Technology Demonstration Report for Underwater Survey Equipment

The underwater equipment technology demonstration report, sponsored by GICHD, provides an evaluation of select commercial technology available for the technical survey of sites contaminated with underwater explosive ordnance. The report, along with the GICHD online equipment catalogue, will assist nations, donors and operators with the selection of the most appropriate equipment for their specific tasks

AUV-enabled adaptive underwater surveying for optimal data collection

A new adaptive strategy for performing data collection with a sonar-equipped autonomous underwater vehicle (AUV) is proposed. The approach is general in the sense that it is applicable to a wide range of underwater tasks that rely on subsequent processing of side-looking sonar imagery. By intelligently allocating resources and immediately reacting to the data collected in-mission, the proposed approach simultaneously maximizes the information content in the data and decreases overall survey time. These improvements are achieved by adapting the AUV route to prevent portions of the mission area from being either characterized by poor image quality or obscured by shadows caused by sand ripples. The peak correlation of consecutive sonar returns is used as a measure for image quality. To detect the presence of and estimate the orientation of sand ripples, a new innovative algorithm is developed. The components of the overall data-driven path-planning algorithm are purposely constructed to permit fast real-time execution with only minimal AUV onboard processing capabilities. Experimental results based on real sonar data collected at sea are used to demonstrate the promise of the proposed approach

Multiple Region Coverage Path Planning for Autonomous Underwater Vehicle.

Coverage path planning methodology for an autonomous underwater vehicle to search multiple non-overlapping regions has been proposed in the paper. The proposed methodology is based on the genetic algorithm (GA). The GA used in the proposed methodology has been tuned for the specific problem, using design of experiment on an equivalent travelling salesman problem benchmark instance. Optimality of the generated paths was analysed through simulation studies. Results indicated that the proposed methodology generated shorter paths in comparison to conventional methods

The GLINT10 field trial results

Autonomous underwater vehicles (AUVs) have gained more interest in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into undersea surveillance using AUVs progresses, issues arise as to how an AUV acquires, acts on, and shares information about the undersea battle space. These issues naturally touch on aspects of vehicle autonomy and underwater communications, and need to be resolved through a spiral development process that includes at sea experimentation. This paper presents a recent AUV implementation for active anti-submarine warfare tested at sea in the summer of 2010. On-board signal processing capabilities and an adaptive behavior are discussed in both a simulation and experimental context. The implications for underwater surveillance using AUVs are discussed

Sensor-driven online coverage planning for autonomous underwater vehicles

Abstract-At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally pre-planned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependant on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method. Index Terms-autonomous underwater vehicles, coverage path planning, information gain, hardware-in-the-loop, mine countermeasure, sidescan sonar, adaptive mission plannin

An acoustic remote sensing method for high-precision propeller rotation and speed estimation of unmanned underwater vehicles

Author Posting. © Acoustical Society of America, 2020. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 148(6), (2020): 3942-3950, https://doi.org/10.1121/10.0002954.Understanding the dominant sources of acoustic noise in unmanned underwater vehicles (UUVs) is important for passively tracking these platforms and for designing quieter propulsion systems. This work describes how the vehicle's propeller rotation can be passively measured by the unique high frequency acoustic signature of a brushless DC motor propulsion system and compares this method to Detection of Envelope Modulation on Noise (DEMON) measurements. First, causes of high frequency tones were determined through direct measurements of two micro-UUVs and an isolated thruster at a range of speeds. From this analysis, common and dominant features of noise were established: strong tones at the motor's pulse-width modulated frequency and its second harmonic, with sideband spacings at the propeller rotation frequency multiplied by the poles of the motor. In shallow water field experiments, measuring motor noise was a superior method to the DEMON algorithm for estimating UUV speed. In negligible currents, and when the UUV turn-per-knot ratio was known, measuring motor noise produced speed predictions within the error range of the vehicle's inertial navigation system's reported speed. These findings are applicable to other vehicles that rely on brushless DC motors and can be easily integrated into passive acoustic systems for target motion analysis.This work was supported by the Office of Naval Research (Award No. N00014-17-1-2474), DARPA, the Draper Fellowship, and the National Defense Science and Engineering Graduate Fellowship Program.2021-06-2