No abstract

New robotic sensor platforms have computing resources that enable a rich set of tasks for adaptive monitoring of the environment. But to substantially augment the toolbox of environmental sensing, such platforms must be embedded with realistic statistical models and coherent methodologies for designing experiments and assimilating the data. In this article, we develop myopic and hybrid strategies for autonomous underwater vehicle sampling in space and time. These strategies are based on a stochastic advection‐diffusion Gaussian process model for the mine tailings concentration in a Norwegian fjord, and the goal is to monitor the excursion set (ES) of high concentrations. Closed form expressions for the expected misclassification probabilities of the ES enable real‐time operation on board the autonomous vehicle, and this is used to guide the spatio‐temporal sampling. Simulation studies show that the suggested strategies outperform other approaches that either (i) simplify the models for spatio‐temporal variation, or (ii) simplify the design criterion. A field test shows how autonomous underwater sampling is useful for refining an initial stochastic advection‐diffusion model. These experiments further show that the vehicle can adapt to focus on regions with intermediate concentrations where it is natural to improve the ES prediction.

Discharge of mine tailings significantly impacts the ecological status of the sea. Methods to efficiently monitor the extent of dispersion is essential to protect sensitive areas. By combining underwater robotic sampling with ocean models, we can choose informative sampling sites and adaptively change the robot’s path based on in situ measurements to optimally map the tailings distribution near a seafill. This paper creates a stochastic spatio-temporal proxy model of dispersal dynamics using training data from complex numerical models. The proxy model consists of a spatio-temporal Gaussian process model based on an advection–diffusion stochastic partial differential equation. Informative sampling sites are chosen based on predictions from the proxy model using an objective function favoring areas with high uncertainty and high expected tailings concentrations. A simulation study and data from real-life experiments are presented.

Cooperative, connected, and automated mobility (CCAM) can lead to a significantly improved transport system by increasing safety and efficiency, and reducing emissions. To achieve the goal of fully automated mobility and self-driving vehicles, accurate and reliable positioning is essential. Positioning methods in CCAM often use sensor fusion combining data from multiple sensors with Global navigation satellite system (GNSS) positioning data. In this paper, we focus on the status of GNSS technology by investigating position accuracies and integrities of different state-of-the-art GNSS technologies. We conduct field tests using a self-driving vehicle in Drammen, Norway. Three different types of GNSS positioning services are explored, and a reference trajectory delivered by the vehicle's navigation system is used to determine the performance of each service. We show that the performance of the GNSS methods alone does not fulfill the requirements needed to obtain fully automated mobility. Moreover, we observe a general decreasing trend in GNSS accuracy for more challenging surroundings.

To get a better understanding of the highly nonlinear processes driving the ocean, efficient and informative sampling is critical. By combining robotic sampling with ocean models we are able to choose informative sampling sites and adaptively change our path based on measurements. We present models exploiting prior information from ocean models as well as real-time information from in situ measurements. The method uses compact Gaussian process modeling and objective functions to locate informative sampling sites. Our aim is to get a better understanding of ocean processes and improve real-time monitoring of dispersal dynamics. The case study focuses on a fjord located in Norway containing a seafill for mine tailings. Transportation of the deposited particles are studied, and the sampling method is tested in the area. The results from these sea trials are presented.