An energy-aware architecture : a practical implementation for autonomous underwater vehicles

Energy awareness, fault tolerance and performance estimation are important aspects for extending the autonomy levels of today’s autonomous vehicles. Those are related to the concepts of survivability and reliability, two important factors that often limit the trust of end users in conducting large-scale deployments of such vehicles. With the aim of preparing the way for persistent autonomous operations this work focuses its efforts on investigating those effects on underwater vehicles capable of long-term missions. A novel energy-aware architecture for autonomous underwater vehicles (AUVs) is presented. This, by monitoring at runtime the vehicle’s energy usage, is capable of detecting and mitigating failures in the propulsion subsystem, one of the most common sources of mission-time problems. Furthermore it estimates the vehicle’s performance when operating in unknown environments and in the presence of external disturbances. These capabilities are a great contribution for reducing the operational uncertainty that most underwater platforms face during their deployment. Using knowledge collected while conducting real missions the proposed architecture allows the optimisation of on-board resource usage. This improves the vehicle’s effectiveness when operating in unknown stochastic scenarios or when facing the problem of resource scarcity. The architecture has been implemented on a real vehicle, Nessie AUV, used for real sea experiments as part of multiple research projects. These gave the opportunity of evaluating the improvements of the proposed system when considering more complex autonomous tasks. Together with Nessie AUV, the commercial platform IVER3 AUV has been involved in the evaluating the feasibility of this approach. Results and operational experience, gathered both in real sea scenarios and in controlled environment experiments, are discussed in detail showing the benefits and the operational constraints of the introduced architecture, alongside suggestions for future research directions

TRIDENT: A Framework for Autonomous Underwater Intervention

TRIDENT is a STREP project recently approved by the European Commission whose proposal was submitted to the ICT call 4 of the 7th Framework Program. The project proposes a new methodology for multipurpose underwater intervention tasks. To that end, a cooperative team formed with an Autonomous Surface Craft and an Intervention Autonomous Underwater Vehicle will be used. The proposed methodology splits the mission in two stages mainly devoted to survey and intervention tasks, respectively. The project brings together research skills specific to the marine environments in navigation and mapping for underwater robotics, multi-sensory perception, intelligent control architectures, vehiclemanipulator systems and dexterous manipulation. TRIDENT is a three years project and its start is planned by first months of 2010.This work is partially supported by the European Commission through FP7-ICT2009-248497 projec

The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Sea-floor and sea-ice conditions in the western Weddell Sea, Antarctica, around the wreck of Sir Ernest Shackleton's Endurance

AbstractMarine-geophysical evidence on sea-floor morphology and shallow acoustic stratigraphy are used to examine the substrate around the location at which Sir Ernest Shackleton's ship Endurance sank in 1915 and on the continental slope-shelf sedimentary system above this site in the western Weddell Sea. Few signs of turbidity-current and mass-wasting activity are found near or upslope of the wreck site, and any such activity was probably linked to full-glacial higher-energy conditions when ice last advanced across the continental shelf. The wreck is well below the maximum depth of iceberg keels and will not have been damaged by ice-keel ploughing. The wreck has probably been draped by only a few centimetres of fine-grained sediment since it sank in 1915. Severe modern sea-ice conditions hamper access to the wreck site. Accessing and investigating the wreck of Endurance in the Weddell Sea therefore represents a significant challenge. An ice-breaking research vessel is required, and even this would not guarantee that the site could be reached. Heavy sea-ice cover at the wreck site, similar to that encountered by Agulhus II during the Weddell Sea Expedition 2019, would also make the launch and recovery of autonomous underwater vehicles and remotely operated vehicles deployed to investigate the Endurance wreck problematic.The Flotilla Foundation Marine Archaeological Consultants Switzerlan

Sonar Image Registration for Localization of an Underwater Vehicle

This paper presents a system to provide augmented localization to an AUV equipped with a side scan sonar. Upon revisiting an area, from which side scan data had previously been collected, the system generates an estimate to bound the error in the AUV’s estimate. Localization is accomplished through the comparison of sonar images. Image comparison is based on the extraction of features which characterize local gradient distributions, such as Lowe’s SIFT feature extractor. To resolve potential ambiguities and noise in the image comparison measurement, the localization system incorporates a Bayesian inference algorithm that considers both image based measurement and relative motion to refine the position estimate over time. We describe the particular methods, constraints and augmentations used to apply established image matching and alignment techniques to side scan sonar imagery. By applying consistent geographical corrections to the raw sonar data; using a flat-bottom assumption; and by adding the constraint that images are formed with north aligned up; the traditional problem of full pose estimation is reduced to the two-dimensional case of determining only the x,y translation independent of vehicle altitude. Due to the assumption of constant scale and orientation between images, sensitivity of image feature matching is shown to be controllable by filtering feature matches based on comparing their scale and orientation. This effect was quantified using binary classification analysis. The system’s performance was measured by performing tests on a large side scan survey which represents the familiar terrain that a returning AUV could use for localization