AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Results of expert judgments on the faults and risks with Autosub3 and an analysis of its campaign to Pine Island Bay, Antarctica, 2009

Probabilistic risk assessment is a methodology that can be systematically applied to estimate the risk associated with the design and operation of complex systems. The National Oceanography Centre, Southampton, UK has developed a risk management process tailored to the operation of autonomous underwater vehicles. Central to the application of the risk management process is a probabilistic risk assessment. The risk management process was applied to estimate the risk associated with an Autosub3 science campaign in the Pine Island Glacier, Antarctica, and to support decision making. The campaign was successful. In this paper we present the Autosub3 risk model and we show how this model was used to assess the campaign risk

Subsea inspection and monitoring challenges

Master's thesis in Offshore technology : industrial asset managementThis paper uncovers and suggests solutions for the challenges to control change over time more reliable and cost effective. Front-end concept engineering, design, inspection and monitoring strategies, technologies, systems and methods for Life-of-Field are recommended. Autonomous underwater vehicles (AUV) are identified as a possible cost- efficient opportunity to reduce cost of inspections and monitoring operations while safeguarding asset integrity. A recognized design spiral methodology is used to perform a front-end concept evaluation of an AUV system. Investigation of key technological limitations and new developments within underwater communication, energy storage and wireless power transmission is performed. It further enables opportunities such as AUV recharging station on the seafloor for better utilization. One major learning point is through the use of numerical models and the outcome being a better and more hydro effective hull design. One expectation from this paper may be the aid to collaborating partners in their design work

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