Design and implementation of balance control in a humanoid robot

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaf 28).A proportional derivative control strategy was developed for the purpose of achieving balance in a humanoid robot. An artificial muscle model was adapted which modified physiological parameters for the purpose of controlling a lightweight robot skeleton. Gains were modified as a function of joint angles to permit low gain near the equilibrium point, and consequently to promote a human-like swaying behavior that is energy-efficient. The control strategy was testing by placing a non-zero initial condition on the ankle joint angle and observing the robot, both physically and in simulation, attempt to achieve a stable swaying pattern. This was achieved successfully in a simulation of the robot's mass and inertial parameters, but further efforts must be made to obtain the same behavior in the robot. The ability of a robot to successfully balance using a human-like sway pattern adds another successful biomimetic feature to humanoid robot control and in addition should improve the efficiency of such systems.by Brendan J. Englot.S.B

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

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 underwater 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.United States. Office of Naval Research (Grant N00014-06-10043

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

Stability and robustness analysis tools for marine robot localization and mapping applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.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. 114-118).The aim of this analysis is to explore the fundamental stability issues of a robotic vehicle carrying out localization, mapping, and feedback control in a perturbation-filled environment. Motivated by the application of an ocean vehicle performing an autonomous ship hull inspection, a planar vehicle model performs localization using point features from a given map. Cases in which the agent must update the map are also considered. The stability of the marine robot controller and estimator duo is investigated using a pair of theorems requiring boundedness and convergence of the transition matrix Euclidean norm. These theorems yield a stability test for the feedback controller. Perturbations are then considered using a theorem on the convergence on the perturbed system transition matrix, yielding a robustness test for the estimator. Together, these tests form a set of tools which can be used in planning and evaluating the robustness of marine vehicle survey trajectories, which is demonstrated through experiment. An augmented A* kinodynamic path-planning algorithm is then implemented to search the control input space for the globally robustness-optimal survey trajectory.by Brendan J. Englot.S.M

Towards Autonomous Ship Hull Inspection using the Bluefin HAUV

URL is to paper listed on conference scheduleIn this paper we describe our effort to automate ship hull inspection for security applications. Our main contribution is a system that is capable of drift-free self-localization on a ship hull for extended periods of time. Maintaining accurate localization for the duration of a mission is important for navigation and for ensuring full coverage of the area to be inspected. We exclusively use onboard sensors including an imaging sonar to correct for drift in the vehicle’s navigation sensors. We present preliminary results from online experiments on a ship hull. We further describe ongoing work including adding capabilities for change detection by aligning vehicle trajectories of different missions based on a technique recently developed in our lab.United States. Office of Naval Research (grant N00014-06- 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

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 robot's 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.United States. Office of Naval Research (Grant N00014-06-10043

Sampling-based sweep planning to exploit local planarity in the inspection of complex 3D structures

We present a hybrid algorithm that plans feasible paths for 100% sensor coverage of complex 3D structures. The structures to be inspected are segmented to isolate planar areas, and back-and-forth sweep paths are generated to view as much of these planar areas as possible while avoiding collision. A randomized planning procedure fills in the remaining gaps in coverage. The problem of selecting an order to traverse the elements of the inspection is solved by reduction to the traveling salesman problem. We present results of the planning algorithm for an autonomous underwater vehicle inspecting the in-water portion of a ship hull. The randomized configurations succeed in observing confined and occluded areas, while the 2D sweep paths succeed in covering the open areas.United States. Office of Naval Research (ONR grant N00014-06-10043