OceanScout - Developing a compact, affordable, fleet-capable autonomous glider

Autonomous underwater gliders are robust and widely-used ocean sampling platforms that are characterized by their endurance, and are one of the best approaches to gather subsurface data at the appropriate spatial resolution to advance our knowledge of the ocean environment. Gliders generally do not employ sophisticated sensors for underwater localization, but instead dead-reckon between set waypoints. Thus, these vehicles are subject to large positional errors between prescribed and actual surfacing locations. Here, we investigate the implementation of a large-scale, regional ocean model into the trajectory design for autonomous gliders to improve their navigational accuracy. We compute the dead-reckoning error for our Slocum gliders, and compare this to the average positional error recorded from multiple deployments conducted over the past year. We then compare trajectory plans computed on-board the vehicle during recent deployments to our prediction-based trajectory plans for 140 surfacing occurrences.

The knowledge of the position of underwater vehicles is fundametal to perform most of the missions. With the absence of the Global Positioning System (GPS) underwater, there is a need to find an alternative solution. This paper presents a methodology to improve the navigation of underwater gliders using a collaborative framework called Twin Autonomous Gliders (TAG). TAG, consists in deploying two preferably twin gliders (same manufacturer and external/internal configuration) that are bound by a possibly acoustic-based relative positioning system – e.g Ultra Short BaseLine (USBL). Thanks to the relative positioning system, the gliders are controlled in a leader follower formation. The main principle of the approach is that this setup can be used to estimate and track the sea current, which is one of the major sources of error in underwater glider navigation. Knowing the relative velocity between the two gliders (measured using the relative positioning system) and the velocity of each glider relative to the water (given by the hydrodynamic model of each vehicle), it is possible to measure the difference in sea current between the two locations of the gliders. This difference can be used to estimate the sea current. The paper shows simulation results using a realistic mediterranean sea model, realistic glider model and realistic sensor data based on Commercial Off The Shelf (COTS) equipment specifications and empirical properties.

Mature Urban Air Mobility (UAM) operations will require operators to manage multiple uncrewed and increasingly autonomous aircraft simultaneously. Operators will supervise automated operations rather than directly control individual aircraft, with monitoring as their primary task. Exploring this operational paradigm, we conducted a study to understand the effects of interruptions and set size (i.e., number of vehicles) on operators' situation awareness. In this study, participants performed a multi-vehicle monitoring task while eye movements were recorded. Eye tracking data were filtered and processed, and various metrics were computed to characterize and better understand eye gaze behavior of operators during multi-vehicle monitoring. The present analyses primarily tested set-size effects on eye tracking metrics but also explored the efficacy of using gaze entropy metrics to assess monitoring performance. Results indicated relationships between gaze entropy metrics and set size. Additionally, the findings suggest that the multi-vehicle monitoring task can be treated as a multiple identity tracking (MIT) task rather than a multiple object tracking (MOT) task. This exploratory research provides valuable insights for future research focused on developing passive performance metrics for multi-vehicle monitoring.

The science of autonomy is the systematic development of fundamental knowledge about autonomous decision making and task completing in the form of testable autonomous methods, models and systems. In ocean applications, it involves varied disciplines that are not often connected. However, marine autonomy applications are rapidly growing, both in numbers and in complexity. This new paradigm in ocean science and operations motivates the need to carry out interdisciplinary research in the science of autonomy. This chapter reviews some recent results and research directions in time-optimal path planning and optimal adaptive sampling. The aim is to set a basis for a large number of vehicles forming heterogeneous and collaborative underwater swarms that are smart, i. e., knowledgeable about the predicted environment and their uncertainties, and about the predicted effects of autonomous sensing on future operations. The methodologies are generic and applicable to any swarm that moves and senses dynamic environmental fields. However, our focus is underwater path planning and adaptive sampling with a range of vehicles such as autonomous underwater vehicles (AUV s), gliders, ships or remote sensing platforms.

[1] The 2009–2010 El Nino marked the first occurrence of this climate phenomenon since the initiation of sustained autonomous glider surveillance in the California Current System (CCS). Spray glider observations reveal the subsurface effects of El Nino in the CCS with spatial and temporal resolutions that could not have been obtained practically with any other observational method. Glider observations show that upper ocean waters in the CCS were unusually warm and isopycnals were abnormally deep during the El Nino event, but indicate no anomalous water masses in the region. Observed oceanic anomalies in the CCS are nearly in phase with an equatorial El Nino index and local anomalies of atmospheric forcing. These observations point toward an atmospheric teleconnection as an important mechanism for the 2009–2010 El Nino's remote effect on the midlatitude CCS.