The ATLANTIS project aims to establish a pioneer pilot infrastructure that will allow the demonstration of key enabling robotic technologies for inspection and maintenance of offshore wind farms. The pilot will be implemented in Viana do Castelo, Portugal, and will allow for testing, validation and demonstration of technologies with a range of technology readiness level, in near-real/real environments.The demonstration of robotic technologies can promote the transition from traditional inspection and maintenance methodologies towards automated robotic strategies, that remove or reduce the need of human-in-the-loop, reducing costs and improving the safety of interventions. Eight scenarios, split into four showcases, will be used to determine the required developments for robotic integration and demonstrate the applicability in the inspection and maintenance processes. The scenarios considered were identified by end-users as key areas for robotics.

This study presents a novel octree‐based three‐dimensional (3D) exploration and coverage method for autonomous underwater vehicles (AUVs). Robotic exploration can be defined as the task of obtaining a full map of an unknown environment with a robotic system, achieving full coverage of the area of interest with data from a particular sensor or set of sensors. While most robotic exploration algorithms consider only occupancy data, typically acquired by a range sensor, our approach also takes into account optical coverage, so the environment is discovered with occupancy and optical data of all discovered surfaces in a single exploration mission. In the context of underwater robotics, this capability is of particular interest, since it allows one to obtain better data while reducing operational costs and time. This study expands our previous study in 3D underwater exploration, which was demonstrated through simulation, presenting improvements in the view planning (VP) algorithm and field validation. Our proposal combines VP with frontier‐based (FB) methods, and remains light on computations even for 3D environments thanks to the use of the octree data structure. Finally, this study also presents extensive field evaluation and validation using the Girona 500 AUV. In this regard, the algorithm has been tested in different scenarios, such as a harbor structure, a breakwater structure, and an underwater boulder.

To autonomously explore complex underwater environments, it is convenient to develop motion planning strategies that do not depend on prior information. In this publication, we present a robotic exploration algorithm for autonomous underwater vehicles (AUVs) that is able to guide the robot so that it explores an unknown 2-dimensional (2D) environment. The algorithm is built upon view planning (VP) and frontier-based (FB) strategies. Traditional robotic exploration algorithms seek full coverage of the scene with data from only one sensor. If data coverage is required for multiple sensors, multiple exploration missions are required. Our approach has been designed to sense the environment achieving full coverage with data from two sensors in a single exploration mission: occupancy data from the profiling sonar, from which the shape of the environment is perceived, and optical data from the camera, to capture the details of the environment. This saves time and mission costs. The algorithm has been designed to be computationally efficient, so that it can run online in the AUV’s onboard computer. In our approach, the environment is represented using a labeled quadtree occupancy map which, at the same time, is used to generate the viewpoints that guide the exploration. We have tested the algorithm in different environments through numerous experiments, which include sea operations using the Sparus II AUV and its sensor suite.

Underwater robots are subject to complex hydrodynamic 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.

Autonomous underwater vehicles (AUVs) have been extensively used for open-sea exploration. However, the mapping or inspection of complex underwater structures, which have an interest from the scientific and the industrial point of view, is still carried out by professional divers or remotely operated vehicles. We propose a probabilistic next-best-view planner, targeted to hover-capable AUVs, that will allow them to explore these complex environments without an apriori model. The proposed method is based on scanning the area from different viewpoints in an iterative way. At each step, a viewpoint is chosen from a set of random samples according to a utility function. An obstacle-free path to the selected viewpoint is planned, and the vehicle navigates to it to gather a new scan that will be registered with the previous ones. To evaluate the proposed method, we present four different tests using the Girona 500 AUV, both in simulation and in real scenarios. The results demonstrate the capability to explore complex environments autonomously, producing models of the environment with a high degree of coverage that can enable mapping and inspection applications.

Achieving full autonomous robotic environment exploration in the underwater domain is very challenging, mainly due to noisy acoustic sensors, high localization error, control disturbances of the water and lack of accurate underwater maps. In this work we present a robotic exploration algorithm for underwater vehicles that does not rely on prior information about the environment. Our method has been greatly influenced by many robotic exploration, view planning and path planning algorithms. The proposed method constitutes a significant improvement over our previous work [1]: Firstly, we refine our exploration approach to improve robustness; Secondly, we propose an alternative map representation based on the quadtree data structure that allows different relevant queries to be performed efficiently, reducing the computational cost of the viewpoint generation process; Thirdly, we present an algorithm that is capable of generating consistent maps even when noisy sonar data is used. The aforementioned contributions have increased the reliability of the algorithm, allowing new real experiments performed in artificial structures but also in more challenging natural environments, from which we provide a 3D reconstruction to show that with this algorithm full optical coverage is obtained.

One of the major goals of the SUNRISE FP7 project is to make the Underwater Internet of Things a reality. In this context, the LOON-DOCK project presented here extends the existing Litoral Ocean Observatory Network testbed with a Docking Station tailored to the Sparus II AUV. The docking system allows a remote user to program survey-like missions through a web-based interface as well as to retrieve the data gathered by the AUV once a mission finalizes. To enable the autonomous docking of the AUV, two complementary and cost-effective localization systems have been developed. The first one implements a range-only localization algorithm to approach the docking station while the second, based on active light beacons, provides high accuracy at short ranges to complete the docking maneuver. The system has been extensively tested, in different trials from a controlled water tank environment to more realistic sea operation conditions proving its viability despite very poor water visibility conditions.

This paper proposes the use of path-planning algorithms for hovering autonomous underwater vehicles (AUVs) in applications where the robot needs to adapt online its trajectory for inspection or safety purposes. In particular, it proposes the platform Sparus II AUV and a set of planning algorithms to conduct these new AUV capabilities. These algorithms generate trajectories under motion constraints, which can be followed without deviations, to ensure the safety even when passing close to obstacles. View planning algorithms are also combined to decide the movements to be executed to discover the unexplored seabed or target, and to cover it with a camera or sonar. Online mapping with profiling sonars and online planning with fast sampling-based algorithms allow the execution of missions without any previous knowledge of the 3-D shape of the environment. Real 2-D results in an artificial harbor structure and simulated natural rocky canyon demonstrate the feasibility of the approach for avoiding or inspecting the underwater environment. These new AUV capabilities can be used to acquire images of the environment that can be used to inspect and map the habitat.

When it is not possible to use remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) with predefined missions to explore complex underwater structures, efficient and safe algorithms for autonomous online exploration are required. In this work we present a robotic exploration algorithm for AUVs which is able to autonomously explore 3D underwater structures. In our proposal, the explored structure must have vertical relief, and the exploration is performed in 2D at a user defined depth. No assumptions are made about the shape of the object, so this makes the algorithm particularly useful to explore unstructured environments. Our approach is able to plan the robot maneuvers to achieve full coverage of the scene with data from two sensors: a scanning profiling sonar, and a camera. The algorithm first incorporates the exteroceptive data from the profiler sonar into a labeled grid map. Then, different candidate viewpoints are generated and the best one is selected according to a metric that balances exploration and trajectory length. Once the best viewpoint has been selected, the robot navigates in the scene to achieve the selected viewpoint configuration. This procedure is repeated until the desired area has been fully explored. To validate our approach, we present simulated and real autonomous explorations of an underwater seamount.

This work proposes a novel 3-dimensional (3D) exploration and coverage methodology for autonomous underwater vehicles (AUVs). Exploration can be defined as the task of discovering an unknown region in the space. Most robotic exploration algorithms seek full coverage of the unknown region with data from one sensor (in the underwater domain, it is usually occupancy data from profiling sonars, or optical data from cameras). Our method is able to guide the robot so that full discovery and coverage is obtained in a single exploration for both occupancy data and optical data. Since the algorithm has to run in the AUV’s onboard computer, with limited processing power, our method is based around efficient operations that can be performed to an octree data structure. This work extends our previous 2-dimensional (2D) method, which has proved to work in real tests conducted with the Sparus II AUV. In this work, we have used three real datasets to generate environments for its exploration in simulation: an underwater seamount, a shipwreck and an underwater cave. Results using the Girona 500 AUV show the feasibility of our exploration approach in all tested scenarios.

This paper describes the experience of preparing and testing the SPARUS II AUV in different applications. The AUV was designed as a lightweight vehicle combining the classical torpedo-shape features with the hovering capability. The robot has a payload area to allow the integration of different equipment depending on the application. The software architecture is based on ROS, an open framework that allows an easy integration of many devices and systems. Its flexibility, easy operation and openness makes the SPARUS II AUV a multipurpose platform that can adapt to industrial, scientific and academic applications. Five units were developed in 2014, and different teams used and adapted the platform for different applications. The paper describes some of the experiences in preparing and testing this open platform to different applications.

We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, that is, those that meet the vehicle's motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time-consuming pruning routines. The second one is to opportunistically check the states' risk of collision. To evaluate the proposed approach, we use the Sparus II performing autonomous missions in different real-world scenarios. These experiments consist of simulated and in-water trials for different tasks. The conducted tasks include the exploration of challenging scenarios such as artificial marine structures, natural marine structures, and confined natural environments. All these applications allow us to extensively prove the efficacy of the presented approach, not only for constant-depth missions (2D), but, more important, for situations in which the vehicle must vary its depth (3D).

Persistent deployment of underwater assets holds the key to achieve consistent, long-term undersea monitoring. To advance in that direction, the LOON-DOCK project aims to demonstrate remote AUV operation and survey data transmission through the Internet using an underwater docking station. We present a funnel-shaped docking station equipped with a contactless high bandwidth link and the necessary equipment to enable autonomous homing and docking by combining acoustic and optical sensing. The proposed combination, using a range-only localization at far distances and a light beacon localization at short ranges ensures a reliable strategy enabled with low-cost equipment and minimal requirements on both the vehicle and the dock sides. Moreover, remote operation is demonstrated by integrating the system in a unified web interface to control underwater assets, thus allowing a user to operate the AUV remotely through Internet. The full system has been successfully validated with tests conducted in a harbor environment.

In most of the current operational autonomous underwater vehicles (AUVs), a survey mission is generally composed of two main stages. The first one conducts an exhaustive coverage over an area of interest, while gathering data of the sea bottom. Then, and after processing the collected data, a second mission is programmed to obtain more detailed information of potential targets, and to cover the gaps that resulted from the first exploration. However, this two-survey strategy can be inefficient, since it requires establishing a communication link between the AUV and its operator for retrieving the data and reprogramming the second mission. To cope with this situation, we present a mission planner that endows an AUV with the capability of extending its missions online. With our approach, the vehicle is also required to conduct an initial and predefined survey of an area of interest, but it processes the gathered data onboard to plan 3D feasible paths to complement the initial exploration. To validate our approach, we present real-world results with the AsterX AUV.

In this letter, we propose a method to automate the exploration of unknown underwater structures for autonomous underwater vehicles (AUVs). The proposed algorithm iteratively incorporates exteroceptive sensor data and replans the next-best-view in order to fully map an underwater structure. This approach does not require prior environment information. However, a safe exploration depth and the exploration area (defined by a bounding box, parameterized by its size, location, and resolution) must be provided by the user. The algorithm operates online by iteratively conducting the following three tasks: (1) Profiling sonar data are first incorporated into a 2-D grid map, where voxels are labeled according to their state (a voxel can be labeled as empty, unseen, occluded, occplane, occupied, or viewed). (2) Useful viewpoints to continue exploration are generated according to the map. (3) A safe path is generated to guide the robot toward the next viewpoint location. Two sensors are used in this approach: a scanning profiling sonar, which is used to build an occupancy map of the surroundings, and an optical camera, which acquires optical data of the scene. Finally, in order to demonstrate the feasibility of our approach, we provide real-world results using the Sparus II AUV.

This paper describes a data set collected with an autonomous underwater vehicle testbed in the unstructured environment of an underwater cave complex. The vehicle is equipped with two mechanically scanned imaging sonar sensors to simultaneously map the caves horizontal and vertical surfaces, a Doppler velocity log, two inertial measurement units, a depth sensor, and a vertically mounted camera imaging the sea floor for ground truth validation at specific points. The testbed collected the data in July 2013, guided by a human diver, to sidestep autonomous navigation in a complex environment. For ease of use, the original robot operating system bag files are provided together with a version combining imagery and human-readable text files for processing on other environments.

We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now considers motion constraints to plan feasible paths, i.e., those that meet vehicle's motion capabilities. The new framework also incorporates a risk function to avoid navigating close to nearby obstacles, and reuses the last best known solution to eliminate time-consuming pruning routines. To evaluate this approach, we use the Sparus II AUV, a torpedo-shaped vehicle performing autonomous missions in a 2-dimensional workspace. We validate the framework's new features by solving tasks in both simulation and real-world in-water trials and comparing results with our previous approach.

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.