A planned approach to high collision risk area

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2020.This thesis examines the transition of a vessel from the open ocean, where collisions are rare, to a high risk and heavy traffic area such as a Traffic Separation Scheme (TSS). Previous autonomy approaches generally view path planning and collision avoidance as two separate functions, i.e. a vessel will follow the planned path until conditions are met for collision avoidance algorithms to take over. Here an intermediate phase is proposed with the goal of adjusting the time of arrival to a high vessel density area so that the risk of collision is reduced. A general algorithm that calculates maximum future traffic density for all choices in the speed domain is proposed and implemented as a MOOS-IvP behavior. This behavior gives the vessel awareness of future collision risks and aids the collision avoidance process. This new approach improves the safety of the vessel by reducing the number of risky encounters that will likely require the vessel to maneuver for safety

Evaluation of an acoustic detection algorithm for reactive collision avoidance in underwater applications

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (page 33).This thesis sought to evaluate a vehicle detection algorithm based on a passive acoustic sensor, intended for autonomous collision avoidance in Unmanned Underwater Vehicles. By placing a hydrophone at a safe distance from a dock, it was possible to record the acoustic signature generated by a small motor boat as it navigated towards, and then away from the sensor. The time-varying sound intensity was estimated by Root Mean Square of the sound amplitude in discrete samples. The time-derivative of the sound intensity was then used to estimate the time to arrival, or collision, of the acoustic source. The algorithm was found to provide a good estimate of the time to collision, with a small standard deviation for the projected collision time, when the acoustic source was moving at approximately constant speed, providing validation of the model at the proof-of-concept level.by Oscar Alberto Viquez Rojas.S.B

An ensemble of online estimation methods for one degree-of-freedom models of unmanned surface vehicles: applied theory and preliminary field results with eight vehicles

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.Comment: v1) 8 Pages, 5 Figures, To appear at 2023 RSJ/IEEE Conference on Intelligent Robotics and Systems (IROS) in Detroit, Michigan, USA, v2) corrected error in reference

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

Demonstration of passive acoustic detection and tracking of unmanned underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2018In terms of national security, the advancement of unmanned underwater vehicle (UUV) technology has transformed UUVs from tools for intelligence, surveillance, and reconnaissance and mine countermeasures to autonomous platforms that can perform complex tasks like tracking submarines, jamming, and smart mining. Today, they play a major role in asymmetric warfare, as UUVs have attributes that are desirable for less-established navies. They are covert, easy to deploy, low-cost, and low-risk to personnel. The concern of protecting against UUVs of malicious intent is that existing defense systems fall short in detecting, tracking, and preventing the vehicles from causing harm. Addressing this gap in technology, this thesis is the first to demonstrate passively detecting and tracking UUVs in realistic environments strictly from the vehicle’s self-generated noise. This work contributes the first power spectral density estimate of an underway micro-UUV, field experiments in a pond and river detecting a UUV with energy thresholding and spectral filters, and field experiments in a pond and river tracking a UUV using conventional and adaptive beamforming. The spectral filters resulted in a probability of detection of 96% and false alarms of 18% at a distance of 100 m, with boat traffic in a river environment. Tracking the vehicle with adaptive beamforming resulted in a 6.2±5.7 ∘ absolute difference in bearing. The principal achievement of this work is to quantify how well a UUV can be covertly tracked with knowledge of its spectral features. This work can be implemented into existing passive acoustic surveillance systems and be applied to larger classes of UUVs, which potentially have louder identifying acoustic signatures.Support from the National Defense Science and Engineering Graduate Fellowship and Draper Labs Fellowship, as well as DARPA for the support of the Bluefin Sandshark unmanned underwater vehicle. This research was conducted with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a

Constructing a distributed AUV network for underwater plume-tracking operations

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in International Journal of Distributed Sensor Networks 2012 (2012): 191235, doi:10.1155/2012/191235.In recent years, there has been significant concern about the impacts of offshore oil spill plumes and harmful algal blooms on the coastal ocean environment and biology, as well as on the human populations adjacent to these coastal regions. Thus, it has become increasingly important to determine the 3D extent of these ocean features (“plumes”) and how they evolve over time. The ocean environment is largely inaccessible to sensing directly by humans, motivating the need for robots to intelligently sense the ocean for us. In this paper, we propose the use of an autonomous underwater vehicle (AUV) network to track and predict plume shape and motion, discussing solutions to the challenges of spatiotemporal data aliasing (coverage versus resolution), underwater communication, AUV autonomy, data fusion, and coordination of multiple AUVs. A plume simulation is also developed here as the first step toward implementing behaviors for autonomous, adaptive plume tracking with AUVs, modeling a plume as a sum of Fourier orders and examining the resulting errors. This is then extended to include plume forecasting based on time variations, and future improvements and implementation are discussed.This research was made with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a

Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots

In this paper, a hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication. The adaptation framework is implemented within the behavior-based mission-oriented operating suite-interval programming (MOOS-IvP) marine autonomy architecture and uses a new embedded high-fidelity acoustic modeling infrastructure, the generic robotic acoustic model (GRAM), to provide real-time estimates of the acoustic environment under changing environmental and situational scenarios. A set of behaviors that combine adaptation to the current acoustic environment with strategies that extend the decision horizon beyond that of typical behavior-based systems have been developed, implemented, and demonstrated in a series of field experiments and virtual experiments in a MOOS-IvP simulation.United States. Office of Naval Research (Grant N00014-08-1-0011)United States. Office of Naval Research (Grant N00014-08-1-0013)NATO Undersea Research Centre (NURC

Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey