Brendan J. Englot

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.

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.

Imaging sonar-aided navigation for autonomous underwater harbor surveillance

In this paper we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach only uses onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. We show results from several experiments that demonstrate drift-free navigation in various underwater environments.

Three-dimensional coverage planning for an underwater inspection robot

To support autonomous, in-water inspection of a ship hull, we propose and implement new techniques for coverage path planning over complex 3D structures. Our main contribution is a comprehensive methodology for sampling-based design of inspection routes, including an algorithm for planning, an algorithm for smoothing, and an analysis of probabilistic completeness. The latter two outcomes are the first of their kind in the area of coverage planning. Our algorithms give high-quality solutions over expansive structures, and we demonstrate this with experiments in the laboratory and on a 75 m Coast Guard cutter.

Planning Complex Inspection Tasks Using Redundant Roadmaps

The aim of this work is fast, automated planning of robotic inspections involving complex 3D structures. A model comprised of discrete geometric primitives is provided as input, and a feasible robot inspection path is produced as output. Our algorithm is intended for tasks in which 2.5D algorithms, which divide an inspection into multiple 2D slices, and segmentation-based approaches, which divide a structure into simpler components, are unsuitable. This degree of 3D complexity has been introduced by the application of autonomous in-water ship hull inspection; protruding structures at the stern (propellers, shafts, and rudders) are positioned in close proximity to one another and to the hull, and clearance is an issue for a mobile robot. A global, sampling-based approach is adopted, in which all the structures are simultaneously considered in planning a path. First, the state space of the robot is discretized by constructing a roadmap of feasible states; construction ceases when each primitive is observed by a specified number of states. Once a roadmap is produced, the set cover problem and traveling salesman problem are approximated in sequence to build a feasible inspection tour. We analyze the performance of this procedure in solving one of the most complex inspection planning tasks to date, covering the stern of a large naval ship, using an a priori triangle mesh model obtained from real sonar data and comprised of 100,000 primitives. Our algorithm generates paths on a par with dual sampling, with reduced computational effort.

Inspection planning for sensor coverage of 3D marine structures

We introduce an algorithm to achieve complete sensor coverage of complex, three-dimensional structures surveyed by an autonomous agent with multiple degrees of freedom. Motivated by the application of an ocean vehicle performing an autonomous ship hull inspection, we consider a planning problem for a fully-actuated, six degree-of-freedom hovering AUV using a bathymetry sonar to inspect the complex structures underneath a ship hull. We consider a discrete model of the structure to be inspected, requiring only that the model be provided in the form of a closed triangular mesh. A dense graph of feasible paths is constructed in the robots configuration space until the set of edges in the graph allows complete coverage of the structure. Then, we approximate the minimum-cost closed walk along the graph which observes 100% of the structure. We emphasize the embedding of observations within the edges of the graph as a means of utilizing all available sensor data in planning the inspection.

Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion

We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our methods binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.

Hierarchical Multi-objective planning: From mission specifications to contingency management

We propose a hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning problem across different layers, leveraging the difference in spatial and temporal scales of the mission objectives. We show that this framework facilitates contingency management under unanticipated events. Interaction between the various layers requires consistent model abstractions and common message semantics. To satisfy these requirements, we adopt a generic knowledge-based architecture that is independent from a specific application domain. We show a specific instance of our framework using a Constrained Markov Decision Process (CMDP) planner at the higher level and a Multi-Objective Probabilistic Roadmap (MO-PRM) planner at the lower level. The resulting planning system is tested in a realistic scenario where an agent is tasked with a mission in a large urban threat rich environment under dynamic uncertain conditions. The mission specification includes a Linear Temporal Logic (LTL) formula that defines the desired behaviors, a list of metrics to be optimized and a list of constraints on time, resources and probability of mission success.

Multiobjective Path Planning: Localization Constraints and Collision Probability

We present a novel path planning algorithm that, starting from a probabilistic roadmap, efficiently constructs a product graph used to search for a near optimal solution of a multiobjective optimization problem. The goal is to find paths that minimize a primary cost, such as the path length from start to goal, subject to a bound on a secondary cost such as the state estimation error covariance. The proposed algorithm is efficient as it relies on a scalar metric, related to the largest eigenvalue of the error covariance, and adaptively quantizes the secondary cost, yielding a product graph whose number of vertices and edges provides a good tradeoff between optimality and computational complexity. We further show how our approach can be extended to handle constraints on the probability of collision avoidance specified at every vertex along the path. Numerical examples show 1) how the computed paths change as a function of the specified bound on the secondary costs, and 2) the tradeoff between accuracy and computational efficiency of the proposed approach compared with methods where the product graph is built by quantizing the secondary cost uniformly.

Multi-goal feasible path planning using ant colony optimization

A new algorithm for solving multi-goal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its well-known application, the traveling salesman problem (TSP), to that of multi-goal feasible path planning for inspection and surveillance applications. Specifically, the ant colony framework is combined with a sampling-based point-to-point planning algorithm; this is compared with two successful sampling-based multi-goal planning algorithms in an obstacle-filled two-dimensional environment. Total mission time, a function of computational cost and the duration of the planned mission, is used as a basis for comparison. In our application of interest, autonomous undewater inspections, the ACO algorithm is found to be the best-equipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality.