해양 작업 지원선의 자율 운항 및 설치 작업 지원을 위한 시뮬레이션 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 조선해양공학과, 2019. 2. 노명일.Autonomous ships have gained a huge amount of interest in recent years, like their counterparts on land{autonomous cars, because of their potential to significantly lower the cost of operation, attract seagoing professionals and increase transportation safety. Technologies developed for the autonomous ships have potential to notably reduce maritime accidents where 75% cases can be attributed to human error and a significant proportion of these are caused by fatigue and attention deficit. However, developing a high-level autonomous system which can operate in an unstructured and unpredictable environment is still a challenging task. When the autonomous ships are operating in the congested waterway with other manned or unmanned vessels, the collision avoidance algorithm is the crucial point in keeping the safety of both the own ship and any encountered ships. Instead of developing new traffic rules for the autonomous ships to avoid collisions with each other, autonomous ships are expected to follow the existing guidelines based on the International Regulations for Preventing Collisions at Sea (COLREGs). Furthermore, when using the crane on the autonomous ship to transfer and install subsea equipment to the seabed, the heave and swaying phenomenon of the subsea equipment at the end of flexible wire ropes makes its positioning at an exact position is very difficult. As a result, an Anti-Motion Control (AMC) system for the crane is necessary to ensure the successful installation operation. The autonomous ship is highly relying on the effectiveness of autonomous systems such as autonomous path following system, collision avoidance system, crane control system and so on. During the previous two decades, considerable attention has been paid to develop robust autonomous systems. However, several are facing challenges and it is worthwhile devoting much effort to this. First of all, the development and testing of the proposed control algorithms should be adapted across a variety of environmental conditions including wave, wind, and current. This is one of the challenges of this work aimed at creating an autonomous path following and collision avoidance system in the ship. Secondly, the collision avoidance system has to comply with the regulations and rules in developing an autonomous ship. Thirdly, AMC system with anti-sway abilities for a knuckle boom crane remains problems regarding its under-actuated mechanism. At last, the performance of the control system should be evaluated in advance of the operation to perform its function successfully. In particular, such performance analysis is often very costly and time-consuming, and realistic conditions are typically impossible to establish in a testing environment. Consequently, to address these issues, we proposed a simulation framework with the following scenarios, which including the autonomous navigation scenario and crane operation scenario. The research object of this study is an autonomous offshore support vessel (OSV), which provides support services to offshore oil and gas field development such as offshore drilling, pipe laying, and oil producing assets (production platforms and FPSOs) utilized in EP (Exploration Production) activities. Assume that the autonomous OSV confronts an urgent mission under the harsh environmental conditions: on the way to an imperative offshore construction site, the autonomous OSV has to avoid target ships while following a predefined path. When arriving at the construction site, it starts to install a piece of subsea equipment on the seabed. So what technologies are needed, what should be invested for ensuring the autonomous OSV could robustly kilometers from shore, and how can an autonomous OSV be made at least as safe as the conventional ship. In this dissertation, we focus on the above critical activities for answering the above questions. In the general context of the autonomous navigation and crane control problem, the objective of this dissertation is thus fivefold: • Developing a COLREGs-compliant collision avoidance system. • Building a robust path following and collision avoidance system which can handle the unknown and complicated environment. • Investigating an efficient multi-ship collision avoidance method enable it easy to extend. • Proposing a hardware-in-the-loop simulation environment for the AHC system. • Solving the anti-sway problem of the knuckle boom crane on an autonomous OSV. First of all, we propose a novel deep reinforcement learning (RL) algorithm to achieve effective and efficient capabilities of the path following and collision avoidance system. To perform and verify the proposed algorithm, we conducted simulations for an autonomous ship under unknown environmental disturbance iiito adjust its heading in real-time. A three-degree-of-freedom dynamic model of the autonomous ship was developed, and the Line-of-sight (LOS) guidance system was used to converge the autonomous ship to follow the predefined path. Then, a proximal policy optimization (PPO) algorithm was implemented on the problem. By applying the advanced deep RL method, in which the autonomous OSV learns the best behavior through repeated trials to determine a safe and economical avoidance behavior in various circumstances. The simulation results showed that the proposed algorithm has the capabilities to guarantee collision avoidance of moving encountered ships while ensuring following a predefined path. Also, the algorithm demonstrated that it could manage complex scenarios with various encountered ships in compliance with COLREGs and have the excellent adaptability to the unknown, sophisticated environment. Next, the AMC system includes Anti-Heave Control (AHC) and Anti-Sway Control (ASC), which is applied to install subsea equipment in regular and irregular for performance analysis. We used the proportional-integral-derivative (PID) control method and the sliding mode control method respectively to achieve the control objective. The simulation results show that heave and sway motion could be significantly reduced by the proposed control methods during the construction. Moreover, to evaluate the proposed control system, we have constructed the HILS environment for the AHC system, then conducted a performance analysis of it. The simulation results show the AHC system could be evaluated effectively within the HILS environment. We can conclude that the proposed or adopted methods solve the problems issued in autonomous system design.해양 작업 지원선 (Offshore Support Vessel: OSV)의 경우 극한의 환경에도 불구하고 출항하여 해상에서 작업을 수행해야 하는 경우가 있다. 이러한 위험에의 노출을 최소화하기 위해 자율 운항에 대한 요구가 증가하고 있다. 여기서의 자율 운항은 선박이 출발지에서 목적지까지 사람의 도움 없이 이동함을 의미한다. 자율 운항 방법은 경로 추종 방법과 충돌 회피 방법을 포함한다. 우선, 운항 및 작업 중 환경 하중 (바람, 파도, 조류 등)에 대한 고려를 해야 하고, 국제 해상 충돌 예방 규칙 (Convention of the International Regulations for Preventing Collisions at Sea, COLREGs)에 의한 선박간의 항법 규정을 고려하여 충돌 회피 규칙을 준수해야 한다. 특히 연근해의 복잡한 해역에서는 많은 선박을 자동으로 회피할 필요가 있다. 기존의 해석적인 방법을 사용하기 위해서는 선박들에 대한 정확한 시스템 모델링이 되어야 하며, 그 과정에서 경험 (experience)에 의존하는 파라미터 튜닝이 필수적이다. 또한, 회피해야 할 선박 수가 많아질 경우 시스템 모델이 커지게 되고 계산 양과 계산 시간이 늘어나 실시간 적용이 어렵다는 단점이 있다. 또한, 경로 추종 및 충돌 회피를 포함하여 자율 운항 방법을 적용하기가 어렵다. 따라서 본 연구에서는 강화 학습 (Reinforcement Learning: RL) 기법을 이용하여 기존 해석적인 방법의 문제점을 극복할 수 있는 방법을 제안하였다. 경로를 추종하는 선박 (agent)은 외부 환경 (environment)과 상호작용하면서 학습을 진행한다. State S_0 (선박의 움직임과 관련된 각종 상태) 가지는 agent는 policy (현재 위치에서 어떤 움직임을 선택할 것인가)에 따라 action A_0 (움직일 방향) 취한다. 이에 environment는 agent의 다음 state S_1 을 계산하고, 그에 따른 보상 R_0 (해당 움직임의 적합성)을 결정하여 agent에게 전달한다. 이러한 작업을 반복하면서 보상이 최대가 되도록 policy를 학습하게 된다. 한편, 해상에서 크레인을 이용한 장비의 이동이나 설치 작업 시 위험을 줄이기 위해 크레인의 거동 제어에 대한 요구가 증가하고 있다. 특히 해상에서는 선박의 운동에 의해 크레인에 매달린 물체가 상하 동요 (heave)와 크레인을 기준으로 좌우 동요 (sway)가 발생하는데, 이러한 운동은 작업을 지연시키고, 정확한 위치에 물체를 놓지 못하게 하며, 자칫 주변 구조물과의 충돌을 야기할 수 있다. 이와 같은 동요를 최소화하는 Anti-Motion Control (AMC) 시스템은 Anti-Heave Control (AHC)과 Anti-Sway Control (ASC)을 포함한다. 본 연구에서는 해양 작업 지원선에 적합한 AMC 시스템의 설계 및 검증 방법을 연구하였다. 먼저 상하 동요를 최소화하기 위해 크레인의 와이어 길이를 능동적으로 조정하는 AHC 시스템을 설계하였다. 또한, 기존의 제어 시스템의 검증 방법은 실제 선박이나 해양 구조물에 해당 제어 시스템을 직접 설치하기 전에는 그 성능을 테스트하기가 힘들었다. 이를 해결하기 위해 본 연구에서는 Hardware-In-the-Loop Simulation (HILS) 기법을 활용하여 AHC 시스템의 검증 방법을 연구하였다. 또한, ASC 시스템을 설계할 때 제어 대상이 under-actuated 시스템이기 때문에 제어하기가 매우 어렵다. 따라서 본 연구에서는 sliding mode control 알고리즘을 이용하며 다관절 크레인 (knuckle boom crane)의 관절 (joint) 각도를 제어하여 좌우 동요를 줄일 수 있는 ASC 시스템을 설계하였다.Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . 1 1.2 Requirements for Autonomous Operation . . . . . . . . . . . . . 5 1.2.1 Path Following for Autonomous Ship . . . . . . . . . . . . 5 1.2.2 Collision Avoidance for Autonomous Ship . . . . . . . . . 5 1.2.3 Anti-Motion Control System for Autonomous Ship . . . . 6 1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Related Work for Path Following System . . . . . . . . . 9 1.3.2 Related Work for Collision Avoidance System . . . . . . . 9 1.3.3 Related Work for Anti-Heave Control System . . . . . . . 13 1.3.4 Related Work for Anti-Sway Control System . . . . . . . 14 1.4 Configuration of Simulation Framework . . . . . . . . . . . . . . 16 1.4.1 Application Layer . . . . . . . . . . . . . . . . . . . . . . 16 1.4.2 Autonomous Ship Design Layer . . . . . . . . . . . . . . . 17 1.4.3 General Technique Layer . . . . . . . . . . . . . . . . . . 17 1.5 Contributions (Originality) . . . . . . . . . . . . . . . . . . . . . 19 Chapter 2 Theoretical Backgrounds 20 2.1 Maneuvering Model for Autonomous Ship . . . . . . . . . . . . . 20 2.1.1 Kinematic Equation for Autonomous Ship . . . . . . . . . 20 2.1.2 Kinetic Equation for Autonomous Ship . . . . . . . . . . 21 2.2 Multibody Dynamics Model for Knuckle Boom Crane of Autonomous Ship. . . 25 2.2.1 Embedding Techniques . . . . . . . . . . . . . . . . . . . . 25 2.3 Control System Design . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.1 Proportional-Integral-Derivative (PID) Control . . . . . . 31 2.3.2 Sliding Mode Control . . . . . . . . . . . . . . . . . . . . 31 2.4 Deep Reinforcement Learning Algorithm . . . . . . . . . . . . . . 34 2.4.1 Value Based Learning Method . . . . . . . . . . . . . . . 36 2.4.2 Policy Based Learning Method . . . . . . . . . . . . . . . 37 2.4.3 Actor-Critic Method . . . . . . . . . . . . . . . . . . . . . 41 2.5 Hardware-in-the-Loop Simulation . . . . . . . . . . . . . . . . . . 43 2.5.1 Integrated Simulation Method . . . . . . . . . . . . . . . 43 Chapter 3 Path Following Method for Autonomous OSV 46 3.1 Guidance System . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.1.1 Line-of-sight Guidance System . . . . . . . . . . . . . . . 46 3.2 Deep Reinforcement Learning for Path Following System . . . . . 50 3.2.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 50 3.2.2 Neural Network Architecture . . . . . . . . . . . . . . . . 56 3.2.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 58 3.3 Implementation and Simulation Result . . . . . . . . . . . . . . . 62 3.3.1 Implementation for Path Following System . . . . . . . . 62 3.3.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 65 3.4 Comparison Results . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.4.1 Comparison Result of PPO with PID . . . . . . . . . . . 83 3.4.2 Comparison Result of PPO with Deep Q-Network (DQN) 87 Chapter 4 Collision Avoidance Method for Autonomous OSV 89 4.1 Deep Reinforcement Learning for Collision Avoidance System . . 89 4.1.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 89 4.1.2 Neural Network Architecture . . . . . . . . . . . . . . . . 93 4.1.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Implementation and Simulation Result . . . . . . . . . . . . . . . 95 4.2.1 Implementation for Collision Avoidance System . . . . . . 95 4.2.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 100 4.3 Implementation and Simulation Result for Multi-ship Collision Avoidance Method . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.1 Limitations of Multi-ship Collision Avoidance Method - 1 107 4.3.2 Limitations of Multi-ship Collision Avoidance Method - 2 108 4.3.3 Implementation of Multi-ship Collision Avoidance Method 110 4.3.4 Simulation Result of Multi-ship Collision Avoidance Method 118 Chapter 5 Anti-Motion Control Method for Knuckle Boom Crane 129 5.1 Configuration of HILS for Anti-Heave Control System . . . . . . 129 5.1.1 Virtual Mechanical System . . . . . . . . . . . . . . . . . 132 5.1.2 Virtual Sensor and Actuator . . . . . . . . . . . . . . . . 138 5.1.3 Control System Design . . . . . . . . . . . . . . . . . . . . 141 5.1.4 Integrated Simulation Interface . . . . . . . . . . . . . . . 142 5.2 Implementation and Simulation Result of HILS for Anti-Heave Control System . . . . . . . . 145 5.2.1 Implementation of HILS for Anti-Heave Control System . 145 5.2.2 Simulation Result of HILS for Anti-Heave Control System 146 5.3 Validation of HILS for Anti-Heave Control System . . . . . . . . 159 5.3.1 Hardware Setup . . . . . . . . . . . . . . . . . . . . . . . 159 5.3.2 Comparison Result . . . . . . . . . . . . . . . . . . . . . . 161 5.4 Configuration of Anti-Sway Control System . . . . . . . . . . . . 162 5.4.1 Mechanical System for Knuckle Boom Crane . . . . . . . 162 5.4.2 Anti-Sway Control System Design . . . . . . . . . . . . . 165 5.4.3 Implementation and Simulation Result of Anti-Sway Control . . . . . . . . . . . . . . 168 Chapter 6 Conclusions and Future Works 176 Bibliography 178 Chapter A Appendix 186 국문초록 188Docto

Using immersive audio and vibration to enhance remote diagnosis of mechanical failure in uncrewed vessels.

There is increasing interest in the maritime industry in the potential use of uncrewed vessels to improve the efficiency and safety of maritime operations. This leads to a number of questions relating to the maintenance and repair of mechanical systems, in particular, critical propulsion systems which if a failure occurs could endanger the vessel. While control data is commonly monitored remotely, engineers on board ship also employ a wide variety of sensory feedback such as sound and vibration to diagnose the condition of systems, and these are often not replicated in remote monitoring. In order to assess the potential for enhancement of remote monitoring and diagnosis, this project simulated an engine room (ER) based on a real vessel in Unreal Engine 4 for the HTC ViveTM VR headset. Audio was recorded from the vessel, with mechanical faults synthesized to create a range of simulated failures. In order to simulate operational requirements, the system was remotely fed data from an external server. The system allowed users to view normal control room data, listen to the overall sound of the space presented spatially over loudspeakers, isolate the sound of particular machinery components, and feel the vibration of machinery through a body worn vibration transducer. Users could scroll through a 10-hour time history of system performance, including audio, vibration and data for snapshots at hourly intervals. Seven experienced marine engineers were asked to assess several scenarios for potential faults in different elements of the ER. They were assessed both quantitatively regarding correct fault identification, and qualitatively in order to assess their perception of usability of the system. Users were able to diagnose simulated mechanical failures with a high degree of accuracy, mainly utilising audio and vibration stimuli, and reported specifically that the immersive audio and vibration improved realism and increased their ability to diagnose system failures from a remote location

Opportunities and Challenges in Using Ship-Bridge Simulators in Maritime Research

Presentation at the Ergoship 2019 conference, Haugesund, 24. - 25. september, 2019. Source at https://www.hvl.no/en/about/marcatch/ergoship-conference-papers/. </a

Cyber-SHIP: Developing Next Generation Maritime Cyber Research Capabilities

As a growing global threat, cyber-attacks can cost millions of dollars or endanger national stability and human lives. While relatively well understood in most sectors, it is becoming clear that, although the maritime sector is becoming more digitally advanced (e.g., autonomy), it is not well protected against cyber or cyber-physical attacks and accidents. To help improve sector-wide safety and resiliency, the University of Plymouth (UoP) is creating a specialised maritime-cyber lab, which combines maritime technology and traditional cyber-security labs. This is in response to the lack of research and mitigation capabilities and will create a new resource capability for academia, government, and industry research into maritime cybersecurity risks and threats. These lab capabilities will also enhance existing maritime-cyber capabilities across the world, including risk assessment frameworks, cybersecurity ranges/labs, ship simulators, mariner training programmes, autonomous ships, etc. The goal of this paper is to explain the need for next generation maritime-cyber research capabilities, and demonstrate how something like the proposed Cyber-SHIP Lab (Hardware, Software, Information and Protections) will help industry, government, and academia understand and mitigate cyber threats in the maritime sector. The authors believe a next generation cyber-secure lab should host a range of real, non-simulated, maritime systems. With multiple configurations to mirror existing bridge system set-ups, the technology can be studied for individual system weakness as well as the system-of-systems vulnerabilities. Such as lab would support a range of research that cannot be achieved with simulators alone and help support the next generation of cyber-secure marine systems. </jats:p

Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs

The paper discusses an intelligent vision-based control solution for autonomous tracking and landing of Vertical Take-Off and Landing (VTOL) capable Unmanned Aerial Vehicles (UAVs) on ships without utilizing GPS signal. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, refers to a standardized visual cue installed on most Navy ships called the "horizon bar" for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning-based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe