Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113717/1/rob21554.pd

Underwater Exploration and Mapping

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.acceptedVersio

On-line 3D path planning for close-proximity surveying with AUVs

We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle’s configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project

Sparus II AUV - a hovering vehicle for seabed inspection

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

Online view planning for inspecting unexplored underwater structures

In this paper, 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 (NBV) 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, parametrized 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 is firstly incorporated into a 2-dimensional (2D) 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 towards 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

Optimized environment exploration for autonomous underwater vehicles

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 un- derwater 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

A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects

Featured Application A potential application of the work is the underwater 3D inspection of industrial structures, such as oil and gas pipelines, offshore wind turbine foundations, or anchor chains. Abstract A new underwater 3D scanning device based on structured illumination and designed for continuous capture of object data in motion for deep sea inspection applications is introduced. The sensor permanently captures 3D data of the inspected surface and generates a 3D surface model in real time. Sensor velocities up to 0.7 m/s are directly compensated while capturing camera images for the 3D reconstruction pipeline. The accuracy results of static measurements of special specimens in a water basin with clear water show the high accuracy potential of the scanner in the sub-millimeter range. Measurement examples with a moving sensor show the significance of the proposed motion compensation and the ability to generate a 3D model by merging individual scans. Future application tests in offshore environments will show the practical potential of the sensor for the desired inspection tasks