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

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Decision uncertainty minimization and autonomous information gathering

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 272-283).Over the past several decades, technologies for remote sensing and exploration have become increasingly powerful but continue to face limitations in the areas of information gathering and analysis. These limitations affect technologies that use autonomous agents, which are devices that can make routine decisions independent of operator instructions. Bandwidth and other communications limitation require that autonomous differentiate between relevant and irrelevant information in a computationally efficient manner. This thesis presents a novel approach to this problem by framing it as an adaptive sensing problem. Adaptive sensing allows agents to modify their information collection strategies in response to the information gathered in real time. We developed and tested optimization algorithms that apply information guides to Monte Carlo planners. Information guides provide a mechanism by which the algorithms may blend online (realtime) and offline (previously simulated) planning in order to incorporate uncertainty into the decisionmaking process. This greatly reduces computational operations as well as decisional and communications overhead. We begin by introducing a 3-level hierarchy that visualizes adaptive sensing at synoptic (global), mesocale (intermediate) and microscale (close-up) levels (a spatial hierarchy). We then introduce new algorithms for decision uncertainty minimization (DUM) and representational uncertainty minimization (RUM). Finally, we demonstrate the utility of this approach to real-world sensing problems, including bathymetric mapping and disaster relief. We also examine its potential in space exploration tasks by describing its use in a hypothetical aerial exploration of Mars. Our ultimate goal is to facilitate future large-scale missions to extraterrestrial objects for the purposes of scientific advancement and human exploration.by Lawrence A. M. Bush.Ph. D

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

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Assessing the suitability of ship design for human factors issues associated with evacuation and normal operations

Evaluating ship layout for human factors (HF) issues using simulation software such as maritimeEXODUS can be a long and complex process. The analysis requires the identification of relevant evaluation scenarios; encompassing evacuation and normal operations; the development of appropriate measures which can be used to gauge the performance of crew and vessel and finally; the interpretation of considerable simulation data. In this paper we present a systematic and transparent methodology for assessing the HF performance of ship design which is both discriminating and diagnostic. The methodology is demonstrated using two variants of a hypothetical naval ship

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

Semi-Autonomous Small Unmanned Aircraft Systems for Sampling Tornadic Supercell Thunderstorms

This work describes the development of a network-centric unmanned aircraft system (UAS) for in situ sampling of supercell thunderstorms. UAS have been identified as a well-suited platform for meteorological observations given their portability, endurance, and ability to mitigate atmospheric disturbances. They represent a unique tool for performing targeted sampling in regions of a supercell thunderstorm previously unreachable through other methods. Doppler radar can provide unique measurements of the wind field in and around supercell thunderstorms. In order to exploit this capability, a planner was developed that can optimize ingress trajectories for severe storm penetration. The resulting trajectories were examined to determine the feasibility of such a mission, and to optimize ingress in terms of flight time and exposure to precipitation. A network-centric architecture was developed to handle the large amount of distributed data produced during a storm sampling mission. Creation of this architecture was performed through a bottom-up design approach which reflects and enhances the interplay between networked communication and autonomous aircraft operation. The advantages of the approach are demonstrated through several field and hardware-in-the-loop experiments containing different hardware, networking protocols, and objectives. Results are provided from field experiments involving the resulting network-centric architecture. An airmass boundary was sampled in the Collaborative Colorado Nebraska Unmanned Aircraft Experiment (CoCoNUE). Utilizing lessons learned from CoCoNUE, a new concept of operations (CONOPS) and UAS were developed to perform in situ sampling of supercell thunderstorms. Deployment during the Verification of the Origins of Rotation in Tornadoes Experiment 2 (VOR- TEX2) resulted in the first ever sampling of the airmass associated with the rear flank downdraft of a tornadic supercell thunderstorm by a UAS. Hardware-in-the-loop simulation capability was added to the UAS to enable further assessment of the system and CONOPS. The simulation combines a full six degree-of-freedom aircraft dynamic model with wind and precipitation data from simulations of severe convective storms. Interfaces were written to involve as much of the system\u27s field hardware as possible, including the creation of a simulated radar product server. A variety of simulations were conducted to evaluate different aspects of the CONOPS used for the 2010 VORTEX2 field campaign

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp