Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

Design and control of next-generation uavs for effectively interacting with environments

In this dissertation, the design and control of a novel multirotor for aerial manipulation is studied, with the aim of endowing the aerial vehicle with more degrees of freedom of motion and stability when interacting with the environments. Firstly, it presents an energy-efficient adaptive robust tracking control method for a class of fully actuated, thrust vectoring unmanned aerial vehicles (UAVs) with parametric uncertainties including unknown moment of inertia, mass and center of mass, which would occur in aerial maneuvering and manipulation. The effectiveness of this method is demonstrated through simulation. Secondly, a humanoid robot arm is adopted to serve as a 6-degree-of-freedom (DOF) automated flight testing platform for emulating the free flight environment of UAVs while ensuring safety. Another novel multirotor in a tilt-rotor architecture is studied and tested for coping with parametric uncertainties in aerial maneuvering and manipulation. Two pairs of rotors are mounted on two independently-controlled tilting arms placed at two sides of the vehicle in a H configuration to enhance its maneuverability and stability through an adaptive robust control method. In addition, an impedance control algorithm is deployed in the out loop that modifies the trajectory to achieve a compliant behavior in the end-effector space for aerial drilling and screwing tasks

Design and Experimental Realization of Adaptive Control Schemes for an Autonomous Underwater Vehicle

Research on Autonomous Underwater Vehicle(AUV) has attracted increased attention of control engineering community in the recent years due to its many interesting applications such as in Defense organisations for underwater mine detection, region surveillance, oceanography studies, oil/gas industries for inspection of underwater pipelines and other marine related industries. However, for the realization of these applications, effective motion control algorithms need to be developed. These motion control algorithms require mathematical representation of AUV which comprises of hydrodynamic damping, Coriolis terms, mass and inertia terms etc. To obtain dynamics of an AUV, different analytical and empirical methods are reported in the literature such as tow tank test, Computational Fluid Dynamics (CFD) analysis and on-line system identification. Among these methods, tow-tank test and CFD analysis provide white-box identified model of the AUV dynamics. Thus, the control design using these methods are found to be ineffective in situation of change in payloads of an AUV or parametric variations in AUV dynamics. On the other hand, control design using on-line identification, the dynamics of AUV can be obtained at every sampling time and thus the aforesaid parametric variations in AUV dynamics can be handled effectively. In this thesis, adaptive control strategies are developed using the parameters of AUV obtained through on-line system identification. The proposed algorithms are verified first through simulation and then through experimentation on the prototype AUV. Among various motion control algorithms, waypoint tracking has more practical significance for oceanographic surveys and many other applications. In order to implement, waypoint motion control schemes, Line-of-Sight (LoS) guidance law can be used which is computationally less expensive. In this thesis, adaptive control schemes are developed to implement LoS guidance for an AUV for practical realization of the control algorithm. Further, in order to realize the proposed control algorithms, a prototype AUV is developed in the laboratory. The developed AUV is a torpedo-shaped in order to experience low drag force, underactuated AUV with a single thruster for forward motion and control planes for angular motion. Firstly, the AUV structure such as nose profile, tail profile, hull section and control planes are designed and developed. Secondly, the hardware configuration of the AUV such as sensors, actuators, computational unit, communication module etc. are appropriately selected. Finally, a software framework called Robot Operating System (ROS) is used for seamless integration of various sensors, actuators with the computational unit. ROS is a software platform which provides right platform for the implementation of the control algorithms using the sensor data to achieve autonomous capability of the AUV. In order to develop adaptive control strategies, the unknown dynamics of the AUV is identified using polynomial-based Nonlinear Autoregressive Moving Average eXogenous (NARMAX) model structure. The parameters of this NARMAX model structure are identified online using Recursive Extended Least Square (RELS) method. Then an adaptive controller is developed for realization of the LoS guidance law for an AUV. Using the kinematic equation and the desired path parameters, a Lyapunov based backstepping controller is designed to obtain the reference velocities for the dynamics. Subsequently, a self-tuning PID controller is designed for the AUV to track these reference velocities. Using an inverse optimal control technique, the gains of the selftuning PID controller are tuned on-line. Although, this algorithm is computationally less expensive but there lie issues such as actuator constraints and state constraints which need to be resolved in view of practical realization of the control law. It is also observed that the proposed NARMAX structure of the AUV consists of redundant regressor terms. To alleviate the aforesaid limitations of the Inverse optimal self-tuning control scheme, a constrained adaptive control scheme is proposed that employs a minimum representation of the NARMAX structure (MR-NARMAX) for capturing AUV dynamics. The regressors of the MR-NARMAX structure are identified using Forward Regressor Orthogonal Least Square algorithm. Further, the parameters of this MRNARMAX model structure of the AUV are identified at every sampling time using RELS algorithm. Using the desired path parameters and the identified dynamics, an error objective function is defined which is to be minimized. The minimization problem where the objective function with the state and actuator constraints is formulated as a convex optimization problem. This optimization problem is solved using quadratic programming technique. The proposed MR-NARMAX based adaptive control is verified in the simulation and then on the prototype AUV. From the obtained results it is observed that this algorithm provides successful tracking of the desired heading. But, the proposed control algorithm is computational expensive, as an optimization problem is to be solved at each sampling instant. In order to reduce the computational time, an explicit model predictive control strategy is developed using the concept of multi-parametric programming. A Lyapunov based backstepping controller is designed to generate desired yaw velocity in order to steer the AUV towards the desired path. This explicit model predictive controller is designed using the identified NARMAX model for tracking the desired yaw velocity. The proposed explicit MPC algorithm is implemented first in simulation and then in the prototype AUV. From the simulation and experimental results, it is found that this controller has less computation time and also it considers both the state and actuator constraints whilst exhibiting good tracking performance

Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

Towards automated sample collection and return in extreme underwater environments

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Billings, G., Walter, M., Pizarro, O., Johnson-Roberson, M., & Camilli, R. Towards automated sample collection and return in extreme underwater environments. Journal of Field Robotics, 2(1), (2022): 1351–1385, https://doi.org/10.55417/fr.2022045.In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators. We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.This work was funded under a NASA PSTAR grant, number NNX16AL08G, and by the National Science Foundation under grants IIS-1830660 and IIS-1830500. The authors would like to thank the Costa Rican Ministry of Environment and Energy and National System of Conservation Areas for permitting research operations at the Costa Rican shelf margin, and the Schmidt Ocean Institute (including the captain and crew of the R/V Falkor and ROV SuBastian) for their generous support and making the FK181210 expedition safe and highly successful. Additionally, the authors would like to thank the Greek Ministry of Foreign Affairs for permitting the 2019 Kolumbo Expedition to the Kolumbo and Santorini calderas, as well as Prof. Evi Nomikou and Dr. Aggelos Mallios for their expert guidance and tireless contributions to the expedition

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective