Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of FIeld Robotics 35 (2018): 421-434, doi:10.1002/rob.21746.This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.This work was supported in part through funding from the Weston Howland Jr. Postdoctoral Scholar Award (BCC), the U.S. Navy's Civilian Institution program via the MIT/WHOI Joint Program (JHK),W. M. Keck Institute for Space Studies, and theWoods Hole Oceanographic Institution

Toward autonomous exploration in confined underwater environments

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring- Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762), and the National Science Foundation (OCE-0955674)

AN INTEGRATED SIMULATION APPROACH FOR AUV IMAGE-BASED SLAM NAVIGATION

This thesis develops a simulation framework for undersea feature-based navigation. Using an autonomous underwater vehicle (AUV) to locate an item of interest on the seafloor is a capability that would greatly benefit the Navy. AUVs provide a gateway toward removing the workforce requirement; however, they are still costly both in acquisition and maintenance. A solution to this problem is using two AUVs, one with increased capability and charged with finding and marking seafloor items with a beacon. An expendable AUV outfitted with cost-effective sensors would relocate, identify and neutralize the threat. Using undersea imaging to correlate seafloor images to an a priori image mosaic together with a ultra short baseline (USBL) beacon allows the AUV to complete challenging mission objectives without traditional navigation systems. Incremental Smoothing and Mapping 2 (iSAM2) is a Simultaneous Localization and Mapping (SLAM) technique that can be used by the AUV for position localization and is an appropriate technique, with image and USBL sensing, for real-time navigation operations. A simulation framework provides the ability to evaluate an AUV's performance while minimizing the risk of real-world operations. The framework is composed of a software architecture that allows for testing using the same software applied in real-world operations. This thesis demonstrates this framework and provides analysis for its usability for image-based SLAM.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Underwater mapping using a SONAR

Este estudo explora a capacidade física do raio de um sonar mecânico para construir um mapa do ambiente envolvente e encontrar a localização do veículo nesse mesmo mapa. Os dados do sonar alimentam um extrator de pontos de interesse e um algoritmo SLAM. Esse algoritmo é composto por uma implementação de Octomaps, junto com um filtro de partículas. Vários testes foram executados dentro de um ambiente estruturado e os resultados desses testes são demonstrados neste estudo.This study explores the physical capabilities of the beam of a mechanical scanning imaging sonar to build a map of the surrounding environment and to find the location of the vehicle within the map. The data from the sonar feed into a feature extractor and a SLAM algorithm. The SLAM algorithm is composed of an Octomaps implementation together with a particle filter. Several tests were ran within a structured environment and the map of the structured environment as well as the location of the vehicle are presented

Terrain-aided navigation for long-range AUVs in dynamic under-mapped environments

Deploying long‐range autonomous underwater vehicles (AUVs) mid‐water column in the deep ocean is one of the most challenging applications for these submersibles. Without external support and speed over the ground measurements, dead‐reckoning (DR) navigation inevitably experiences an error proportional to the mission range and the speed of the water currents. In response to this problem, a computationally feasible and low‐power terrain‐aided navigation (TAN) system is developed. A Rao‐Blackwellized Particle Filter robust to estimation divergence is designed to estimate the vehicle's position and the speed of water currents. To evaluate performance, field data from multiday AUV deployments in the Southern Ocean are used. These form a unique test case for assessing the TAN performance under extremely challenging conditions. Despite the use of a small number of low‐power sensors and a Doppler velocity log to enable TAN, the algorithm limits the localisation error to within a few hundreds of metres, as opposed to a DR error of 40 km, given a 50 m resolution bathymetric map. To evaluate further the effectiveness of the system under a varying map quality, grids of 100, 200, and 400 m resolution are generated by subsampling the original 50 m resolution map. Despite the high complexity of the navigation problem, the filter exhibits robust and relatively accurate behaviour. Given the current aim of the oceanographic community to develop maps of similar resolution, the results of this study suggest that TAN can enable AUV operations of the order of months using global bathymetric models

Pause-and-Go Self-Balancing Formation Control of Autonomous Vehicles Using Vision and Ultrasound Sensors

In this work, we implement a decentralized and noncooperative state estimation and control algorithm to autonomously balance a team of robots in a circular formation pattern. The group of robots includes a leader periodically moving at a constant steering angle and a set of followers that, by only leveraging intermittent and noisy proximity measurements, independently implement a fully decentralized state estimation control algorithm to determine and adjust their relative position with closest neighbors. The algorithm is conducted in a pause-and-go sequence, where, during the pause, each robot stops to gather and process the information coming from the measurements, estimate the relative phase with respect to the others, and identify its closest pursuant. During the go, each robot accelerates to space from its closest pursuant and then to move at a constant speed when the desired spacing is achieved. The algorithm is tested in an unprecedented experiment on a custom-made low-cost caster-wheeled robotic framework featuring sonar and vision sensors mounted on a rotating platform to estimate the proximity distance to closer neighbors. The control scheme, which does not necessitate cooperation and is capable of coping with uncertain and intermittent sensor feedback data, is shown to be effective in balancing the robot on the circle even when, at a steady state, no feedback sensor data are available