This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model.

Increased ocean temperatures caused by climate change are expected to lead to more frequent and severe harmful algal blooms, which deteriorate water quality, cause human illness and fish mortality. Scientific understanding of algal blooms and their dynamics is limited due to the lack of data from such ocean phenomena. State-of-the-art ocean monitoring includes satellite imagery and dedicated research vessels. Mobile sensors based on autonomous underwater vehicles (AUVs) and other robotic technologies are of growing importance for efficient environmental monitoring of the oceans. The overall objective of this thesis is to design a system for ocean feature estimation and tracking based on adaptive sensor sampling using AUVs. The thesis contributions are focused on the following three problems.

 The first problem we consider is how to estimate and track circular and non-circular ocean features using a multi-robot system. We propose a circumnavigation control law, proving that it forces the AUVs to converge to a circular formation. Two target estimation algorithms are presented: one is based on a leader-follower approach, while the other is distributed. Both algorithms are shown to successfully estimate and track the mobile target's location. Secondly, we consider the problem of tracking ocean fronts using a single AUV supported by satellite data. We develop a Gaussian process model for the front estimation and show how it can be updated based on the available sensor and satellite data. Using this model, a control law is developed that guides the AUV to move toward and along the ocean front. The closed-loop system is evaluated through a detailed simulation environment with realistic vehicle and environment models and real algal bloom data. Finally, we develop an experimental setup based on a real AUV to demonstrate that our method for algal bloom tracking is feasible in practice. We show experimental results from two surveys in the Stockholm archipelago and compare the performance of the real system with simulation studies. The results indicate that the front tracking and gradient estimation algorithms are working well but also suggest important items for further studies.

Stigande havstemperaturer på grund av klimatförändringar förväntas ledatill fler och allvarligare algblomningar som kommer att försämra vattenkvaliten,men också orsaka sjukdomar bland människor och dödsfall bland fiskar. Samtidigtär den vetenskapliga förståelsen för algblomning och dess utbredningsmekanismerbegränsad, och det finns ont om information om detta fenomen. Idagsläget görs havsövervakning med bland annat satelitbilder och dedikeradeforskningsfarkoster. Mobila sensorer på autonoma undervattensfarkoster (autonomousunderwater vehicles, AUVs) och andra robotikmetoder har därmedbörjat spela en större roll i att uppnå en effektiv klimatövervakning i våra vatten.Det övergripande målet med denna avhandling är att designa ett system fördetektering och spårning av havsföremål med hjälp av adaptiv sensorprovtagningmed AUVs. Avhandlingens bidrag fokuserar på de följande tre problemen.Det första problemet som behandlas är hur cirkulära och icke-cirkulära havsföremålkan detekteras och spåras av ett system med flera robotar. Vi föreslåren regleralgoritm som bygger på kringsegling, och bevisar hur det resulterar iatt alla AUVs konvergerar till en cirkulär formation. Två målestimeringsalgoritmerpresenteras: den ena bygger på en ledare-följare-metod och den andra påen distribuerad metod. Vi demonstrerar att båda algoritmerna kan detektera detrörliga målet och spåra dess position.Det andra problemet som behandlas är spårning av havsfronter med en ensamAUV som har tillgång till satelitinformation. Vi använder en gaussisk processför att modellera fronter och visar att den kan uppdateras med data frånsensorer och sateliter. Modellen används följaktligen till att ta fram en regleralgoritmsom styr en AUV till fronten och sedan följer den. Det återkoppladesystemet utvärderas i simuleringar med realistiska modeller för farkost och vattenmiljötillsammans med verklig algblomningsdata.Slutligen utvecklar vi en experimentplatform baserat på en riktig AUV föratt demonstera att spårningen av algblomning fungerar i verkligheten. Resultatfrån två experiment in Stockholms skärgård presenteras, och det verkliga systemetsprestanda jämförs med prestandan i simulering. Resultaten indikerar attalgoritmerna för frontspårning och gradientestimering fungerar väl, men lyfteräven fram frågor som bör besvaras i framtida studier.

The problem of the concurrent tracking and mapping of a river plume front with an autonomous underwater vehicle (AUV) is formulated and addressed in the framework of an interdisciplinary approach building on experience in robotics and oceanographic field studies. The problem formulation is targeted at the scientific study of the processes by which the river and the ocean interact. The approach extends previous work in AUV plume tracking to the simultaneous tracking and mapping under different ocean and meteorological conditions. This is done with the help of parameterizable motion control algorithms to enable adaptation to these time-varying conditions. The approach is evaluated in simulation with the help of a high-resolution hydrodynamic model. The test plan covers over 300 test cases exercising the most representative combinations of the ocean and meteorological conditions. Lessons learned and future operational deployments are discussed in the conclusions.

We consider the problem of tracking moving algal bloom fronts using an unmanned surface vehicle (USV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a is a green pigment found in plants, and its concentration is an indicator of phytoplankton abundance. Our algal bloom front tracking mission consists of three stages: deployment, data collection, and front tracking. At the deployment stage, a satellite collects an image of the sea from which the location of the front, the reference value for the concentration at this front and, consequently, the appropriate initial position for the USV are determined. At the data collection stage, the USV collects data points to estimate the local algal gradient as it crosses the front. Finally, at the front tracking stage, an adaptive algorithm based on recursive least squares fitting using recent past sensor measures is executed. We evaluate the performance of the algorithm and its sensitivity to measurement noise through MATLAB simulations. We also present an implementation of the algorithm on the DUNE onboard software platform for marine robots and validate it using simulations with satellite model forecasts from Baltic sea data.

In this paper we consider the problem of tracking a mobile target using adaptive estimation while circumnavigating it with a system of Unmanned Surface Vehicles (USVs). The mobile target considered is an irregular dynamic shape approximated by a circle with moving centre and varying radius. The USV system is composed of n USVs of which one is equipped with an Unmanned Aerial Vehicle (UAV) capable of measuring both the distance to the boundary of the target and to its centre. This USV equipped with the UAV uses adaptive estimation to calculate the location and size of the mobile target. The USV system must circumnavigate the boundary of the target while forming a regular polygon. We design two algorithms: One for the adaptive estimation of the target using the UAV’s measurements and another for the control protocol to be applied by all USVs in their navigation. The convergence of both algorithms to the desired state is proved up to a limit bound. Two simulated examples are provided to verify the performance of the algorithms designed in this paper.

Harmful algal blooms occur frequently and deteriorate water quality. A reliable method is proposed in this paper to track algal blooms using a set of autonomous surface robots. A satellite image indicates the existence and initial location of the algal bloom for the deployment of the robot system. The algal bloom area is approximated by a circle with time varying location and size. This circle is estimated and circumnavigated by the robots which are able to locally sense its boundary. A multi-agent control algorithm is proposed for the continuous monitoring of the dynamic evolution of the algal bloom. Such algorithm comprises of a decentralised least squares estimation of the target and a controller for circumnavigation. We prove the convergence of the robots to the circle and in equally spaced positions around it. Simulation results with data provided by the SINMOD ocean model are used to illustrate the theoretical results.

This paper investigates using satellite data to improve adaptive sampling missions, particularly for front tracking scenarios such as with algal blooms. Our proposed solution to find and track algal bloom fronts uses an Autonomous Underwater Vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a and satellite data. The proposed method learns the kernel parameters for a Gaussian process (GP) model using satellite images of chlorophyll a from the previous days. Then, using the data collected by the AUV, it models chlorophyll a concentration online. We take the gradient of this model to obtain the direction of the algal bloom front and feed it to our control algorithm. The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor. We compare the performance of different estimation methods, from GP to curve interpolation using least squares. Sensitivity analysis is performed to evaluate the impact of sensor noise on the methods’ performance. We implement our method on an AUV and run experiments in the Stockholm archipelago in the summer of 2022.