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)

Technologies Enabling Exploration of Skylights, Lava Tubes and Caves

Robotic exploration of skylights and caves can seek out life, investigate geology and origins, and open the subsurface of other worlds to humankind. However, exploration of these features is a daunting venture. Planetary voids present perilous terrain that requires innovative technologies for access, exploration, and modeling. This research developed technologies for venturing underground and conceived mission architectures for robotic expeditions that explore skylights, lava tubes and caves. The investigation identified effective designs for mobile robot architecture to explore sub-planetary features. Results provide insight into mission architectures, skylight reconnaissance and modeling, robot configuration and operations, and subsurface sensing and modeling. These are developed as key enablers for robotic missions to explore planetary caves. These results are compiled to generate "Spelunker", a prototype mission concept to explore a lunar skylight and cave. The Spelunker mission specifies safe landing on the rim of a skylight, tethered descent of a power and communications hub, and autonomous cave exploration by hybrid driving/hopping robots. A technology roadmap was generated identifying the maturation path for enabling technologies for this and similar missions

Errors and Truths from Transportation Data Aggregation: Some Implications for Research and Practice

Data aggregation, which is a process to combine information by defined groups for statistical analysis, summary, data size reduction, or other purposes, has fundamental challenges, such as loss of the original information. Improper data aggregation, such as sampling bias or incorrect calculation of average, may cause misreading of information. In first chapter, it is revealed that the harmonic mean, which is used to calculate space mean speed for fixed segment, has a sampling bias, i.e., overestimation with small samples. The several impact analyses show that the sampling bias is affected by sampling rate, time interval, segment length, and distribution type. If the data aggregation is properly used, it can help us improve analytical efficiency, encounter some of critical problems, or reveal its casualties and other relevant information. Second and third chapters utilize the aggregation of multi-source data to estimate error distributions of data sources and improve accuracy of their measurements. This is a leaping point of evaluating data sources as the proposed model does not require ground truth data. Second chapter focuses more on the methodology, i.e., a modified Approximate Bayesian Computation, incorporated to construct the error distribution with numerous simulations. In the simulated experiment, the proposed model outperformed the alternative approach, which is a conventional way of evaluating data source that is gathering error information by comparing with ground data source. Several sensitivity analyses explore that how the model performance is affected by sample size, number of data sources, and distribution types. The proposed model in chapter II is limited to one dimensional variable, and then the application is expanded to improving the position and distance measurement of connected vehicle environment. The proposed model can be used to further improve the accuracy of vehicle positioning with other existing methods, such as simultaneous localization and mapping (SLAM). The estimation process can be conducted in real-time operation, and the learning process will try to keep improving the accuracy of estimation. The results show that the proposed model noticeably improves the accuracy of position and distance measurements

Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.Massachusetts Institute of Technology. Department of Mechanical EngineeringSingapore-MIT Alliance for Research and Technology (SMART) (Graduate Fellowship

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Umgebungskartenschätzung aus Sidescan-Sonardaten für ein autonomes Unterwasserfahrzeug

Für die Schätzung der Höhenkarten aus Sidescan-Sonardaten liefert die Arbeit mehrere Beiträge: Ein neues Schätzverfahren, das Sonarmessungen aus vorberechneten Sonarantworten von Basiselementen, sog. Kerneln, zusammensetzt und so zu einer Höhenschätzung gelangt. Des Weiteren ein dreidimensionales Verfahren, das auf Markov Random Fields basiert und eine Sidescan-Sonarsimulationsumgebung für beliebige dreidimensionale Szenen, die auch verschiedene Sonaraufnahmemodi und Terraingeneratoren bietet

Cooperative algorithms for a team of autonomous underwater vehicles

Ph.DDOCTOR OF PHILOSOPH

Lost on Purpose

In nine linked nonfiction essays and eight codas, the author seeks to understand the meaning of being lost and the importance of not knowing in an era of instantaneous and ubiquitous information. Through extensive interviews, research, and memoir, the author seeks out those who choose to live lost in order to understand his own penchant for escape. Framed by the mystery of antique maps, these essays find the author in several different locations, from Tasmania to Siberia to Utah. In each, the author meets and spends time with an ambassador of each place before finally attempting to become lost himself