On Collaborative Aerial and Surface Robots for Environmental Monitoring of Water Bodies

Part 8: Robotics and ManufacturingInternational audienceRemote monitoring is an essential task to help maintaining Earth ecosystems. A notorious example is the monitoring of riverine environments. The solution purposed in this paper is to use an electric boat (ASV - Autonomous Surface Vehicle) operating in symbiosis with a quadrotor (UAV – Unmanned Air Vehicle). We present the architecture and solutions adopted and at the same time compare it with other examples of collaborative robotics systems, in what we expected could be used as a survey for other persons doing collaborative robotics systems. The architecture here purposed will exploit the symbiotic partnership between both robots by covering the perception, navigation, coordination, and integration aspects

Advances in Decentralized Single-Beacon Acoustic Navigation for Underwater Vehicles: Theory and Simulation

This paper reports the theory and implementation of a decentralized navigation system that enables simultaneous single-beacon navigation of multiple underwater vehicles. In single-beacon navigation, each vehicle uses ranges from a single, moving reference beacon in addition to its own inertial navigation sensors to perform absolute localization and navigation. In this implementation the vehicles perform simultaneous communication and navigation using underwater acoustic modems, encoding and decoding data within the acoustic broadcast. Vehicles calculate range from the time of flight of asynchronous acoustic broadcasts from the reference beacon. Synchronous clocks on the reference beacon and the vehicles enable the measurement of one-way travel-times, whereby the time of launch of the acoustic signal at the reference beacon is encoded in the acoustic broadcast and the time of arrival of the broadcast is measured by each vehicle. The decentralized navigation algorithm, running independently on each vehicle, is implemented using the information form of the extended Kalman filter and has been previously shown to yield results that are identical to a centralized Kalman filter at the instant of each range measurement. We summarize herein the architecture and design of the acoustic communications (Acomms) system consisting of an underwater acoustic modem, synchronous clock, and the software necessary to run them, and salient results from the validation of the decentralized information filter using a simulated data set.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86057/1/swebster-4.pd

Underwater vehicle localization using range measurements

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 79-83).This thesis investigates the problem of cooperative navigation of autonomous marine vehicles using range-only acoustic measurements. We consider the use of a single maneuvering autonomous surface vehicle (ASV) to aid the navigation of one or more submerged autonomous underwater vehicles (AUVs), using acoustic range measurements combined with position measurements for the ASV when data packets are transmitted. The AUV combines the data from the surface vehicle with its proprioceptive sensor measurements to compute its trajectory. We present an experimental demonstration of this approach, using an extended Kalman filter (EKF) for state estimation. We analyze the observability properties of the cooperative ASV/AUV localization problem and present experimental results comparing several different state estimators. Using the weak observability theorem for nonlinear systems, we demonstrate that this cooperative localization problem is best attacked using nonlinear least squares (NLS) optimization. We investigate the convergence of NLS applied to the cooperative ASV/AUV localization problem. Though we show that the localization problem is non-convex, we propose an algorithm that under certain assumptions (the accumulative dead reckoning variance is much bigger than the variance of the range measurements, and that range measurement errors are bounded) achieves convergence by choosing initial conditions that lie in convex areas. We present experimental results for this approach and compare it to alternative state estimators, demonstrating superior performance.by Georgios Papadopoulos.S.M

Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts

Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-106).Autonomous operations are vital to future naval operations. Unmanned systems, including autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs), are anticipated to play a key role for critical tasks such as mine countermeasures (MCM) and anti-submarine warfare (ASW). Addressing these issues with autonomous systems poses a host of difficult research challenges, including sensing, power, acoustic communications, navigation, and autonomous decision-making. This thesis addresses the issues of sensing and autonomy, studying the benefits of adaptive motion in overcoming partial observability of sensor observations. We focus on the challenge of target tracking with range-only measurements, relying on adaptive motion to localize and track maneuvering targets. Our primary contribution has been to develop new MOOS-IvP autonomy and state estimation modules to enable an autonomous surface vehicle to locate and track a submerged contact using range-only sensor information. These capabilities were initially tested in simulation for increasing levels of complexity of target motion, and subsequently evaluated in a field test with a Kingfisher ASV. Our results demonstrate the feasibility, in a controlled environment, to localize and track a maneuvering undersea target using range-only measurements.by Andrew J. Privette.S.M.Nav.E

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2013Autonomous marine vehicles are increasingly used in clusters for an array of oceanographic tasks. The effectiveness of this collaboration is often limited by communications: throughput, latency, and ease of reconfiguration. This thesis argues that improved communication on intelligent marine robotic agents can be gained from acting on knowledge gained by improved awareness of the physical acoustic link and higher network layers by the AUV’s decision making software. This thesis presents a modular acoustic networking framework, realized through a C++ library called goby-acomms, to provide collaborating underwater vehicles with an efficient short-range single-hop network. goby-acomms is comprised of four components that provide: 1) losslessly compressed encoding of short messages; 2) a set of message queues that dynamically prioritize messages based both on overall importance and time sensitivity; 3) Time Division Multiple Access (TDMA) Medium Access Control (MAC) with automatic discovery; and 4) an abstract acoustic modem driver. Building on this networking framework, two approaches that use the vehicle’s “intelligence” to improve communications are presented. The first is a “non-disruptive” approach which is a novel technique for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. This system was analyzed on experimental data and implemented on a fielded vehicle. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved. The second approach is “disruptive,” as it changes the vehicle’s course to effect an improvement in the communications channel. 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.I wish to acknowledge the sponsors of this research for their generous support of my tuition, stipend, and research: the WHOI/MIT Joint Program, the MIT Presidential Fellowship, the Office of Naval Research (ONR) # N00014-08-1-0011, # N00014-08-1-0013, and the ONR PlusNet Program Graduate Fellowship, the Defense Advanced Research Projects Agency (DARPA) (Deep Sea Operations: Applied Physical Sciences (APS) Award # APS 11-15 3352-006, APS 11-15-3352-215 ST 2.6 and 2.7

Modelos baseados em funções Kernel para localização de veículos autônomos subaquáticos

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.Esta tese aborda a análise do sistema de localização acústica de veículos subaquáticos em uma configuração dita de base longa auxiliada por sensores inerciais. Para o processo de filtragem de dados, o filtro de Kalman, em sua versão estendida EKF (Extended Kalman Filter), é utilizado de modo a aproveitar toda informação relacionada aos estados do veículo proveniente dos sensores. O foco do trabalho está no processo de aprendizagem a partir de dados com vistas à identificação das medições errôneas do tempo de chegada da sonda sonora e à correção das mesmas. As técnicas exploradas para essas finalidades são o AAKR (AutoAssociative Kernel Regression) e o SVDD (Support Vector Data Description). O objetivo é melhorar a estimação dos estados do veículo (posição, velocidade e orientação) provenientes dos sensores inerciais, aproveitando um conjunto de medições corretas obtidas durante a navegação ou em missões anteriores à atual. A melhoria no desempenho do sistema de localização foi analisado por simulação utilizando dados experimentais obtidos em missões com veículos de baixo custo e modelos de propagação acústica que inserem desvios factíveis aos tempos de chegada medidos. Os resultados são comparados ao desempenho obtido com uma solução clássica para a localização acústica de veículos autônomos em ambiente subaquático. Destaca-se ainda que a arquitetura proposta não se apresenta firmemente acoplada ou fortemente dependente de qualquer outro algoritmo presente no veículo, o que a caracteriza como uma solução bastante modular com a possibilidade de estendê-la a outras aplicações.Abstract : This thesis deals with the analysis of an acoustic localization system in a long baseline configuration for navigation of underwater vehicles aided by inertial sensors. For the process of filtering data, the Extended Kalman Filter (EKF) is used in order to take advantage of all information related to the states of the vehicle from the sensors. The focus is on the process of learning from data with a view to identify erroneous measurements of the time of flight of the acoustic signal and correct eventual deviations in this quantity. The techniques used for these purposes are AAKR (AutoAssociative Kernel Regression) and SVDD (Support Vector Data Description). The objective is to improve the accuracy of the estimates of the vehicle s states (position, velocity and orientation) coming from the inertial sensors, taking advantage of a set of correct measurements obtained during prior navigations or in the current missions. The improved performance of the tracking system is evidenced by the data obtained using in field missions with low-cost vehicles and acoustic propagation models that insert feasible deviations to arrival times measured. The results are compared with the performance obtained with other classical solution to acoustic localization task in underwater environment of autonomous vehicles. It is highlighted also that the proposed architecture is not tightly coupled to any other algorithm running in the vehicle , which characterizes the approach as a very modular and cost-effective computing solution