Active planning for underwater inspection and the benefit of adaptivity

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from an exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.United States. Office of Naval Research (ONR Grant N00014-09-1-0700)United States. Office of Naval Research (ONR Grant N00014-07-1-00738)National Science Foundation (U.S.) (NSF grant 0831728)National Science Foundation (U.S.) (NSF grant CCR-0120778)National Science Foundation (U.S.) (NSF grant CNS-1035866

Belief-space Planning for Active Visual SLAM in Underwater Environments.

Autonomous mobile robots operating in a priori unknown environments must be able to integrate path planning with simultaneous localization and mapping (SLAM) in order to perform tasks like exploration, search and rescue, inspection, reconnaissance, target-tracking, and others. This level of autonomy is especially difficult in underwater environments, where GPS is unavailable, communication is limited, and environment features may be sparsely- distributed. In these situations, the path taken by the robot can drastically affect the performance of SLAM, so the robot must plan and act intelligently and efficiently to ensure successful task completion. This document proposes novel research in belief-space planning for active visual SLAM in underwater environments. Our motivating application is ship hull inspection with an autonomous underwater robot. We design a Gaussian belief-space planning formulation that accounts for the randomness of the loop-closure measurements in visual SLAM and serves as the mathematical foundation for the research in this thesis. Combining this planning formulation with sampling-based techniques, we efficiently search for loop-closure actions throughout the environment and present a two-step approach for selecting revisit actions that results in an opportunistic active SLAM framework. The proposed active SLAM method is tested in hybrid simulations and real-world field trials of an underwater robot performing inspections of a physical modeling basin and a U.S. Coast Guard cutter. To reduce computational load, we present research into efficient planning by compressing the representation and examining the structure of the underlying SLAM system. We propose the use of graph sparsification methods online to reduce complexity by planning with an approximate distribution that represents the original, full pose graph. We also propose the use of the Bayes tree data structure—first introduced for fast inference in SLAM—to perform efficient incremental updates when evaluating candidate plans that are similar. As a final contribution, we design risk-averse objective functions that account for the randomness within our planning formulation. We show that this aversion to uncertainty in the posterior belief leads to desirable and intuitive behavior within active SLAM.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133303/1/schaves_1.pd

Comparative experimental study of control theoretic and connectionist controllers for nonlinear systems

Thesis (Ocean. E.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1995.Includes bibliographical references (leaves 63-66).by He Huang.Ocean.E

Standardization Roadmap for Unmanned Aircraft Systems, Version 1.0

This Standardization Roadmap for Unmanned Aircraft Systems, Version 1.0 (“roadmap”) represents the culmination of the UASSC’s work to identify existing standards and standards in development, assess gaps, and make recommendations for priority areas where there is a perceived need for additional standardization and/or pre-standardization R&D. The roadmap has examined 64 issue areas, identified a total of 60 gaps and corresponding recommendations across the topical areas of airworthiness; flight operations (both general concerns and application-specific ones including critical infrastructure inspections, commercial services, and public safety operations); and personnel training, qualifications, and certification. Of that total, 40 gaps/recommendations have been identified as high priority, 17 as medium priority, and 3 as low priority. A “gap” means no published standard or specification exists that covers the particular issue in question. In 36 cases, additional R&D is needed. The hope is that the roadmap will be broadly adopted by the standards community and that it will facilitate a more coherent and coordinated approach to the future development of standards for UAS. To that end, it is envisioned that the roadmap will be widely promoted and discussed over the course of the coming year, to assess progress on its implementation and to identify emerging issues that require further elaboration

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Sparse Bayesian information filters for localization and mapping

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 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

New 3-d video methods reveal novel territorial drift-feeding behaviors that help explain environmental correlates of Chena River chinook salmon productivity

Thesis (Ph.D.) University of Alaska Fairbanks, 2014.Chinook salmon (Oncorhynchus tshawytscha) are critical to subsistence and commerce in the Yukon River basin, but several recent years of low abundance have forced devastating fishery closures and raised urgent questions about causes of the decline. The Chena River subpopulation in interior Alaska has experienced a decline similar to that of the broader population. To evaluate possible factors affecting Chena River Chinook salmon productivity, I analyzed both population data and the behavior of individual fish during the summer they spend as fry drift feeding in the river. Using a stereo pair of high definition video cameras, I recorded the fine-scale behavior of schools of juvenile Chinook salmon associated with woody debris along the margins of the Chena River. I developed a software program called VidSync that recorded 3-D measurements with sub-millimeter accuracy and provided a streamlined workflow for the measurement of several thousand 3-D points of behavioral data (Chapter 1). Juvenile Chinook salmon spent 91% of their foraging attempts investigating and rejecting debris rather than capturing prey, which affects their energy intake rate and makes foraging attempt rate an unreliable indicator of foraging success (Chapter 2). Even though Chinook salmon were schooling, some were highly territorial within their 3-D school configurations, and many others maintained exclusive space-use behaviors consistent with the population regulatory effects of territoriality observed in other salmonids (Chapter 3). Finally, a twenty-year population time series from the Chena River and neighboring Salcha River contained evidence for negative density dependence and a strong negative effect of sustained high summer stream discharge on productivity (Chapter 4). The observed territoriality may explain the population's density dependence, and the effect of debris on foraging efficiency represents one of many potential mechanisms behind the negative effect of high stream discharge. In combination, these findings contribute to a statistically and mechanistically plausible explanation for the recent decline in Chena River Chinook salmon. If they are, in fact, major causes of the decline (other causes cannot be ruled out), then we can be tentatively hopeful that the population may be experiencing a natural lull in abundance from which a recovery is possible.General Introduction -- Chapter 1: Measuring fish and their habitats: Versatile 2-D and 3-D video techniques with user-friendly software -- Chapter 2: Mechanisms of drift-feeding behavior in juvenile Chinook salmon and the role of inedible debris in a clear-water Alaskan Stream -- Chapter 3: Territoriality within schools: dynamic competition of drift-feeding juvenile Chinook salmon in 3-dimensional space -- Chapter 4: Low productivity of Chinook salmon strongly correlates with high summer stream discharge in two Alaskan rivers in the Yukon drainage

A knowledge base system approach to inspection scheduling for fixed offshore platforms

In the offshore oil and gas industry in the UK, one of the most common forms of structure is the fixed steel jacket type of offshore platform. These are highly redundant structures subject to many random or uncertain factors. In particular, they are subject to uncertainties in the load distribution through the components, and to time-varying and cyclic loads leading to deterioration through fatigue. Operators are required to ensure the integrity of these structures by carrying out periodic inspections and repairing when necessary. Decisions on inspection, repair and maintenance (IRM) actions on structures involves making use of various tools and can be a complex problem. Traditionally, engineering judgement is employed to schedule inspections and deterministic analyses are used to confirm decisions. The use of structural reliability methods may lead to more rational scheduling of IRM actions. Applying structural reliability analysis to the production of rational inspection strategies, however, requires understanding the inspection procedure and making use of the appropriate information on inspection techniques. There are difficulties in collecting input data and the interpreted results need to be combined to form a rational global solution for the structure which takes into account practical constraints. The development of a knowledge base system (KBS) for reliability based inspection scheduling (RISC) provides a way of making use of complex quantitative objective analyses for scheduling. This thesis describes the development of a demonstrator RISC KBS. The general problems of knowledge representation and scheduling are discussed and schemes from Artificial Intelligence are proposed. Additionally, a system for automated inspection is described and its role in IRM of platforms is considered. A RISC System integrating suitable databases with fatigue fracture mechanics based reliability analysis within a KBS framework will enable operators to develop rational IRM scheduling strategies