This work was developed under the need for an acoustic localization system to monitor marine protected areas (MPAs) with the help of autonomous underwater vehicles (AUVs). Although the use of acoustic signals for underwater localization has been previously studied, most of the solutions rely on filter-based optimization, which is prone to linearization problems in long-term applications. Instead, we implemented a Modular Acoustic Graph Simultaneous Localization and Mapping (SLAM) algorithm that, using a factor graph framework, tracks acoustic beacons with either ranges or bearings. In addition, we developed several novel methods, like a delayed-position update for ultra-short baseline (USBL) position factor integration process, an initialization algorithm for acoustic landmarks, and the creation of a new 3D bearing factor that combines two angles. After developing the algorithm, field experiments were carried out in different areas on the coast of Catalonia. Besides the localization, some monitoring tasks were also tested, such as visual mapping of localized landmarks or optical transmission of data with seafloor stations, which helped validate the accuracy of the acoustic localization system. The results of such experiments are presented and discussed.

Robot localization is a fundamental task in achieving true autonomy. Recently, many graph-based navigators have been proposed that combine an inertial measurement unit (IMU) with an exteroceptive sensor applying IMU preintegration to synchronize both sensors. IMUs are affected by biases that also have to be estimated. To increase the navigator robustness when faults appear on the perception system, IMU preintegration can be complemented with linear velocity measurements obtained from visual odometry, leg odometry, or a Doppler Velocity Log (DVL), depending on the robotic application. Moreover, higher grade IMUs are sensitive to the Earth rotation rate, which must be compensated in the preintegrated measurements. In this article, we propose a general purpose preintegration methodology formulated on a compact Lie group to set motion constraints on graph simultaneous localization and mapping problems considering the Earth rotation effect. We introduce the SE$_{N}(3)$group to jointly preintegrate IMU data and linear velocity measurements to preserve all the existing correlation within the preintegrated quantity. Field experiments using an autonomous underwater vehicle equipped with a DVL and a navigational grade IMU are provided and results are benchmarked against a commercial filter-based inertial navigation system to prove the effectiveness of our methodology.

In robotics, occupancy grids serve as required repositories of information about the environment in numerous applications. One such critical application is Simultaneous Localization and Mapping (SLAM), where robots dynamically scan and explore their surroundings while in motion. In the context of extended-duration missions, it becomes imperative to confront the complexities linked to the expansion of occupancy grids as well as handling loop closure detection. These challenges primarily revolve around two key aspects: enabling the seamless expansion of the map on multiple occasions, thus avoiding the need to map smaller regions in numerous separate missions, and ensuring real-time updates to the map to sustain the robot’s knowledge base and enhance its responsiveness. To address these challenges, we introduce an innovative map called Frustum Accelerated GPU-Based Grid Map (FRAGG-Map). This map adopts a highly parallelizable 3D grid structure and leverages the power of CUDA kernels to facilitate efficient insertion of point-clouds and enables real-time updates of the map. FRAGG-Map identifies the portions of the map that require updates and utilises the GPU to update them, significantly enhancing computational performance. Our results show that FRAGG-Map can run 31 times faster than OctoMap, significantly outperforming state-of-the-art methods.

Acoustic perception in underwater environments is challenging due to the low frequency of the acquisition system and multiple and huge sources of noise. Therefore, point clouds built by profiling sonars mounted on Autonomous Underwater Vehicles (AUV) are sparse and noisy. To solve the mapping task, AUVs need a registration algorithm to prevent maps from inconsistencies. Many scan matching algorithms are available, however, a few of them are specialized in acoustic data. In this paper, a probabilistic scan matching methodology based on Gaussian Mixtures Models (GMM) is presented and, for the first time, the Bayesian-GMM algorithm is applied in this context to model acoustic data. The scan matching problem is properly formulated using Lie groups to define pose. In addition, this methodology can return an uncertainty measure for the matching result, which is fundamental in Pose SLAM applications. This tool is implemented in a public C++library11The library repository can be found in https://bitbucket.org/gmmregistration/gmm_registration. that can process in real-time 2D and 3D scans acquired by a profiling sonar. Theoretical justification and results with real data are provided to benchmark our method against the state-of-the-art Normal Distributions Transforms (NDT) technique.

Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot's onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous underwater vehicles (AUVs) is presented. By introducing a loosely coupled multilayered planning design, our framework is able to generate dynamically feasible trajectories while keeping the planning time low enough for online planning. First, a fast path planner operating in a lower-dimensional projected space computes a lead path from the start to the goal configuration. Then, the lead path is used to bias the sampling of a second motion planner, which takes into account all the dynamic constraints. Furthermore, we propose a strategy for online planning that saves computational resources by generating the final trajectory only up to a finite horizon. By using the finite horizon strategy together with the multilayered approach, the sampling of the second planner focuses on regions where good quality solutions are more likely to be found, significantly reducing the planning time. To provide strong safety guarantees our framework also incorporates the conservative approximations of inevitable collision states (icss). finally, we present simulations and experiments using a real underwater robot to demonstrate the capabilities of our framework.

We present a novel method for planning 3D coverage paths for inspection of complex structures on the ocean floor (such as seamounts or coral reefs) using an autonomous underwater vehicle (AUV). Our method initially uses an a priori map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target structure. We then go beyond previous approaches in the literature by considering the vehicle's position uncertainty rather than relying on the unrealistic assumption of an idealized path execution. To this aim, we present a replanning algorithm based on stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in realtime during the inspection mission, adapting the path according to the measurements provided by the vehicle's range sensing sonars. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV where we cover a concrete block of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depth. Moreover, we apply state-of-the-art surface reconstruction techniques to the data acquired by the AUV and obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping.

Forward-looking sonar can be used for underwater mapping when water visibility is poor. The generation of an acoustic mosaic of the environment is of high interest when underwater vehicles are used for surveys or search tasks. Moreover, if the mosaic is generated in real-time it can be used to provide instantaneous location feedback (e.g. to a ROV pilot or to an AUV), to ensure complete coverage of an area or facilitate the immediate location of targets. In this paper, we present an approach for achieving such a real-time mosaicing capability. Our system considers a simplified imaging model and estimates 2D sonar motions from the pairwise registration of sonar frames. The registration is performed by using a Fourier-based technique, avoiding the extraction of features and ensuring a fast implementation. The mosaicing problem is formulated using a pose graph, with the vertices being the sonar image positions and the edges being constraints from the vehicle odometry and the registration of consecutive and non-consecutive frames. The graph is incrementally optimized using the g2o framework and the optimized poses are then used to build the mosaic online. We apply the method on data gathered on real conditions and show that the resulting sonar mosaic closely matches both the offline generated mosaic as well as ground truth measurements while operating under real-time constraints.

This paper describes the design and implementation of a robust position tracking control scheme for an Autonomous Underwater Vehicle (AUV). The proposed controller does not require knowledge of the vehicle's dynamic parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances acting on the vehicle. The resulting scheme is of low complexity and computational cost and thus can be easily integrated to an embedded control platform of an AUV. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona500 AUV into two different missions: a) navigation and stabilization to a specific configuration, b) meandrus-like trajectory tracking. In both cases the vehicle was under the influence of time-varying external disturbances caused by a high-pressure water jet installed on the Girona500 manipulator.

Tracking an underwater chain using an autonomous vehicle can be a first step towards more efficient solutions for cleaning and inspecting mooring chains. We propose to use a forward looking sonar as a primary perception sensor to enable the vehicle operation in limited visibility conditions and overcome the turbidity arisen during marine growth removal. Despite its advantages, working with acoustic imagery raises additional challenges to the involved image processing and control methodologies. In this paper we present a robust framework to perform chain following, combining perception, planning and control disciplines. We first introduce a detection system that exploits the sonar's high frame rate and applies local pattern matching to handle the complexity of detecting link chains in acoustic images. Then, a planning system deals with the dispersed detections and determines the link waypoints that the vehicle should reach. Finally, the vehicle is guided through these waypoints using a high level controller that has been tailored to simultaneously traverse the chain and keep track of upcoming links. Experiments on real data demonstrate the capability of autonomously follow a chain with sufficient accuracy to perform subsequent cleaning or inspection tasks.

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities: the maintenance of permanent underwater structures as well as the recovery of benthic stations or black-boxes are a few of them. These tasks are addressed nowadays using manned submersibles or work-class remotely operated vehicles (ROVs), equipped with teleoperated arms under human supervision. In the context of the TRITON Spanish funded project, a subsea panel docking and an intervention procedure are proposed. The light-weight intervention AUV (I-AUV) Girona 500 is used to autonomously dock into a subsea panel using a funnel-based docking method for passive accommodation. Once docked, an autonomous fixed-based manipulation system, which uses feedback from a digital camera, is used to turn a valve and plug/unplug a connector. The paper presents the techniques used for the autonomous docking and manipulation as well as how the adapted subsea panel has been designed to facilitate such operations.

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicle's dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard camera's field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500.

This paper presents a navigation system for an Autonomous Underwater Vehicle (AUV) which merges standard dead reckoning navigation data with absolute position fixes from an Ultra-Short Base Line (USBL) system. Traditionally, the USBL transceiver is located on the surface, which makes necessary to feed the position fixes back to the AUV by means of an acoustic modem. An Information filter, which maintains a bounded circular buffer of past vehicle poses, is in charge of the sensor data fusion while dealing with the delays induced by the acoustic communication. The method is validated using a data set gathered for a dam inspection task.

This paper provides a methodology to model and execute coordinated missions involving multiple vehicles using Petri nets. Individual vehicle missions are defined by means of Petri nets and three constraints are added for coordination purposes: mutual exclusion, ordering and synchronisation. The proposed methodology generates a centralised net, checks if it is deadlock free and then obtains a decentralised Petri net for every vehicle minimising the communication between them. The resulting Petri nets implement the multi-vehicle mission control program that is responsible for coordinating in real-time the set of vehicles involved in the mission.

This paper presents the design and implementation of a mission control system (MCS) for an autonomous underwater vehicle (AUV) based on Petri nets. In the proposed approach the Petri nets are used to specify as well as to execute the desired autonomous vehicle mission. The mission is easily described using an imperative programming language called mission control language (MCL) that formally describes the mission execution thread. A mission control language compiler (MCL-C) able to automatically translate the MCL into a Petri net is described and a real-time Petri net player that allows to execute the resulting Petri net onboard an AUV are also presented.

A pioneer team of students of the University of Girona decided to design and develop an autonomous underwater vehicle (AUV) called ICTINEUAUVto face the Student Autonomous Underwater Challenge-Europe (SAUC-E). The prototype has evolved from the initial computer aided design (CAD) model to become an operative AUV in the short period of seven months. The open frame and modular design principles together with the compatibility with other robots previously developed at the lab have provided the main design philosophy. Hence, at the robot's core, two networked computers give access to a wide set of sensors and actuators. The Gentoo/Linux distribution was chosen as the onboard operating system. A software architecture based on a set of distributed objects with soft real time capabilities was developed and a hybrid control architecture including mission control, a behavioural layer and a robust map-based localization algorithm made ICTINEUAUVthe winning entry.

This paper presents a complete control architecture that has been designed to fulfill predefined missions with an autonomous underwater vehicle (AUV). The control architecture has three levels of control: mission level, task level and vehicle level. The novelty of the work resides in the mission level, which is built with a Petri network that defines the sequence of tasks that are executed depending on the unpredictable situations that may occur. The task control system is composed of a set of active behaviours and a coordinator that selects the most appropriate vehicle action at each moment. The paper focuses on the design of the mission controller and its interaction with the task controller. Simulations, inspired on an industrial underwater inspection of a dam grate, show the effectiveness of the control architecture