An Adaptive Vision-based Sensor for Underwater Line Detection Employing Shape and Color Image Segmentation

This work aims the design and implementation of an adaptive vision-based sensor for detecting a pipe on underwater scenes in real time. The motivation is focused to future applications of vision servo control in underwater vehicles. The approach employs color and shape image segmentation together with an adjust mechanism that aims continuously in time to reach the best setup of a parameter set of the color image segmentation. The sensor performs very well even in the case of large and rapid changes in the scene illumination. On the basis of many experiments carried out in real scenes and the comparison with similar algorithms in the state-of-the-art field on the same application, the approach gets a better positioning with respect to related results above all in the case of extremely changing and poor luminance conditions. As drawback, the required computation time to achieve optimal values for the first time (auto-tuning phase) may be large; contrary to the adaptive ongoing process, in where the optimization is much more agile.Fil: Jordan, Mario Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; ArgentinaFil: Trabes, Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentin

Mapping of complex marine environments using an unmanned surface craft

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 185-199).Recent technology has combined accurate GPS localization with mapping to build 3D maps in a diverse range of terrestrial environments, but the mapping of marine environments lags behind. This is particularly true in shallow water and coastal areas with man-made structures such as bridges, piers, and marinas, which can pose formidable challenges to autonomous underwater vehicle (AUV) operations. In this thesis, we propose a new approach for mapping shallow water marine environments, combining data from both above and below the water in a robust probabilistic state estimation framework. The ability to rapidly acquire detailed maps of these environments would have many applications, including surveillance, environmental monitoring, forensic search, and disaster recovery. Whereas most recent AUV mapping research has been limited to open waters, far from man-made surface structures, in our work we focus on complex shallow water environments, such as rivers and harbors, where man-made structures block GPS signals and pose hazards to navigation. Our goal is to enable an autonomous surface craft to combine data from the heterogeneous environments above and below the water surface - as if the water were drained, and we had a complete integrated model of the marine environment, with full visibility. To tackle this problem, we propose a new framework for 3D SLAM in marine environments that combines data obtained concurrently from above and below the water in a robust probabilistic state estimation framework. Our work makes systems, algorithmic, and experimental contributions in perceptual robotics for the marine environment. We have created a novel Autonomous Surface Vehicle (ASV), equipped with substantial onboard computation and an extensive sensor suite that includes three SICK lidars, a Blueview MB2250 imaging sonar, a Doppler Velocity Log, and an integrated global positioning system/inertial measurement unit (GPS/IMU) device. The data from these sensors is processed in a hybrid metric/topological SLAM state estimation framework. A key challenge to mapping is extracting effective constraints from 3D lidar data despite GPS loss and reacquisition. This was achieved by developing a GPS trust engine that uses a semi-supervised learning classifier to ascertain the validity of GPS information for different segments of the vehicle trajectory. This eliminates the troublesome effects of multipath on the vehicle trajectory estimate, and provides cues for submap decomposition. Localization from lidar point clouds is performed using octrees combined with Iterative Closest Point (ICP) matching, which provides constraints between submaps both within and across different mapping sessions. Submap positions are optimized via least squares optimization of the graph of constraints, to achieve global alignment. The global vehicle trajectory is used for subsea sonar bathymetric map generation and for mesh reconstruction from lidar data for 3D visualization of above-water structures. We present experimental results in the vicinity of several structures spanning or along the Charles River between Boston and Cambridge, MA. The Harvard and Longfellow Bridges, three sailing pavilions and a yacht club provide structures of interest, having both extensive superstructure and subsurface foundations. To quantitatively assess the mapping error, we compare against a georeferenced model of the Harvard Bridge using blueprints from the Library of Congress. Our results demonstrate the potential of this new approach to achieve robust and efficient model capture for complex shallow-water marine environments. Future work aims to incorporate autonomy for path planning of a region of interest while performing collision avoidance to enable fully autonomous surveys that achieve full sensor coverage of a complete marine environment.by Jacques Chadwick Leedekerken.Ph.D

Underwater object localization using a biomimetic binaural sonar

Thesis (S.M. in Oceanographic Engineering)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering; and the Woods Hole Oceanographic Institution), 1999.Includes bibliographical references (leaves 85-89).by Qiang Wang.S.M.in Oceanographic Engineerin

UNMANNED GROUND VEHICLE (UGV) DOCKING, CONNECTION, AND CABLING FOR ELECTRICAL POWER TRANSMISSION IN AUTONOMOUS MOBILE MICROGRIDS

Autonomous Mobile Microgrids provide electrical power to loads in environments where humans either can not, or would prefer not to, perform the task of positioning and connecting the power grid equipment. The contributions of this work compose an architecture for electrical power transmission by Unmanned Ground Vehicles (UGV). Purpose-specific UGV docking and cable deployment software algorithms, and hardware for electrical connection and cable management, has been deployed on Clearpath Husky robots. Software development leverages Robot Operating System (ROS) tools for navigation and rendezvous of the autonomous UGV robots, with task-specific visual feedback controllers for docking validated in Monte-Carlo outdoor trials with a 73% docking rate, and application to wireless power transmission demonstrated in an outdoor environment. An “Adjustable Cable Management Mechanism” (ACMM) was designed to meet low cost, compact-platform constraints for powered deployment and retraction by a UGV of electrical cable subject to disturbance, with feed rates up to 1 m/s. A probe-and-funnel AC/DC electrical connector system was de- veloped for deployment on UGVs, which does not substantially increase the cost or complexity of the UGV, while providing a repeatable and secure method of coupling electrical contacts subject to a docking miss-alignment of up to +/-2 cm laterally and +/-15 degrees axially. Cabled power transmission is accomplished by a feed-forward/feedback control method, which utilizes visual estimation of the cable state to deploy electrical cable without tension, in the obstacle-free track of the UGV as it transverses to connect power grid nodes. Cabling control response to step-input UGV chassis velocities in the forward, reverse, and zero-point-turn maneuvers are presented, as well as outdoor cable deployment. This power transmission capability is relevant to diverse domains including military Forward-Operating-Bases, disaster response, robotic persistent operation, underwater mining, or planetary exploration