생체의 모델링과 Simulation

This paper addressesthe problemsof automatically planning AutonomousUnderwater Vehicle (AUV)pathswhichbestexploit complex current data, from computational estuarine modelforecasts, whilealsoavoiding obstacles. Inparticular weexamine thepossibilities foranovel typeofAUV mission deployment infastflowing tidal river regions whichexperience bi-directional current flow. These environments are interesting in that,by choosing an appropriate pathinspaceandtime, anAUV maybothbypass adverse currents whicharetoofasttobeovercome bythe vehicle's motorsandalsoexploit favorable currents toachieve fargreater speeds thanthemotorscouldotherwise provide, while substantially saving energy. TheAUV can"ride" currents bothupanddowntheriver, enabling extended monitoring of otherwise energy-exhausting, fastflowenvironments. The paperdiscusses suitable pathparameterizations, costfunctions andoptimization techniques whichenable optimal AUV paths to be efficiently generated. Thesepathstakemaximum advantage oftheriver currents inordertominimize energy expenditure, journey timeandothercostparameters. The resulting pathplannercan automatically suggest useful alternative mission start andendtimes andlocations tothose specified bytheuser. Examples arepresented fornavigation in a simple simulation ofthefastflowing HudsonRiverwaters around Manhattan.

Optimal AUV pathplanning forextended missions incomplex, fast-flowing estuarine environments.

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

The General Circulation of the Atmosphere

The localization of autonomous underwater vehicles (AUVs) in anchor-free environments has always been a difficult problem due to the lack of global positioning systems and absolute references. In general, AUVs localize themselves by dead reckoning (DR), whereas the localization error grows without bound. To alleviate the growth of the localization errors, we propose intermittent belief propagation based dead reckoning (IBPDR) as a cooperative localization (CL) framework. In IBPDR, AUVs use DR to localize themselves and periodically correct DR's deviation with CL methods. The intermittent feature of IBPDR reduces communication costs among AUVs by decreasing the frequency of CL. In the IBPDR framework, we design a particle-based underwater-adaptive belief propagation (UABP) algorithm for CL. The UABP algorithm is naturally distributed and viable in nonlinear and non-Gaussian situations. Thus, it is suitable for CL issues. Furthermore, the UABP algorithm is robust to the accumulated inertial measurement errors and reduces communication costs among AUVs. Moreover, we propose a particle-based current-aided filter to further improve the localization accuracy by comparing AUVs’ ambient current observations with the available current maps. Simulation results validate the proposed algorithms by comparisons with alternative approaches in localization accuracy, communication costs, and robustness to abnormal cases, such as packet loss, ranging bias, and outliers.

Multiple Autonomous Underwater Vehicle Cooperative Localization in Anchor-Free Environments

Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations ( $>$ 1 h), where neither frequent surfacing nor consistent bottom tracking is available. We instantiate this concept through mid-depth underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller scale turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved small-scale flow perturbations are present. For a 6-h navigation with an automotive-grade inertial navigation system, the current-aided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent double-gyre flow field, and under 7.3% UDT in a turbulent meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-h mission, positioning result with under 25% UDT for a 24-h navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

A quasi-dynamic method is proposed to evaluate the characteristics of ball bearings, which include pressure distribution over the contact area between the ball and the raceway, sliding velocity distribution, and lubrication parameters The extent of permissible wear is confirmed based on the Archard wear equation A mathematical model for wear-life analysis is then presented for 6000 deep groove ball bearings with axial loads The effects of axial loads, rotating speed, and structural parameters on wear characteristics of bearings are analyzed A number of conclusions are drawn Based on the proposed mathematical model, the wear life of a bearing decreases with increased axial load The wear life of the bearing decreases as the rotational speed of the inner ring increases The wear life of the bearing exhibits nearly linear increment as the groove curvature of the inner ring increases and decreases as the groove curvature of the outer ring increases

Wear-life analysis of deep groove ball bearings based on Archard wear theory

In this paper, a bioinspired, compact, cost-effective autonomous underwater vehicle system is presented. Designed to operate in a heterogeneous, multivehicle collaboration hierarchy, the presented vehicle design features 3D printing technology to enable fast fabrication with a complex internal structure. Similar to a previous vehicle prototype, this system generates propulsive forces by expelling unsteady, pulsed jets, inspired by the locomotion of cephalopods and jellyfish. The novel thrusters enable the vehicle to be fully actuated in horizontal plane motions, without sacrificing the low-forward-drag, slender vehicle profile. By successively ingesting water and expelling finite water jets, periodic actuation forces are generated at all possible vehicle velocities, eliminating the need for control surfaces used in many conventional underwater vehicle designs. A semiactive buoyancy control system, inspired by the nautilus, adjusts the vehicle depth by passively allowing water flowing into and actively expelling water out of an internal bladder. A compact embedded system is developed to achieve the control and sensing capabilities necessary for multiagent interactions with the minimum required processing power and at a low energy cost. The new vehicle design also showcases an underwater optical communication system for short-range, high-speed data transmission, supplementing the conventional acoustic communication system. Experimental results show that, with the thruster motors powered at a 60% duty-cycle, the new vehicle is able to achieve a 1/4 zero-radius turn in 3.5 s and one-body-width sway translation in 2.5 s.

A Compact Autonomous Underwater Vehicle With Cephalopod-Inspired Propulsion

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the current-aided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

Zhuoyuan Song

Papers

Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Wear-life analysis of deep groove ball bearings based on Archard wear theory

A Compact Autonomous Underwater Vehicle With Cephalopod-Inspired Propulsion

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features