WO2024045539A1 - 一种欠驱商船自主靠离泊测试方法及系统 - Google Patents

一种欠驱商船自主靠离泊测试方法及系统 Download PDF

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WO2024045539A1
WO2024045539A1 PCT/CN2023/079763 CN2023079763W WO2024045539A1 WO 2024045539 A1 WO2024045539 A1 WO 2024045539A1 CN 2023079763 W CN2023079763 W CN 2023079763W WO 2024045539 A1 WO2024045539 A1 WO 2024045539A1
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data
underdriven
ship
berthing
merchant
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PCT/CN2023/079763
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French (fr)
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白旭
凌浩
张茜
李雨珊
李永正
罗小芳
杨立
张海华
刘启新
孙宇
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江苏科技大学
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Publication of WO2024045539A1 publication Critical patent/WO2024045539A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B79/00—Monitoring properties or operating parameters of vessels in operation
    • B63B79/30—Monitoring properties or operating parameters of vessels in operation for diagnosing, testing or predicting the integrity or performance of vessels

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  • the invention relates to an autonomous berthing and unberthing test method and system for an underdriven merchant ship.
  • Unmanned ships refer to the use of sensors, communications, Internet of Things, Internet and other technical means to automatically perceive and obtain information and data on the ship itself, marine environment, logistics, ports, etc., and are based on computer technology, automatic control technology and big data processing and analysis technology to realize intelligent operation of ships in ship navigation, management, maintenance, cargo transportation, etc., to make ships safer, more environmentally friendly, more economical and more reliable, research and market demand for unmanned ships in the field of marine equipment manufacturing increasingly widespread.
  • the berthing and unberthing operation is an important link in the entire maritime navigation process of traditional ships. It is also one of the most complex and difficult operations. Port entry and exit technology is also an important technology for smart ships. Smart ship entry and exit technology not only controls the course and speed of the ship, but also includes berth stabilization, trajectory tracking, path planning, etc. Traditional ships are berthing and unberthing. There are risk factors caused by human uncertainty in the process.
  • the offshore working environment is complex. Traditional ships lack an evaluation and planning system that integrates the environment and their own data. It is difficult to make plans based on real-time conditions. The overall controllability is poor and the degree of intelligence is low. .
  • the present invention aims to provide a method and system that can evaluate the autonomous berthing and berthing test operations of underdriven merchant ships in real time, and can improve the testing efficiency and safety during the ship berthing and berthing process.
  • the autonomous berthing and unberthing test method for underdriven merchant ships includes the following steps:
  • Step S1 Use the sensing module to monitor water environment data, under-driven merchant ship position data and under-driven merchant ship status data, and transmit these three data to the data processing module;
  • Step S2 The data processing module summarizes the monitoring data of the sensing module and performs data fusion
  • Step S3 The fused data is used through the scenario generator to construct the situation information of underdriven merchant ships autonomously berthing and unberthing in waters, and the relevant information data is transmitted to the server database;
  • Step S4 The fused data is used to construct the motion status information of the underdriven merchant ship through the ship motion simulator, and the relevant information data is transmitted to the server database;
  • Step S5 The underdriven merchant ship algorithm module plans the ship berthing and unberthing route based on the water situation information and the unmanned ship motion status information;
  • Step S6 The evaluation module calls the server database in real time, compares the deviation of the actual sailing path of the underdriven merchant ship with the planned path, and evaluates the actual behavior of the underdriven merchant ship according to the evaluation criteria;
  • Step S7 The server controls the underdriven merchant ship based on the evaluation results. If the evaluation results are qualified, the steps will be repeated until the end of autonomous berthing and unberthing; if the evaluation results are unqualified, the test will be stopped and the test will be performed again after the problem is rectified.
  • the water environment data in step S1 includes: water wind speed and direction, flow speed and direction, temperature and humidity, and wave height and pressure.
  • the status data of the underdriven merchant ship includes: the speed, heading, acceleration and three-axis attitude angle of the underdriven merchant ship.
  • the underdriven merchant ship position data detection method is: GPS positioning method, acoustic positioning method, visual recognition positioning method and UWB positioning method work together to obtain the distance between the underdriven merchant ship and the berth and other targets.
  • the data fusion uses intermediate sensor fusion technology and Kalman filter to obtain fused accurate data;
  • the intermediate sensor fusion technology includes the following steps:
  • Step S21 Use the data measured for the first time by the sensing module as the initial measurement value
  • Step S22 The filter initializes the state and covariance matrix according to the initial measurement value
  • Step S23 The filter receives new measurement values within the preset time period, and the Bayesian algorithm gives a predicted value within this time period;
  • Step S24 Compare the measured value and the predicted value, and the filter applies a corresponding weight to each value
  • Step S25 The corresponding measurement value will be received within the next predetermined time period, and the algorithm executes the corresponding prediction value and update steps.
  • step S5 the planning of the ship's berthing and unberthing routes in step S5 includes the following steps:
  • Step S51 Divide the berthing process into two parts: the far terminal trajectory planning stage and the terminal end calm berthing stage;
  • Step S52 Conduct simulation research on the above two parts and optimize the artificial potential field method at the same time;
  • Step S53 Draw the trajectory planning and rudder angle output curve.
  • step S6 includes the following steps:
  • Step S61 Analyze the factors affecting berthing, call the key points and specifications of berthing, and obtain evaluation indicators;
  • Step S62 Classify the indicators and build an evaluation indicator system
  • Step S63 Use the analytic hierarchy process to compare the importance of each indicator in the autonomous ship berthing and unberthing test evaluation indicator system to obtain the weight Wi of each indicator; determine the indicator scoring and grading standards based on the data of the indicators at each test time. Score R i,t ;
  • Step S64 Combine the weight of each indicator Wi with the scoring score R i ,t to obtain the final score Si,t of each test indicator at the autonomous berthing and unberthing test monitoring time t of the underdriven merchant ship;
  • Step S65 Comprehensive scores of various indicators are obtained to obtain the overall autonomous berthing and unberthing score S t at time t.
  • the analytic hierarchy process is used to determine the weight Wi , including the following steps:
  • Step S631 Based on the evaluation index, establish a hierarchical structure describing all structural factors
  • Step S632 Compare structural factors pairwise and construct all judgment matrices
  • Step S633 Interpret the interpretation matrix to obtain the eigenroots and eigenvectors, and check whether each judgment matrix has complete consistency. If the consistency condition is not met, the judgment matrix must be modified until it is satisfied;
  • Step S634 Calculate the combined weight of factors at each level and check the structural consistency.
  • the autonomous berthing and unberthing test system for underdriven merchant ships includes a sensing module to monitor the water environment and the status of underdriven merchant ships; the data processing module receives and processes the data monitored by the sensing module; the algorithm module calls the server for data processing The module fuses data and performs path planning; the display module calls the server information, performs graphics processing, and displays relevant information on the display for the autonomous berthing and unberthing test of an underdriven merchant ship.
  • the data processing module also includes a scene generator, a ship motion simulator and a data fusion module.
  • the data processing module receives data monitored from the sensing module and performs data fusion; based on the fused data, the scene generator Water situation information is constructed, and the ship motion simulator constructs underdriven merchant ship motion information.
  • the present invention has the following significant advantages: in monitoring and data processing of water environment and ship status, constructing water situation information and ship movement information, on this basis, path planning and evaluation module Compare the deviation between the actual path and the planned path, and automatically score according to the established index evaluation standards, which improves the test efficiency; secondly, when constructing the autonomous berthing and unberthing evaluation index and scoring of underdriven merchant ships, the analytic hierarchy process is used to reduce the determination weight and subjective evaluation. It improves the accuracy of the final result and increases the safety of the ship during the berthing process.
  • Figure 1 is a schematic flow chart of the method of the present invention
  • Figure 2 is a schematic diagram of the system structure of the present invention.
  • An underdriven merchant ship autonomous berthing and unberthing test method and its system as shown in Figure 1, include the following steps:
  • Step S1 use the sensing module to monitor water environment data, under-driven merchant ship position data and under-driven merchant ship status data and transmit these three data to the data processing module;
  • Step S2 The data processing module summarizes the monitoring data of the sensing module and performs data fusion
  • Step S3 The fused data is used through the scenario generator to construct the situation information of underdriven merchant ships autonomously berthing and unberthing in waters, and the relevant information data is transmitted to the server database;
  • Step S4 The fused data is used to construct the motion status information of the underdriven merchant ship through the ship motion simulator, and the relevant information data is transmitted to the server database;
  • Step S5 The underdriven merchant ship algorithm module plans the ship berthing and unberthing route based on the water situation information and the unmanned ship motion status information;
  • Step S6 The evaluation module calls the server database, compares the deviation between the actual sailing path of the underdriven merchant ship and the planned path, and evaluates the actual behavior of the underdriven merchant ship according to the evaluation criteria;
  • Step S7 The server controls the underdriven merchant ship based on the evaluation results. If the evaluation results are qualified, the steps will be repeated until the end of autonomous berthing and unberthing; if the evaluation results are unqualified, the test will be stopped and the test will be performed again after the problem is rectified.
  • the perception module monitors the water environment and the status of underdriven merchant ships;
  • the data processing module receives and processes the data monitored by the perception module;
  • the algorithm module calls the server Fusion data from the data processing module and perform path planning;
  • the evaluation module calls the server's real-time motion status information and path planning information of the underdriven merchant ship;
  • the display module calls the server information, performs graphics processing, and performs autonomous berthing and unberthing testing of the underdriven merchant ship through the display Relevant information is presented.
  • step S1 the sensing module monitors the water environment and the status (positioning, attitude) of the underdriven merchant ship, and transmits the data to the data processing module; in this step, the status data of the underdriven merchant ship is detected.
  • the position of the underdriven merchant ship is positioned.
  • the positioning method combines GPS positioning, acoustic positioning, visual recognition positioning and UWB positioning. Multiple positioning methods work together to ensure that the position of the underdriven merchant ship can be accurately determined under any circumstances, thereby obtaining information about the underdriven merchant ship.
  • the distance between the merchant ship and the berth and other targets is used as the position data of the under-driven merchant ship; the attitude of the under-driven merchant ship is measured by the inertial measurement unit in the sensing module, including the three-axis attitude angle and acceleration, which are converted through the coordinate system.
  • the course and speed of the unmanned ship on the water surface are used as status data of the underdriven merchant ship; on the basis of learning the status data of the underdriven merchant ship, the water environment is detected.
  • the sensing module includes a sensor for measuring and recording the wave height in the water area.
  • the data processing module summarizes the monitoring data of the sensing module and performs data fusion; in this step, mid-level sensing is used
  • the sensor fusion technology includes the Kalman filter combined with the Kalman filter to fuse the data measured by the perception module, and the most accurate fusion data shall prevail; the specific implementation process is to initialize the merchant ship and ship based on the first measured data of the perception module.
  • the Kalman filter receives new sensor measurement values, the algorithm gives a predicted value, the filter compares the predicted value with the actual measured value and combines it to give the fusion within the time period data, in which the Kalman filter gives different weights to the predicted and measured values based on the uncertainty of each value, then in the next T time period another measured value is received and the algorithm gives another predicted value, Thus begins the next comparison and fusion of data.
  • the fused data is used through the scenario generator to construct the situation information of underdriven merchant ships autonomously berthing and unberthing in waters, and the relevant information data is transmitted to the server database; in other words, through the scenario generator in the data processing module, a simulation of underdriven merchant ships berthing and unberthing in waters is established. environment, and assign real-time fusion data to the simulation environment, and then transmit the simulation environment to the server database.
  • the fused data is used to construct under-driven merchant ship motion status information through the ship motion simulator, and the relevant information data is transmitted to the server database; an under-driven merchant ship model with real-time motion status information is established and transmitted to the server database.
  • the underdriven merchant ship algorithm module plans the ship's berthing and berthing route based on the water situation information and the unmanned ship's motion status information.
  • the server performs trajectory planning for the underdriven merchant ship task, and the presence of the underdriven merchant ship is a relatively certain reminder. Therefore, the process of berthing at the wharf is divided into two parts: the trajectory planning stage of the far wharf and the calm berthing at the end of the wharf. Simulation studies are conducted on the two parts respectively.
  • the traditional artificial potential field method was optimized. The optimization process is as follows: considering that the merchant ship is large, the obstacle affects a wide distance, and the excessive gravity may encounter the obstacle ship, the range is increased based on the traditional gravity function of the present invention.
  • the repulsion function is optimized to add the influence of the distance between the target and the ship on the repulsion force; when the ship approaches the transition area, although the surrounding obstacles are The repulsive force field generated by the object increases, but the distance between the two is shortened, so it weakens the repulsive force field and solves the problem of excessive gravity and unreachable targets.
  • the trajectory planning and rudder angle output curves are drawn. , which provides a basis for choosing an appropriate route for ship navigation before entering the berth.
  • the evaluation module calls the server database in real time, compares the deviation between the actual sailing path of the under-driven merchant ship and the planned path, and evaluates the actual behavior of the under-driven merchant ship based on the evaluation standards; specifically, it uses a combination of evidence theory and the analytic hierarchy process to evaluate the autonomous approach of unmanned ships. Compare the importance of each indicator in the off-berth test evaluation indicator system to obtain the weight Wi of each indicator; determine the indicator scoring and grading standards, and score R i,t based on the indicator data at each test time; compare the weight of each indicator with the scoring score Combined, the final scores of each test indicator at the monitoring time t of the underdriven merchant ship's autonomous berthing and unberthing test are obtained.
  • the steps of determining the weight Wi by the analytic hierarchy process include: establishing an independent hierarchical structure that describes the system functions or features; comparing structural factors pairwise to construct all judgment matrices; interpreting the matrix to obtain the characteristic roots and characteristics vector, and check whether each judgment matrix has complete consistency. If the consistency condition is not met, the judgment matrix must be modified until it is satisfied; the combined weight of factors at each layer is calculated, and the structural consistency is checked.
  • the server controls the underdriven merchant ship based on the evaluation results. If the evaluation result is qualified, the steps will be repeated until the end of autonomous berthing and unberthing; if the evaluation result is unqualified, the test will be stopped and the test will be carried out after the problem is rectified.
  • the autonomous under-driven merchant ship berthing and unberthing test system suitable for the above method is characterized by: including a sensing module, a data processing module, an algorithm module, a server, and an evaluation module.
  • a sensing module including a sensing module, a data processing module, an algorithm module, a server, and an evaluation module.
  • the data processing module includes a scene generator, ship motion simulator and data fusion module.
  • the sensing module monitors the water environment and the status of underdriven merchant ships, and the monitoring data is transmitted to the data processing module for processing;
  • the data processing module includes a scene generator, a ship motion simulator and a data fusion module, and the data processing module receives data monitored from the sensing module , perform data fusion, construct water situation information through the scene generator, construct underdriven merchant ship motion information through the ship motion simulator, and transmit relevant information data to the server;
  • the algorithm module calls the server's information from the data processing module to perform path planning, path planning
  • the information is transmitted to the server;
  • the evaluation module calls the server's real-time motion status information and path planning information of the underdriven merchant ship for evaluation.
  • the server controls the underdriven merchant ship based on the evaluation results.
  • the display module calls the server information, performs graphics processing, and displays the information related to the autonomous berthing and unberthing test of the underdriven merchant ship through the display.

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Abstract

一种欠驱商船自主靠离泊测试方法及系统，方法包括以下步骤：使用感知模块监测水域环境数据、欠驱商船位置数据和欠驱商船状态数据；数据处理模块汇总感知模块监测数据，进行数据融合；基于融合后的数据，场景生成器构建欠驱商船自主靠离泊水域态势信息，船舶运动模拟器构建欠驱商船运动状态信息，相关信息数据传输至服务器数据库；算法模块依据相关信息数据，规划出船舶靠离泊路线；测评模块对实际欠驱商船行为进行测评；测评结果合格则重复步骤至自主靠离泊结束，不合格停止测试排除问题。

Description

一种欠驱商船自主靠离泊测试方法及系统 技术领域
本发明涉及一种欠驱商船自主靠离泊测试方法及系统。
背景技术
无人船指利用传感器、通信、物联网、互联网等技术手段，自动感知和获得船舶自身、海洋环境、物流、港口等方面的信息和数据，并基于计算机技术、自动控制技术和大数据处理分析技术，在船舶航行、管理、维护保养、货物运输等方面实现智能化运行的船舶，以使船舶更加安全、更加环保、更加经济和更加可靠，无人船在海洋装备制造领域的研究和市场需求愈加广泛。
靠离泊操作是传统船舶在整个海上航行过程中重要的环节，也是最复杂、操作难度最大的过程之一。进出港技术同样也是智能船舶的一项重要技术，智能船舶进出港技术不仅对船舶的航向、航速进行控制，还包含了泊位镇定、轨迹跟踪、路径规划等多项内容；传统船舶在靠离泊过程中存在人为的不确定性带来的危险因素，海上工作环境复杂，传统船舶缺少对融合环境和自身数据的评估规划系统，难以根据实时情况做出规划，整体的可控性差智能化程度低。
发明内容
发明目的：本发明旨在提供一种能够实时测评欠驱商船自主靠离泊测试作业的方法及系统，能够提高船只离靠泊过程中测试效率及安全性。
技术方案：本发明所述的欠驱商船自主靠离泊测试方法，包括以下步骤：
步骤S1、使用感知模块监测水域环境数据、欠驱商船位置数据和欠驱商船状态数据，并将这三项数据传至数据处理模块；
步骤S2、数据处理模块汇总感知模块监测数据，进行数据融合；
步骤S3、融合后的数据通过场景生成器构建欠驱商船自主靠离泊水域态势信息，相关信息数据传输至服务器数据库；
步骤S4、融合后的数据通过船舶运动模拟器构建欠驱商船运动状态信息，相关信息数据传输至服务器数据库；
步骤S5、欠驱商船算法模块依据水域态势信息和无人船运动状态信息，规划出船舶靠离泊路线；
步骤S6、测评模块实时调用服务器数据库，对比欠驱商船实际航行路径与规划路径偏差，依据评判标准对实际欠驱商船行为进行测评；
步骤S7、服务器根据测评结果对欠驱商船进行调控，测评结果合格则重复步骤至自主靠离泊结束；测评结果不合格则停止测试，待问题排查后再进行测试。
其中，所述步骤S1中的水域环境数据包括：水域风速风向、流速流向、温度湿度以及浪高浪压，欠驱商船状态数据包括：欠驱商船的航速、航向、加速度以及三轴姿态角。
其中，所述欠驱商船位置数据检测方式为：GPS定位方式、声学定位方式、视觉识别定位方式和UWB定位方式相互协同作业，从而获得欠驱商船与泊位以及其他目标之间的距离。
其中，所述数据融合采用中级传感器融合技术和卡尔曼滤波器，从而得到融合后的精准数据；中级传感器融合技术包括以下步骤：
步骤S21：将感知模块首次测量的数据作为初始测量值；
步骤S22：滤波器根据初始测量值初始化状态和协方差矩阵；
步骤S23：在预设时间段内滤波器收到新的测量值，贝叶斯算法在此时间段内给出预测值；
步骤S24：将测量值和预测值对比，滤波器为每个值施加对应的权重；
步骤S25：在下一个预定时间段内将收到对应的测量值，算法执行对应的预测值和更新步骤。
其中，所述步骤S5中船舶靠离泊路线的规划包括以下步骤：
步骤S51：将靠泊过程分为远码头轨迹规划阶段和码头末端镇定靠泊两部分；
步骤S52：这上述两部分进行仿真研究，同时优化人工势场法；
步骤S53：绘制出航迹规划和舵角输出曲线。
其中，所述步骤S6中的测评过程包括以下步骤：
步骤S61：分析影响靠泊的因素，调用靠泊的要点和规范，获得评价指标；
步骤S62：对指标进行归纳分类并构建评价指标体系；
步骤S63：采用层次分析法，对无人船舶自主靠离泊测试评价指标体系中各指标进行重要性比较得到各指标的权重Wi；确定指标打分分级标准，依据每个测试时刻指标的数据进行打分Ri,t；
步骤S64：将各指标权重Wi与打分分数Ri,t相结合，得到欠驱商船自主靠离泊测试监测时刻t的各测试指标最终分数Si,t；
步骤S65：综合各个指标分数，得出t时刻自主靠离泊整体分数St。
其中，所述层次分析法用于确定权重Wi，包括以下步骤：
步骤S631：基于评价指标，建立描述所有结构因素的递阶层次结构；
步骤S632：两两比较结构因素，构造出所有的判断矩阵；
步骤S633：解判读矩阵得出特征根和特征向量，检验每个判断矩阵是否具有完全一致性若不满足一致性条件，则要修改判断矩阵直到满足为止；
步骤S634：计算各层因素的组合权重，并检验结构一致性。
本发明所述的欠驱商船自主靠离泊测试系统，包括感知模块，对水域环境和欠驱商船状态进行监测；数据处理模块接收并处理来自感知模块监测的数据；算法模块调用服务器来自数据处理模块的融合数据，并进行路径规划；显示模块调用服务器信息，进行图形处理后通过显示器进行欠驱商船自主靠离泊测试相关信息呈现。
其中，所述数据处理模块还包括场景生成器、船舶运动模拟器和数据融合模块，所述数据处理模块接收来自感知模块监测的数据，进行数据融合；基于融合后的数据，所述场景生成器构建水域态势信息，所述船舶运动模拟器构建欠驱商船运动信息。
有益效果：与现有技术相比，本发明具有如下的显著优点：在对水域环境以及船舶状态进行监测和数据处理，构建水域态势信息以及船舶运动信息，在此基础上进行路径规划，测评模块对比实际路径与规划路径偏差，依据既定指标评判标准自动打分，提高了测试效率；其次，在构建欠驱商船自主靠离泊测评指标以及进行打分时，采用层次分析法降低了确定权重，测评主观性，提升最终结果的正确性，增加了船舶在靠泊过程中的安全性。
附图说明
图1为本发明的方法流程示意图；
图2为本发明的系统结构示意图。
具体实施方式
下面结合附图1-2对本发明的技术方案作进一步说明，一种欠驱商船自主靠离泊测试方法及其系统，如图1所示，包括以下步骤：
步骤S1、使用感知模块监测水域环境数据、欠驱商船位置数据和欠驱商船状态数 据，并将这三项数据传至数据处理模块；
步骤S2、数据处理模块汇总感知模块的监测数据，并进行数据融合；
步骤S3、融合后的数据通过场景生成器构建欠驱商船自主靠离泊水域态势信息，相关信息数据传输至服务器数据库；
步骤S4、融合后的数据通过船舶运动模拟器构建欠驱商船运动状态信息，相关信息数据传输至服务器数据库；
步骤S5、欠驱商船算法模块依据水域态势信息和无人船运动状态信息，规划出船舶靠离泊路线；
步骤S6、测评模块调用服务器数据库，对比欠驱商船实际航行路径与规划路径偏差，依据评判标准对实际欠驱商船行为进行测评；
步骤S7、服务器根据测评结果对欠驱商船进行调控，测评结果合格则重复步骤至自主靠离泊结束；测评结果不合格则停止测试，待问题排查后再进行测试。
如图2所示，包括感知模块、数据处理模块、测评模块以及显示模块；感知模块对水域环境和欠驱商船状态进行监测；数据处理模块接收并处理来自感知模块监测的数据；算法模块调用服务器来自数据处理模块的融合数据，并进行路径规划；测评模块调用服务器欠驱商船实时运动状态信息以及路径规划信息；显示模块调用服务器信息，进行图形处理后通过显示器进行欠驱商船自主靠离泊测试相关信息呈现。
在步骤S1中：感知模块对水域环境和欠驱商船状态(定位、姿态)进行监测，数据传输至数据处理模块；在此步骤中对欠驱商船状态数据进行检测，具体言之，首先，对欠驱商船的位置进行定位，其定位方式融合了GPS定位、声学定位视觉识别定位和UWB定位，多种定位方式协同作业保证在任何情况下都能精准确定欠驱商船的位置，从而获取欠驱商船与泊位以及其他目标之间的距离，以此作为欠驱商船的位置数据；欠驱商船的姿态有感知模块中的惯性测量单元测得，包括三轴姿态角、加速度，通过坐标系转化得出水面无人船的航向和速度，以此作为欠驱商船的状态数据；在获悉欠驱商船状态数据的基础上，对水域环境进行检测，感知模块中包括用于测量和记录水域波浪高度的浪高仪、用于测量和记录水域波浪压力的浪压仪以及用于测量和记录水域风速以及风向仪，还包括温度传感器用于测量和记录温度、湿度传感器用于测量和记录湿度、流速流向检测浮标，以此作为水域环境数据。
数据处理模块汇总感知模块监测数据，进行数据融合；在此步骤中，采用中级传感 器融合技术，包括卡尔曼滤波器结合卡尔曼滤波器对感知模块测得的数据进行融合，以最精确的融合数据为准；具体实施过程为，以感知模块首次测量的数据为基础初始化商船和协方差矩阵，预定时间段T内，卡尔曼滤波器收到新的传感器的测量值，算法给出预测值，滤波器将预测值和实际测量值进行比较并结合给出该时间段内的融合数据，在此过程中卡尔曼滤波器根据每个值的不确定性给预测值和测量值施加不同的权重，然后在下一个T时间段内接收另一个测量值，算法给出另一个预测值，从而开始下一次数据的比较和融合。
融合后的数据通过场景生成器构建欠驱商船自主靠离泊水域态势信息，相关信息数据传输至服务器数据库；换言之，通过数据处理模块中的场景生成器，建立欠驱商船靠离泊水域的模拟环境，并给模拟环境赋予实时的融合数据，再将模拟环境传输至服务器数据库中。融合后的数据通过船舶运动模拟器构建欠驱商船运动状态信息，相关信息数据传输至服务器数据库；建立具有实时运动状态信息的欠驱商船模型，并将其传输至服务器数据库。
欠驱商船算法模块依据水域态势信息和无人船运动状态信息，规划出船舶靠离泊路线；在本步骤中，服务器对欠驱商船任务进行轨迹规划，欠驱商船存在提醒较大的确定，由此将靠泊码头的过程分为远码头轨迹规划阶段和码头末端镇定靠泊两部分,对两部分分别进行仿真研究。同时对传统人工势场法进行了优化，其优化过程为：考虑到商船体型较大，障碍影响距离较广，引力过大可能会碰到障碍船，在本发明传统引力函数基础上增加了范围限定，避免由于离过渡区域太远导致的引力过大问题。鉴于过渡区域附近有障碍物，产生的斥力场大，导致船舶无法靠近目标点，因此对斥力函数进行优化，增添了目标和船舶距离对斥力的影响；当船舶靠近过渡区域时，虽然周围的障碍物对其产生的斥力场增大，但两者距离在缩短，所以起到对斥力场的削弱作用，解决引力过大和目标不可达的问题，在此基础上绘制航迹规划和舵角输出曲线，为进入泊位前的船舶航行选择合适的航线提供了依据。
测评模块实时调用服务器数据库，对比欠驱商船实际航行路径与规划路径偏差，依据评判标准对实际欠驱商船行为进行测评；具体而言，采用结合证据理论和层次分析法，对无人船舶自主靠离泊测试评价指标体系中各指标进行重要性比较得到各指标的权重Wi；确定指标打分分级标准，依据每个测试时刻指标的数据进行打分Ri,t；将各指标权重与打分分数相结合，得到欠驱商船自主靠离泊测试监测时刻t的各测试指标最终分数 Si,t，将各指标分数相加得出t时刻自主靠离泊整体分数St。其中，层次分析法确定权重Wi的步骤包括：建立描述系统功能或特征内部独立的递阶层次结构；两两比较结构因素，构造出所有的判断矩阵；解判读矩阵，得出特征根和特征向量，并检验每个判断矩阵是否具有完全一致性若不满足一致性条件，则要修改判断矩阵，直到满足为止；计算各层因素的组合权重，并检验结构一致性。
服务器根据测评结果对欠驱商船进行调控，测评结果合格则重复步骤至自主靠离泊结束；测评结果不合格则停止测试，待问题排查后再进行测试。
如图2所示适用于上述方法的欠驱商船自主靠离泊测试系统，欠驱商船自主靠离泊测试系统，其特征在于：包括感知模块、数据处理模块、算法模块、服务器、测评模块、预警模块和显示模块；数据处理模块包括场景生成器、船舶运动模拟器以及数据融合模块。感知模块对水域环境和欠驱商船状态进行监测，监测数据传输至数据处理模块进行处理；数据处理模块包括场景生成器、船舶运动模拟器和数据融合模块，数据处理模块接收来自感知模块监测的数据，进行数据融合，通过场景生成器构建水域态势信息，通过船舶运动模拟器构建欠驱商船运动信息，相关信息数据传输至服务器；算法模块调用服务器来自数据处理模块的信息，进行路径规划，路径规划信息传输至服务器；测评模块调用服务器欠驱商船实时运动状态信息以及路径规划信息，进行测评，服务器根据测评结果对欠驱商船进行调控，测评结果合格则重复步骤至自主靠离泊结束；测评结果不合格则停止测试，待问题排查后再进行测试。显示模块调用服务器信息，进行图形处理后通过显示器进行欠驱商船自主靠离泊测试相关信息呈现。

Claims (9)

  1. 一种欠驱商船自主靠离泊测试方法，其特征在于：包括以下步骤：
    步骤S1、使用感知模块监测水域环境数据、欠驱商船位置数据和欠驱商船状态数据，并将这三项数据传至数据处理模块；
    步骤S2、数据处理模块汇总感知模块的监测数据，并进行数据融合；
    步骤S3、融合后的数据通过场景生成器构建欠驱商船自主靠离泊水域态势信息，相关信息数据传输至服务器数据库；
    步骤S4、融合后的数据通过船舶运动模拟器构建欠驱商船运动状态信息，相关信息数据传输至服务器数据库；
    步骤S5、欠驱商船算法模块依据水域态势信息和无人船运动状态信息，规划出船舶靠离泊路线；
    步骤S6、测评模块调用服务器数据库，对比欠驱商船实际航行路径与规划路径偏差，依据评判标准对实际欠驱商船行为进行测评；
    步骤S7、服务器根据测评结果对欠驱商船进行调控，测评结果合格则重复步骤至自主靠离泊结束；测评结果不合格则停止测试，待问题排查后再进行测试。
  2. 根据权利要求1所述的一种欠驱商船自主靠离泊测试方法，其特征在于：所述步骤S1中的水域环境数据包括：水域风速风向、流速流向、温度湿度以及浪高浪压，欠驱商船状态数据包括：欠驱商船的航速、航向、加速度以及三轴姿态角。
  3. 根据权利要求1所述的一种欠驱商船自主靠离泊测试方法，其特征在于：步骤S1中，所述欠驱商船位置数据检测方式为：GPS定位方式、声学定位方式、视觉识别定位方式和UWB定位方式相互协同作业，从而获得欠驱商船与泊位以及障碍之间的距离。
  4. 根据权利要求1所述的一种欠驱商船自主靠离泊测试方法，其特征在于：步骤S2中，所述数据融合采用中级传感器融合技术-卡尔曼滤波，从而得到融合后的精准数据；中级传感器融合技术包括以下步骤：
    步骤S21：将感知模块首次测量的数据作为初始测量值；
    步骤S22：滤波器根据初始测量值初始化状态和协方差矩阵；
    步骤S23：在预设时间段内滤波器收到新的测量值，算法在此时间段内给出预测值；
    步骤S24：将测量值和预测值对比，滤波器为每个值施加对应的权重；
    步骤S25：在下一个预定时间段内将收到对应的测量值，算法执行对应的预测值和 更新步骤。
  5. 根据权利要求1所述的一种欠驱商船自主靠离泊测试方法，其特征在于：所述步骤S5中船舶靠离泊路线的规划包括以下步骤：
    步骤S51：将靠泊过程分为远码头轨迹规划阶段和码头末端镇定靠泊两部分；
    步骤S52：对上述两部分进行仿真研究，同时优化人工势场法；
    步骤S53：绘制出航迹规划和舵角输出曲线。
  6. 根据权利要求1所述的一种欠驱商船自主靠离泊测试方法，其特征在于：所述步骤S6中的测评过程包括以下步骤：
    步骤S61：分析影响靠泊的因素，调用靠泊的要点和规范，获得评价指标；
    步骤S62：对指标进行归纳分类并构建评价指标体系；
    步骤S63：采用层次分析法，对无人船舶自主靠离泊测试评价指标体系中各指标进行重要性比较得到各指标的权重Wi；确定指标打分分级标准，依据每个测试时刻指标的数据进行打分Ri,t；
    步骤S64：将各指标权重Wi与打分分数Ri,t相结合，得到欠驱商船自主靠离泊测试监测时刻t的各测试指标最终分数Si,t；
    步骤S65：综合各个指标分数，得出t时刻自主靠离泊整体分数St。
  7. 根据权利要求6所述的一种欠驱商船自主靠离泊测试方法，其特征在于：步骤S63中，所述将证据理论和层次分析法组合起来用于确定权重Wi，包括以下步骤：
    步骤S631：基于评价指标，建立描述所有结构因素的递阶层次结构；
    步骤S632：两两比较结构因素，构造出所有的判断矩阵；
    步骤S633：解判读矩阵得出特征根和特征向量，检验每个判断矩阵是否具有完全一致性若不满足一致性条件，则要修改判断矩阵直到满足为止；
    步骤S634：计算各层因素的组合权重，并检验结构一致性。
  8. 一种使用如权利要求1中所述方法的欠驱商船自主靠离泊测试系统，其特征在于：包括感知模块、数据处理模块、测评模块以及显示模块；感知模块对水域环境和欠驱商船状态进行监测；数据处理模块接收并处理来自感知模块监测的数据；算法模块调用服务器来自数据处理模块的融合数据，并进行路径规划；测评模块调用服务器欠驱商船实时运动状态信息以及路径规划信息；显示模块调用服务器信息，进行图形处理后通过显示器进行欠驱商船自主靠离泊测试相关信息呈现。
  9. 根据权利要求8所述的一种欠驱商船自主靠离泊测试系统，其特征在于：所述数据处理模块还包括场景生成器、船舶运动模拟器和数据融合模块，所述数据处理模块接收来自感知模块监测的数据，数据融合模块进行数据融合；基于融合后的数据，所述场景生成器构建水域态势信息，所述船舶运动模拟器构建欠驱商船运动信息。
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CN112859867A (zh) * 2021-01-19 2021-05-28 武汉理工大学 一种基于多拖轮协同的船舶靠离泊控制系统和方法
CN115258093A (zh) * 2022-08-30 2022-11-01 江苏科技大学 一种欠驱商船自主靠离泊测试方法及系统

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