Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Adaptive Sampling For Efficient Online Modelling

This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general

A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping

Bathymetric mapping with Autonomous Underwater Vehicles (AUVs) receives increased attentions in recent years. AUVs offer a lower operational cost and smaller carbon footprint with reduced ship usage, and they can provide higher resolution data when surveying the seabed at a closer distance if compared to ships. However, advancements are still needed to improve the data quality of AUV-based surveys. Unlike mobile robots with deterministic mapping performance, multibeam sonars used in AUV-based bathymetric mapping often yields inconsistent swath width due to the varied seabed elevation and surficial properties. As a result, mapping voids may exist between planned lawnmower transects. Although this could be solved by planning closer lawnmower paths, mission time increases proportionally. Therefore, an onboard path planner is demanded to assure the defined survey objective, i.e., coverage rate. Here in this paper, we present a new data-driven coverage path planning (CPP) method, in which the vehicle automatically updates the waypoints intermittently based on an objective function constructed using the information about the exploration preference, sonar performance, and coverage efficiency. The goal of the proposed method is to plan a cost-effective path on-the-fly to obtain high quality mapping result meeting the requirements in coverage rate and uncertainty. The proposed CPP method has been evaluated in a simulated environment with a 6DOF REMUS AUV model and a realistic seafloor topography. A series of trials has been conducted to investigate the performance affected by the parameters in the objective function. We also compared the proposed method with traditional lawnmower and spiral paths. The results show that the weight assignment in the objective function is critical as they affect the overall survey performance. With proper weight settings, the AUV yields better survey performance, coverage rate and coverage efficiency, compared to traditional approaches. Moreover, the proposed method can be easily adjusted or modified to achieve different coverage goals, such as rapid data gathering of the entire region, survey of irregular workspace, or maintaining real time path planning

Static maritime enviroment representation of electronic navigational charts in global path planning

In past years, numerous global path planning methods have been researched and applied in maritime surface navigation. Regardless of intended usage for either decision-support in manned, or autonomous vessel navigation, path planning should generate a safe and efficient route. However, prior to route generation, static maritime environment representation must be created first. Whether it is transformed in to discrete or continuous form, common approach is to use Electronic Navigational Charts (ENCs) as a basis for maritime environment representation. Nevertheless its origins, ENCs still adhere to inherited data generalisations and simplifications to be comprehensible for human navigators. This leads to limitations when considering path planning and spatial resolution at different chart scales. Furthermore, when generating the representation and path, uncertainty must be considered since the quality and accuracy of chart data varies. Although these topics have been addressed separately in their respective domains, their relations have not been researched in detail. The aim of the proposed paper is the review of electronic navigational charts, environment representation and common global path planning approaches’ relations. Forthcoming standards and technologies, such as usage of high-density charts, are presented and discussed as well.Peer Reviewe

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

Development of Autonomous Surface Vessels for Hydrographic Survey Applications

Autonomously navigating surface vessels have a variety of potential applications for ocean mapping. The use of small vessels for coastal mapping is investigated through the development of hardware and software that form a complete system for survey operations. The hardware is selected to minimize cost while providing flexibility for installation on different platforms. MOOS-IvP open-source autonomy software enables independent operation of the vessel and provides for human monitoring. Custom applications allow the sensors and actuators of the hardware platforms to interface with MOOS-IvP. An autonomy behavior is developed that replicates current human driven survey acquisition, in which the boat plans paths automatically to achieve full survey coverage with a swath sonar system. With initial input of a survey boundary and depths from the onboard sonar system, subsequent paths are planned to be offset based on the collected data. This behavior is tested in simulation and field experiments. A model reference adaptive control system for the heading of the vessel is investigated for improved reliability of vessel operation in a variety of conditions and over the full range of operation speeds. Simulations tests verify the adaptation of two types of controllers. A new method for speed control to increase endurance and decrease engine wear is also proposed and simulated. Together, these developments form an easily configurable system that provides automated hydrographic survey capability to a vessel with minimal human involvement for optimal performance

MARV: Marine Autonomous Research Vessel

Conducting hydrologic research in remote areas is currently performed manually, making it a labor intensive and inaccurate solution. Due to the size, weight, and cost of automated solutions on the market today, a need has arisen for a low cost, highly portable, autonomous solution. Working closely with Santa Clara University’s Robotics Systems Lab (RSL), our team has developed a low cost, highly portable autonomous marine research vessel named MARV (Marine Autonomous Research Vessel). It is an autonomous surface platform where scientists outfit the vessel with their own data acquisition equipment. The mechanical chassis is collapsible for modes of remote transportation (i.e. helicopter, small trucks, backpacking). With a final weight of 25 kilograms, material cost of $4,482, and a cross track error of ±1 meter, we have successfully designed and manufactured low cost, highly portable autonomous solution. However, MARV does not operate on an adaptive navigation system. Further developments such as object avoidance and depth control would result in a fully autonomous marine platform

Advancing Estuarine Shoreline Change Analysis Using Small Uncrewed Autonomous Systems

Estuarine shorelines face the threats of accelerating sea-level rise, recurrent storms, and disruptions of natural sediment and ecological adjustments owing to historic human interventions. The growing availability and technical capability of uncrewed systems (UxS), including remote or autonomous aerial and surface vessels, provide new opportunities to study and understand estuarine shoreline changes. This chapter assesses the state of the technology, interdisciplinary science and engineering literature, and presents case studies from the Chesapeake Bay, Virginia, and coastal North Carolina, USA, that demonstrate new insights into coastal geomorphic processes and applications to managing complex and dynamic estuarine shorelines. These technologies enhance the collection of geospatial environmental data, coastal monitoring, reduce spatial uncertainty, and support measurement of alongshore and onshore/offshore sediment fluxes. Case studies in this chapter highlight scientific insights such as shoreline responses to sea-level rise as well as the practical value of these technologies to develop adaptive management solutions such as living shorelines and nature-based features

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

TRIDENT: A Framework for Autonomous Underwater Intervention

TRIDENT is a STREP project recently approved by the European Commission whose proposal was submitted to the ICT call 4 of the 7th Framework Program. The project proposes a new methodology for multipurpose underwater intervention tasks. To that end, a cooperative team formed with an Autonomous Surface Craft and an Intervention Autonomous Underwater Vehicle will be used. The proposed methodology splits the mission in two stages mainly devoted to survey and intervention tasks, respectively. The project brings together research skills specific to the marine environments in navigation and mapping for underwater robotics, multi-sensory perception, intelligent control architectures, vehiclemanipulator systems and dexterous manipulation. TRIDENT is a three years project and its start is planned by first months of 2010.This work is partially supported by the European Commission through FP7-ICT2009-248497 projec