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

BENTHIC HABITAT MAPPING OF MOUNTAIN TOP BANK WITHIN THE NORTHERN GULF OF MEXICO THROUGH INTEGRATED GEOPHYSICAL AND VISUAL DATA ANALYSIS

Mesophotic coral ecosystems (MCEs) are among the seafloor ecosystems that have been poorly studied throughout the world’s oceans, but they are a vital and diverse ecosystem that should be prioritized for future mapping and ecological studies. Priority should be given to them because they possess natural, social, and economic values, and face a variety of threats, all of which, if not better understood will result in the loss of this unique ecosystem. Insights into these ecosystems, among other deep-sea environments, are lacking due to difficulty accessing them, inherent lag between data collection by an autonomous system and observation by a scientific team, and the vastness of the seafloor. The Gulf of Mexico, a geologically complex environment, has demonstrated the characteristics needed to support MCEs, with reefs such as the Pinnacle Reefs, Flower Garden Banks National Marine Sanctuary (FGBNMS), the Florida Middle Ground reef system, and Pulley Ridge already identified. Mountain Top Bank (MTB), a hardground feature 60-150 m below the sea surface, is a mesophotic reef site off the coast of Mississippi, USA. As it is poorly understood, it is the focus of this study. Bathymetry, backscatter, and photographic ground truthing data were collected by autonomous surface and underwater vehicles (ASV, AUV) and compiled into ArcGIS software to produce a benthic habitat map (BHM) and geodatabase of this site. These data were used to correlate fish and macroinvertebrate presence and abundance with habitat features within a transect atop MTB. This analysis illustrated that the site is characterized by a network of outcrops and boulders interspersed within a predominately sandy environment, with a diverse array of biota including Cnidaria, Porifera, Mollusca, Chordata, Echinodermata, and Rhodophyta. Compiling these data into a BHM and geographic information system (GIS) geodatabase is a powerful way to assess ecosystems and support conservation efforts

Biogeographical Analysis of Abyssal Bottom Habitats: Using an Abiotic Province Scheme and Metazoan Occurrence Databases.

M.A. Thesis. University of Hawaiʻi at Mānoa 2017