fly mission adaptations based on events and observations. These mission types require more advanced autonomy which should also be reflected in the software architecture of the AUV. An approach to tackle this is presented here.
The use of compact underwater vehicles for deep see exploration is still a big challenge in terms of available energy, reliability and robustness. Due to limited payload capacity these vehicles have to be equipped with narrow mission specific hardware. At the same time those vehicles still have to provide suitable navigation as well as actuation and communication solutions not unlike larger more capable vehicles. Using small compact vehicles in the deep sea requires different setups or at least a rapidly reconfigurable system that shares common building blocks in hardware and software in order to perform tasks. To this end this paper addresses the concept and development of a ROS2 based modular soft-and hardware architecture, which allows to decentralize and distribute different tasks by using microcontroller equipped modules in order to take advantage of a distributed data communication framework such as DDS (Data Distribution Service) and thus implement the microcontroller-oriented operating system (micro-ROS) in conjunction with ROS2 in the marine robotics domain. We report on initial tests and sea evaluations and consequently present an outlook toward the implementation of a new class of AUVs.
