Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles

Abstract — We discuss the problem of learning uncertainty models of ocean processes to assist in the operation of Au-tonomous Underwater Vehicles (AUVs) in the ocean. We focus on the prediction of ocean currents, which have significant effect on the navigation of AUVs. Available models provide accurate prediction of ocean currents, but they typically do not provide confidence estimates of these predictions. We propose augmenting existing prediction methods with variance measures based on Gaussian Process (GP) regression. We show that commonly used measures of variance in GPs do not accurately reflect errors in ocean current prediction, and we propose an alternative uncertainty measure based on interpolation variance. We integrate these measures of uncertainty into a probabilistic planner running on an AUV during a field deployment in the Southern California Bight. Our experiments demonstrate that the proposed uncertainty measures improve the safety and reliability of AUVs operating in the coastal ocean. I

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

DexROV: Enabling effective dexterous ROV operations in presence of communication latency

Subsea interventions in the oil & gas industry as well as in other domains such as archaeology or geological surveys are demanding and costly activities for which robotic solutions are often deployed in addition or in substitution to human divers - contributing to risks and costs cutting. The operation of ROVs (Remotely Operated Vehicles) nevertheless requires significant off-shore dedicated manpower to handle and operate the robotic platform and the supporting vessel. In order to reduce the footprint of operations, DexROV proposes to implement and evaluate novel operation paradigms with safer, more cost effective and time efficient ROV operations. As a keystone of the proposed approach, manned support will in a large extent be delocalized within an onshore ROV control center, possibly at a large distance from the actual operations, relying on satellite communications. The proposed scheme also makes provision for advanced dexterous manipulation and semi-autonomous capabilities, leveraging human expertise when deemed useful. The outcomes of the project will be integrated and evaluated in a series of tests and evaluation campaigns, culminating with a realistic deep sea (1,300 meters) trial in the Mediterranean sea