We consider the problem of localization and navigation of Autonomous Underwater Vehicles (AUV) in the context of high performance subsea asset inspection missions in deep water. We propose a solution based on the recently introduced Unscented Kalman Filter on Manifolds (UKF-M) for onboard navigation to estimate the robot's location, attitude and velocity, using a precise round and rotating Earth navigation model. Our algorithm has the merit of seamlessly handling nonlinearity of attitude, and is far simpler to implement than the extended Kalman filter (EKF), which is widely used in the navigation industry. The unscented transform notably spares the user the computation of Jacobians and lends itself well to fast prototyping in the context of multi-sensor data fusion. Besides, we provide the community with feedback about implementation, and execution time is shown to be compatible with real-time. Realistic extensive Monte-Carlo simulations prove uncertainty is estimated with accuracy by the filter, and illustrate its convergence ability. Real experiments in the context of a 900m deep dive near Marseille (France) illustrate the relevance of the method.
Service providers for subsea inspection, maintenance, and repair (IMR) generally utilize remotely operated vehicles (ROVs), tethered to a surface vessel and piloted in real-time, to evaluate and manipulate underwater infrastructure. The cost of these operations can be considerable, mostly due to the need to deploy a large surface vessel and crew to support the IMR campaign. Recent progress in marine autonomy, acoustic communication, and artificial intelligence, however, enables new approaches to subsea IMR that could substantially reduce the need for an on-site support vessel and, consequently, the overall cost of these activities. This work describes a novel multi-agent autonomous system comprising an autonomous underwater vehicle (AUV) and unmanned surface vessel (USV). We describe a unique AUV configuration that incorporates a custom high-bandwidth acoustic communication system capable of video transmission to the surface. A state-of-the-art proprietary USV was configured to act as a communication gateway for the AUV, enabling remote mission management from a nearby support vessel. We describe the results of a field test campaign in which real-time video transmission from the AUV was demonstrated at a depth of approximately 900 m. We also describe results from a shallow water test in which the AUV's profiling sonar and integrated lidar system were used to generate 3D maps of a wreck of a World War 2 fighter plane.
This thesis studies the benefits of using opportunistic routing, implicit acknowledgments, and network coding on a linear broadcast packet network. Nodes are arranged in a line, and the first node wishes to communicate with the end node. When node i transmits, it is received at node j with a probability P i,j . Several communication protocols are proposed and their performance studied using the mean and variance of the completion time as metrics. The protocols studied use end-to-end retransmission, end-to-end coding, and link-by-link retransmission with network coding both with and without opportunistic routing. Simulation and analytical results are presented.End-to-end coding significantly outperforms end-to-end retransmission on both metrics, and the link-by-link protocols outperform both. Opportunistic routing shows a mixed benefit over link-by-link protocols without it. When using opportunistic routing, the variance of the completion time is higher, and the mean is either similar or lower, depending on the channel conditions. When the loss probabilities are higher, opportunistic routing shows little benefit, whereas with a lower probability of packet loss, opportunistic routing shows a significant reduction in mean completion time.
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