Sampling-Based Coverage Path Planning for Inspection of Complex Structures

We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstacle-filled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a sampling-based subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.United States. Office of Naval Research (ONR Grant N0014-06-10043

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

Maintenance and Asset Life Cycle for Reliability Systems

This chapter presents tools, methods, and indicators, in order to develop a successful and modern maintenance program. These are based on reliability engineering that improves the reliability of a system or complex equipment. Frequently, the industry implements maintenance schemes, which are based on equipment’s manufacturer’s recommendations and may not apply changes throughout the asset life cycle. In this sense, several philosophies, methodologies, and standards seek to assist this process, but most of them do not take into consideration their operation characteristics, production necessity, and other factors that are regarded as being important to one’s company. This method is based on the analysis of preventive component replacements and the subsequent critical consequences. These analyses may be used as a decision-making tool for defining component replacement decisions. In this chapter, the first section introduces and justifies the importance of this topic being approached from the perspective of asset management. Next, it discusses key maintenance concepts and techniques, with the aim of establishing the foundation of a maintenance management. The purpose of the final section is to present a maintenance strategy model, and it presents the findings of the case study about model implementation at home cleaning service company

Advanced perception, navigation and planning for autonomous in-water ship hull inspection

Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.United States. Office of Naval Research (Grant N00014-06-10043)United States. Office of Naval Research (Grant N00014-07-1-0791

Sampling-based coverage path planning for complex 3D structures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 173-186).Path planning is an essential capability for autonomous robots, and many applications impose challenging constraints alongside the standard requirement of obstacle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For two-dimensional environments, optimal algorithms are documented and well-understood. For threedimensional structures, however, few of the available heuristics succeed over occluded regions and low-clearance areas. This thesis makes several contributions to sampling-based coverage path planning, for use on complex three-dimensional structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry than the well-known dual sampling method, especially when high-quality solutions are desired. Second, we present an improvement procedure that iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage constraints. Third, we propose a modular algorithm that allows the simple components of a structure to be covered using planar, back-and-forth sweep paths. An analysis of probabilistic completeness, the first of its kind applied to coverage planning, accompanies each of these algorithms, as well as ensemble computational results. The motivating application throughout this work has been autonomous, in-water ship hull inspection. Shafts, propellers, and control surfaces protrude from a ship hull and pose a challenging coverage problem at the stern. Deployment of a sonar-equipped underwater robot on six large vessels has led to robust operations that yield triangle mesh models of these structures; these models form the basis for planning inspections at close range. We give results from a coverage plan executed at the stern of a US Coast Guard Cutter, and results are also presented from an indoor experiment using a precision scanning laser and gantry positioning system.by Brendan J. Englot.Ph.D

Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.Comment: RSS 201

Technology capabilities for an automated and connected earthwork roadmap

Purpose: The development of communication and artificial intelligence technologies has raised interest in connectivity and increased autonomy of automated earthmoving equipment for earthwork. These changes are motivating work to reduce uncertainties, in terms of improving equipment object detection capability and reducing strikes and accidents on site. The purpose of this study is to illustrate industrial drivers for automated earthwork systems; identify the specific capabilities which make the transformation happen; and finally determine use cases that create value for the system. These three objectives act as components of a technology roadmap for automated and connected earthwork and can guide development of new products and services. Design/methodology/approach: This paper used a text mining approach in which the required data was captured through a structured literature review, and then expert knowledge was used for verification of the results. Findings: Automated and connected earthwork can enhance construction site and its embraced infrastructure, resilience by avoiding human faults during operations. Automating the monitoring process can lead to reliable anticipation of problems and facilitate real-time responses to unexpected situation via connectedness capabilities. Research findings are presented in three sections: industrial perspectives, trends and drivers for automated and connected earthwork; capabilities which are met by technologies; and use cases to demonstrate different capabilities. Originality/value: This study combines the results of disintegrated and fragmented research in the area of automated and connected earthwork and categorises them under new capability levels. The identified capabilities are classified in three main categories including reliable environmental perception, single equipment decision-making toward safe outcomes and fleet-level safety enhancement. Finally, four different levels of automation are proposed for earthwork technology roadmap