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

Soil Oxygen Monitoring with Fibre Optode Sensors: Experimental Evaluation in Soil Columns under Fluctuating Water Table and Freeze Thaw Conditions

The biogeochemical functioning of natural and engineered environments is closely linked to spatial and temporal variations in molecular oxygen (O2) concentrations. A luminescence-based, Multi-Fibre Optode (MuFO) microsensor technique was developed to measure O2 concentrations in fully and partially water-saturated systems. The technique relies on the conversion of high-resolution digital images of sensor-emitted light into O2 concentrations using the classical Stern-Volmer (SV) and Lehrer equations. The method was tested in two soil column experiments designed to simulate water table fluctuations (WT) and freeze-thaw cycles (FTC) under controlled conditions. The columns were filled with a homogenized mixture of peat (20 %) and sand (80 %). Depth distributions of O2 concentration were monitored without interruption for 20 (WT experiment) and 39 days (FTC experiment), while CO2 effluxes from the soils were measured periodically. Increases in CO2 emission accompanied both thawing (FTC experiment) and water table drawdown (WT experiment). During freezing, O2 levels in the unsaturated depth interval of the soil dropped by up to 20% because of more restricted gas exchanges with the atmosphere. The CO2 pulses in the FTC experiment were therefore attributed to the build-up of respiratory CO2 in the pore space during freezing, and its subsequent release upon thawing. In the WT experiment, the lowering (rate of 12 cm day-1 over 2.5 days) of the water table allowed for O2 migration deeper into the soil, which enhanced the aerobic mineralization of peat organic matter and, consequently, increased the emission of CO2. In both the FTC and WT experiments, the magnitude of the CO2 pulse decreased (from 0.26 μmol2 s-1 to 0.07 μmol2 s-1) with each subsequent water table and freeze-thaw cycle, indicating the progressive depletion of reactive organic carbon. No degradation of optode performance and O2 signals were observed over the entire duration of the experiments, hence supporting the long-term deployment of the microsensors for continuous O2 monitoring in field and laboratory settings

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