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

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

Offshore marine visualization

In 85 B.C. a Greek philosopher called Posidonius set sail to answer an age-old question: how deep is the ocean? By lowering a large rock tied to a very long length of rope he determined that the ocean was 2km deep. These line and sinker methods were used until the 1920s when oceanographers developed the first echo sounders that could measure the water's depth by reflecting sound waves off the seafloor. The subsequent increase in sonar depth soundings resulted in oceanologists finally being able to view the alien underwater landscape. Paper printouts and records dominated the industry for decades until the mid 1980s when new digital sonar systems enabled computers to process and render the captured data streams.In the last five years, the offshore industry has been particularly slow to take advantage of the significant advancements made in computer and graphics technologies. Contemporary marine visualization systems still use outdated 2D representations of vessels positioned on digital charts and the potential for using 3D computer graphics for interacting with multidimensional marine data has not been fully investigated.This thesis is concerned with the issues surrounding the visualization of offshore activities and data using interactive 3D computer graphics. It describes the development of a novel 3D marine visualization system and subsequent study of marine visualization techniques through a number of offshore case studies that typify the marine industry. The results of this research demonstrate that presenting the offshore engineer or office based manager with a more intuitive and natural 3D computer generated viewing environment enables complex offshore tasks, activities and procedures to be more readily monitored and understood. The marine visualizations presented in this thesis take advantage of recent advancements in computer graphics technology and our extraordinary ability to interpret 3D data. These visual enhancements have improved offshore staffs' spatial and temporal understanding of marine data resulting in improved planning, decision making and real-time situation awareness of complex offshore data and activities

Toward the development of smart capabilities for understanding seafloor stretching morphology and biogeographic patterns via DenseNet from high-resolution multibeam bathymetric surveys for underwater vehicles

The increasing use of underwater vehicles facilitates deep-sea exploration at a wide range of depths and spatial scales. In this paper, we make an initial attempt to develop online computing strategies to identify seafloor categories and predict biogeographic patterns with a deep learning-based architecture, DenseNet, integrated with joint morphological cues, with the expectation of potentially developing its embedded smart capacities. We utilized high-resolution multibeam bathymetric measurements derived from MBES and denoted a collection of joint morphological cues to help with semantic mapping and localization. We systematically strengthened dominant feature propagation and promoted feature reuse via DenseNet by applying the channel attention module and spatial pyramid pooling. From our experiment results, the seafloor classification accuracy reached up to 89.87%, 82.01%, and 73.52% on average in terms of PA, MPA, and MIoU metrics, achieving comparable performances with the state-of-the-art deep learning frameworks. We made a preliminary study on potential biogeographic distribution statistics, which allowed us to delicately distinguish the functionality of probable submarine benthic habitats. This study demonstrates the premise of using underwater vehicles through unbiased means or pre-programmed path planning to quantify and estimate seafloor categories and the exhibited fine-scale biogeographic patterns

EM 2000 Microbathymetric and HYDROSWEEP DS-2 Bathymetric Surveying – a Comparison of Seafloor Topography at Porcupine Bank, west of Ireland

One of the latest discoveries in the world oceans are carbonate structures in the North-East Atlantic. In the frameworks of several European projects, the research vessel POLARSTERN and underwater robot VICTOR 6000 were engaged to explore these areas. The data described in this thesis were collected during the expedition ARK XIX/3 between 16 - 19th June 2003. Bathymetric and microbathymetric data in parts of the Pelagia Province, located on the northern Porcupine Bank, west of Ireland, were measured with two multibeam sonar systems deployed at different distances from the bottom. The four compared models come from a KONGSBERG SIMRAD EM 2000 multibeam sonar system and an ATLAS ELEKTRONIK HYDROSWEEP DS-2 multibeam sonar system. After necessary corrections of the data, digital terrain models were created, subtracted and correlated using appropriate software. This thesis begins with a description of the historical background of bathymetry, followed by a description of the principles of navigation and underwater navigation, inertial navigation systems, and the calibration of these systems. Systematic errors will be pointed out. It examines the measurement principles of the echo sounders used on the ARK XIX/3a expedition and accompanying necessary procedures, such as CTD measurements. A discussion of how the data are processed from raw data to edited results, and the effects of the errors, follows. One chapter is dedicated to a comparison and interpretation of the data. Sidescan, mosaic and PARASOUND data from the Hedge and Scarp Mounds are introduced as complementary information

Autonomous Exploration of Large-Scale Natural Environments

This thesis addresses issues which arise when using robotic platforms to explore large-scale, natural environments. Two main problems are identified: the volume of data collected by autonomous platforms and the complexity of planning surveys in large environments. Autonomous platforms are able to rapidly accumulate large data sets. The volume of data that must be processed is often too large for human experts to analyse exhaustively in a practical amount of time or in a cost-effective manner. This burden can create a bottleneck in the process of converting observations into scientifically relevant data. Although autonomous platforms can collect precisely navigated, high-resolution data, they are typically limited by finite battery capacities, data storage and computational resources. Deployments are also limited by project budgets and time frames. These constraints make it impractical to sample large environments exhaustively. To use the limited resources effectively, trajectories which maximise the amount of information gathered from the environment must be designed. This thesis addresses these problems. Three primary contributions are presented: a new classifier designed to accept probabilistic training targets rather than discrete training targets; a semi-autonomous pipeline for creating models of the environment; and an offline method for autonomously planning surveys. These contributions allow large data sets to be processed with minimal human intervention and promote efficient allocation of resources. In this thesis environmental models are established by learning the correlation between data extracted from a digital elevation model (DEM) of the seafloor and habitat categories derived from in-situ images. The DEM of the seafloor is collected using ship-borne multibeam sonar and the in-situ images are collected using an autonomous underwater vehicle (AUV). While the thesis specifically focuses on mapping and exploring marine habitats with an AUV, the research applies equally to other applications such as aerial and terrestrial environmental monitoring and planetary exploration

Mapping of complex marine environments using an unmanned surface craft

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 185-199).Recent technology has combined accurate GPS localization with mapping to build 3D maps in a diverse range of terrestrial environments, but the mapping of marine environments lags behind. This is particularly true in shallow water and coastal areas with man-made structures such as bridges, piers, and marinas, which can pose formidable challenges to autonomous underwater vehicle (AUV) operations. In this thesis, we propose a new approach for mapping shallow water marine environments, combining data from both above and below the water in a robust probabilistic state estimation framework. The ability to rapidly acquire detailed maps of these environments would have many applications, including surveillance, environmental monitoring, forensic search, and disaster recovery. Whereas most recent AUV mapping research has been limited to open waters, far from man-made surface structures, in our work we focus on complex shallow water environments, such as rivers and harbors, where man-made structures block GPS signals and pose hazards to navigation. Our goal is to enable an autonomous surface craft to combine data from the heterogeneous environments above and below the water surface - as if the water were drained, and we had a complete integrated model of the marine environment, with full visibility. To tackle this problem, we propose a new framework for 3D SLAM in marine environments that combines data obtained concurrently from above and below the water in a robust probabilistic state estimation framework. Our work makes systems, algorithmic, and experimental contributions in perceptual robotics for the marine environment. We have created a novel Autonomous Surface Vehicle (ASV), equipped with substantial onboard computation and an extensive sensor suite that includes three SICK lidars, a Blueview MB2250 imaging sonar, a Doppler Velocity Log, and an integrated global positioning system/inertial measurement unit (GPS/IMU) device. The data from these sensors is processed in a hybrid metric/topological SLAM state estimation framework. A key challenge to mapping is extracting effective constraints from 3D lidar data despite GPS loss and reacquisition. This was achieved by developing a GPS trust engine that uses a semi-supervised learning classifier to ascertain the validity of GPS information for different segments of the vehicle trajectory. This eliminates the troublesome effects of multipath on the vehicle trajectory estimate, and provides cues for submap decomposition. Localization from lidar point clouds is performed using octrees combined with Iterative Closest Point (ICP) matching, which provides constraints between submaps both within and across different mapping sessions. Submap positions are optimized via least squares optimization of the graph of constraints, to achieve global alignment. The global vehicle trajectory is used for subsea sonar bathymetric map generation and for mesh reconstruction from lidar data for 3D visualization of above-water structures. We present experimental results in the vicinity of several structures spanning or along the Charles River between Boston and Cambridge, MA. The Harvard and Longfellow Bridges, three sailing pavilions and a yacht club provide structures of interest, having both extensive superstructure and subsurface foundations. To quantitatively assess the mapping error, we compare against a georeferenced model of the Harvard Bridge using blueprints from the Library of Congress. Our results demonstrate the potential of this new approach to achieve robust and efficient model capture for complex shallow-water marine environments. Future work aims to incorporate autonomy for path planning of a region of interest while performing collision avoidance to enable fully autonomous surveys that achieve full sensor coverage of a complete marine environment.by Jacques Chadwick Leedekerken.Ph.D

Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean Sea)

Underwater photogrammetry provides a means of generating high-resolution products such as dense point clouds, 3D models, and orthomosaics with centimetric scale resolutions. Underwater photogrammetric models can be used to monitor the growth and expansion of benthic communities, including the assessment of the conservation status of seagrass beds and their change over time (time lapse micro-bathymetry) with OBIA classifications (Object-Based Image Analysis). However, one of the most complex aspects of underwater photogrammetry is the accuracy of the 3D models for both the horizontal and vertical components used to estimate the surfaces and volumes of biomass. In this study, a photogrammetry-based micro-bathymetry approach was applied to monitor Posidonia oceanica restoration actions. A procedure for rectifying both the horizontal and vertical elevation data was developed using soundings from high-resolution multibeam bathymetry. Furthermore, a 3D trilateration technique was also tested to collect Ground Control Points (GCPs) together with reference scale bars, both used to estimate the accuracy of the models and orthomosaics. The root mean square error (RMSE) value obtained for the horizontal planimetric measurements was 0.05 m, while the RMSE value for the depth was 0.11 m. Underwater photogrammetry, if properly applied, can provide very high-resolution and accurate models for monitoring seagrass restoration actions for ecological recovery and can be useful for other research purposes in geological and environmental monitoring