Crane collision modelling using a neural network approach

The objective of the present work is to find a Collision Detection algorithm to be used in the Virtual Reality crane simulator (UVSim®), developed by the Robotics Institute of the University of Valencia for the Port of Valencia. The method is applicable to box-shaped objects and is based on the relationship between the colliding object positions and their impact points. The tool chosen to solve the problem is a neural network, the multilayer perceptron, which adapts to the characteristics of the problem, namely, non-linearity, a large amount of data, and no a priori knowledge. The results achieved by the neural network are very satisfactory for the case of box-shaped objects. Furthermore, the computational burden is independent from the object positions and how the surfaces are modelled; hence, it is suitable for the real-time requirements of the application and outperforms the computational burden of other classical methods. The model proposed is currently being used and validated in the UVSim Gantry Crane simulator

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

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance

Integrating Building Information Modelling and Firefly Algorithm to Optimize Tower Crane Layout

Abstract - Tower crane layout design and planning within construction site is a common construction technical issue and regarded as a complex combinatorial problem. To transport heavy materials, such as rebar, formwork, scaffolding, equipment and steel, tower cranes are needed and should be well located to reduce construction cost and improve safety management. Currently, practitioners in the industry are over-reliance on individual experience and subjective judgment during decision-making process. The purpose of this paper wants to develop a well-defined approach, which integrating Building Information Modelling (BIM) and firefly algorithm to come up with an optimal tower crane layout for construction projects. Firstly, BIM technology is utilized to automatically generate the quantity of materials which need to be transported. Then firefly algorithms are used to determine the locations of tower cranes, supply points and demand points according to transportation requirement, time and cost. Thirdly, the optimal tower crane layout scheme will be visualized by 4-Dimension (4D) BIM to verify its constructability and safety based on computer simulation and individual experience. Finally, a practical case is selected to evaluate the developed approach. In addition, some lessons learned and issues are highlighted that help direct future research and implementation. The optimization results of the example are very promising and it demonstrates the application value of the approach

Decision Making Analysis for an Integrated Risk Management Framework of Maritime Container Port Infrastructure and Transportation Systems

This research proposes a risk management framework and develops generic risk-based decision-making, and risk-assessment models for dealing with potential Hazard Events (HEs) and risks associated with uncertainty for Operational Safety Performance (OSP) in container terminals and maritime ports. Three main sections are formulated in this study: Section 1: Risk Assessment, in the first phase, all HEs are identified through a literature review and human knowledge base and expertise. In the second phase, a Fuzzy Rule Base (FRB) is developed using the proportion method to assess the most significant HEs identified. The FRB leads to the development of a generic risk-based model incorporating the FRB and a Bayesian Network (BN) into a Fuzzy Rule Base Bayesian Network (FRBN) method using Hugin software to evaluate each HE individually and prioritise their specific risk estimations locally. The third phase demonstrated the FRBN method with a case study. The fourth phase concludes this section with a developed generic risk-based model incorporating FRBN and Evidential Reasoning to form an FRBER method using the Intelligence Decision System (IDS) software to evaluate all HEs aggregated collectively for their Risk Influence (RI) globally with a case study demonstration. In addition, a new sensitivity analysis method is developed to rank the HEs based on their True Risk Influence (TRI) considering their specific risk estimations locally and their RI globally. Section 2: Risk Models Simulations, the first phase explains the construction of the simulation model Bayesian Network Artificial Neural Networks (BNANNs), which is formed by applying Artificial Neural Networks (ANNs). In the second phase, the simulation model Evidential Reasoning Artificial Neural Networks (ERANNs) is constructed. The final phase in this section integrates the BNANNs and ERANNs that can predict the risk magnitude for HEs and provide a panoramic view on the risk inference in both perspectives, locally and globally. Section 3: Risk Control Options is the last link that finalises the risk management based methodology cycle in this study. The Analytical Hierarchal Process (AHP) method was used for determining the relative weights of all criteria identified in the first phase. The last phase develops a risk control options method by incorporating Fuzzy Logic (FL) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to form an FTOPSIS method. The novelty of this research provides an effective risk management framework for OSP in container terminals and maritime ports. In addition, it provides an efficient safety prediction tool that can ease all the processes in the methods and techniques used with the risk management framework by applying the ANN concept to simulate the risk models