Ship motion model parameter estimation system, motion model parameter estimation method, and program

Abstract

To improve accuracy while reducing processing load in parameter estimation of a ship motion model using an optimization problem. [Solution] The ship parameter estimation system has an acquisition unit that acquires the added mass and added moment of inertia of the ship, an extraction unit that extracts a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing, a first estimation unit that uses the acquired added mass and added moment of inertia and a first parameter set from the extracted plurality of parameter sets to estimate a first motion model parameter of the ship in a first motion pattern corresponding to the first parameter set, and a second estimation unit that uses a second parameter set from the extracted plurality of parameter sets and the estimated first motion model parameter to estimate a second motion model parameter of the ship in a second motion pattern corresponding to the second parameter set.

Description

[Technical Field]

[0001] The present invention relates to a system for estimating parameters of a motion model of a ship, a method for estimating parameters of a motion model, and a program. [Background technology]

[0002] Conventionally, techniques have been developed for controlling the navigation of a ship based on a motion model of the ship.

[0003] For example, Patent Document 1 discloses a configuration for controlling automatic steering in which control parameters are set by making dimensionless motion parameters dimensional when the ship speed changes. Also, Patent Document 2 discloses a configuration for correcting engine control parameters based on actual measured values ​​from sensors, etc., in relation to a ship autopilot. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2023-139851 [Patent Document 2] Patent No. 6430985 Summary of the Invention [Problem to be solved by the invention]

[0005] The control model used for ship autopiloting must be set after identifying the parameters of the motion model based on the results of various tests and simulations, taking into account various factors such as the shape, size, and specifications of the ship. This identification process requires a lot of cost and time, especially since ships are often produced individually to order rather than mass-produced identically. This means that the parameters of the motion model must be identified individually according to the characteristics of each ship, which increases the cost and time required to identify the parameters of the motion model.

[0006] For example, nonlinear programming, a type of optimization problem, is used to estimate parameters for controlling a ship. When estimating a large number of parameters at once, this method requires a long time for estimation and increases the processing load. Furthermore, depending on the initial value settings, the method is more likely to fall into a local optimum. Falling into a local optimum can make it difficult to achieve sufficient accuracy in controlling the ship.

[0007] In view of the above problems, an object of the present invention is to improve accuracy while reducing the processing load in estimating parameters of a ship motion model using an optimization problem. [Means for solving the problem]

[0008] In order to solve the above problems, one aspect of the present invention has the following configuration: A parameter estimation system for estimating parameters of a ship motion model, comprising: an acquisition unit that acquires an added mass and an added moment of inertia of the vessel; an extracting unit that extracts a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation unit that estimates a first motion model parameter of the ship in a first motion pattern corresponding to the first parameter set, using the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set of the plurality of parameter sets extracted by the extraction unit; a second estimation unit that estimates second motion model parameters of the ship in a second motion pattern corresponding to the second parameter set, using a second parameter set of the plurality of parameter sets extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; and It has.

[0009] Another aspect of the present invention has the following configuration: A parameter estimation method for estimating parameters of a ship motion model, comprising the steps of: acquiring an added mass and an added moment of inertia of the vessel; an extraction step of extracting a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation step of estimating first motion model parameters of the ship in a first motion pattern corresponding to a first parameter set, using the added mass and added moment of inertia acquired in the acquisition step and a first parameter set of the plurality of parameter sets extracted in the extraction step; a second estimation step of estimating second motion model parameters of the ship in a second motion pattern corresponding to a second parameter set, using a second parameter set of the plurality of parameter sets extracted in the extraction step and the first motion model parameters estimated in the first estimation step; It has.

[0010] Another aspect of the present invention has the following configuration: a program comprising: Computer, an acquisition unit that acquires the added mass and added moment of inertia of the vessel; an extraction unit that extracts a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation unit that estimates first motion model parameters of the ship in a first motion pattern corresponding to the first parameter set, using the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set of the plurality of parameter sets extracted by the extraction unit; a second estimation unit that estimates second motion model parameters of the ship in a second motion pattern corresponding to the second parameter set, using a second parameter set of the plurality of parameter sets extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; Function as. [Effects of the Invention]

[0011] According to the present invention, it is possible to improve accuracy while reducing the processing load in estimating parameters of a ship motion model using an optimization problem. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a parameter estimation system for an exercise model according to a first embodiment of the present invention. [Figure 2] FIG. 1 is a block diagram showing an example of the configuration of a ship according to a first embodiment of the present invention. [Figure 3] Schematic diagram of step-by-step parameter estimation according to the first embodiment of the present invention. [Figure 4] 1 is a flowchart of a parameter estimation process according to a first embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the embodiments described below are merely examples for explaining the present invention and are not intended to limit the present invention. Furthermore, not all of the configurations described in each embodiment are necessarily essential configurations for solving the problems of the present invention. Furthermore, in each drawing, the same components are assigned the same reference numerals to indicate their correspondence. Note that in order to avoid unnecessary redundancy and to facilitate understanding by those skilled in the art, some of the description may be omitted or simplified. For example, detailed descriptions of already well-known matters or redundant descriptions of substantially identical configurations may be omitted.

[0014] First Embodiment [System Configuration] FIG. 1 is a schematic diagram showing an example of the configuration of a parameter estimation system (hereinafter simply referred to as the "parameter estimation system") that estimates parameters of a motion model according to a first embodiment of the present invention. The motion model parameter estimation system 100 is a system for estimating parameters of a motion model related to automatic navigation of a ship and constructing a control model corresponding to the ship. Here, automatic navigation is not limited to navigation in a mode in which no navigation by the user is required, but may also include navigation in a mode in which partial assistance is provided to the user's navigation. The motion model parameter estimation system 100 may be used in a form in which it is mounted on the ship, or may be configured as a separate device that is configured to be able to provide the ship with a control model based on a motion model whose parameters have been estimated. The parameter estimation system 100 may be configured, for example, as an information processing device such as a PC (Personal Computer), or as a control device such as an ECU (Electronic Control Unit) mounted on the ship.

[0015] The parameter estimation system 100 includes a control unit 101, a storage unit 102, an input unit 103, an output unit 104, and a communication unit 105. Each unit is configured to be able to communicate with each other via an internal bus or the like.

[0016] The control unit 101 controls the operation of the parameter estimation system 100. The control unit 101 is composed of, for example, a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and provides various functions by reading and executing various programs and data stored in the storage unit 102.

[0017] The storage unit 102 is a storage device for storing programs, data, etc. for executing various control processes and functions by the control unit 101. The storage unit 102 is configured from volatile / non-volatile storage devices such as RAM (Random Access Memory), ROM (Read Only Memory), HDD (Hard Disk Drive), and flash memory.

[0018] The input unit 103 is configured with a mouse, keyboard, microphone, etc., and accepts input of operations from a user, etc. The output unit 104 is configured with a display, speaker, etc., and outputs various types of data. The output by the output unit 104 may be any of visual, auditory, and tactile output, such as an image, sound, or vibration. Furthermore, the input unit 103 and the output unit 104 may be configured as an integrated unit using a touch panel display, etc.

[0019] The communication unit 105 is a communication interface for communicating with external devices via a network (not shown). The communication unit 105 may be configured to be compatible with multiple communication standards depending on the configuration of the network. The network may be configured, for example, by the Internet, an intranet, a wireless LAN (Local Area Network), or a WAN (Wide Area Network). Note that the communication standards and whether the network is wired or wireless are not particularly limited, and the network may be configured by combining multiple communication standards.

[0020] 2 is a block diagram showing an example of the configuration of a ship according to this embodiment. The ship 200 is configured to be capable of automatic navigation using a control model constructed based on the estimation results of the parameters of the motion model according to this embodiment. The ship 200 includes a control unit 201, a storage unit 202, an operation unit 203, a drive unit 204, an output unit 205, a sensor unit 206, an antenna unit 207, and a camera 208.

[0021] The control unit 201 controls the ship 200. For example, the control unit 201 is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a dedicated circuit, etc., and provides various functions by reading and executing various programs and data stored in the storage unit 202.

[0022] The storage unit 202 is a storage device for storing programs, data, etc. for executing various control processes and functions provided by the control unit 201. The storage unit 202 is configured from volatile / non-volatile storage devices such as a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD (Hard Disk Drive), and a flash memory.

[0023] The operation unit 203 receives various operations related to the navigation of the vessel 200. The operation unit 203 may be configured to include a throttle lever, a steering wheel, a joystick lever, a shift lever, various switches, and the like. The operations here may include operations related to navigation such as steering, acceleration, and deceleration, as well as operations related to each part of the vessel 200. The drive unit 204 performs operations related to the navigation of the vessel 200 based on instructions from the control unit 201 and operations from the operation unit 203. The drive unit 204 is an engine (main engine) for propelling the vessel 200, and may be configured to include, for example, an engine, a motor, a thruster, a propeller, a pump, and the like. Note that the type of the drive unit 204 is not particularly limited, and any configuration may be used as long as it is capable of automatic navigation using parameter estimation of a motion model and a control model, which will be described later.

[0024] The output unit 205 is composed of a display, a speaker, etc., and outputs various types of data. The output by the output unit 205 may be any of visual, auditory, and tactile output, such as an image, sound, or vibration. For example, the output unit 205 may display various types of data acquired by the sensor unit 206, the antenna unit 207, and the camera 208 on a predetermined user interface screen. The output unit 205 may also output information for performing navigation using map information, etc.

[0025] The sensor unit 206 is composed of multiple sensors for acquiring information about the surroundings and interior of the vessel 200. The sensor unit 206 may include multiple types of sensors, such as a wind direction and speed sensor, an acceleration sensor, a temperature sensor, a humidity sensor, a Light Detection and Ranging (LiDAR), a radar, a gyro sensor, a position sensor, a rotation sensor, and a sonar. Furthermore, multiple sensors of the same type may be provided. The various sensors included in the sensor unit 206 may be configured to acquire information about the vessel 200 as a whole, as well as information about specific parts of the vessel 200. The antenna unit 207 is a component for transmitting and receiving information to and from the outside. The antenna unit 207 may include multiple types of antennas, such as a Global Navigation Satellite System (GNSS) antenna and an Automatic Identification System (AIS) antenna. The camera 208 is an imaging device for acquiring images of the surroundings of the vessel 200. The direction of images that can be acquired by the camera 208 is not particularly limited, and the camera 208 may be configured to acquire images in all directions.

[0026] [Movement model parameter estimation] The following describes parameter estimation of a motion model related to automatic navigation of the ship 200 in this embodiment.

[0027] In this example, the ENU coordinate system will be used for explanation. The ENU coordinate system is a three-dimensional coordinate system consisting of the E axis (East), N axis (North), and U axis (Up), and the current position of the ship 200 is set as the origin. The E axis corresponds to the X direction, the N axis corresponds to the Y direction, and the U axis corresponds to the Z direction, with the North direction, East direction, and Up direction being positive directions. Furthermore, thrust that moves the hull in the positive direction of each axis is considered positive. The negative direction of the X axis (towards the stern) is considered to be a rudder angle of 0.

[0028] 3 is a schematic diagram showing the flow of control model construction including motion model parameter estimation according to this embodiment. The motion model parameter estimation according to this embodiment uses a navigation log 310 acquired when the ship 200 actually navigates. In this embodiment, the navigation log 310 includes control information and navigation result information.

[0029] The control information is information about control parameters as input to the vessel 200, and includes the propeller rotation speed (hereinafter simply referred to as "rotation speed") and rudder angle. The navigation result information is information as output from the vessel 200 controlled based on the control information, and includes the speed and acceleration during navigation. The information included in the navigation log 310 here is an example and may include other information. For example, the control information may further include torque, the propeller rotation direction, etc. Each piece of information included in the navigation log 310 is acquired by the sensor unit 206, antenna unit 207, and camera 208 mounted on the vessel 200, and is recorded in association with time information at the time of acquisition, etc. The navigation log 310 may be recorded during manual navigation or automatic navigation. For example, the various pieces of information included in the navigation log 310 are listed below in chronological order.

[0030]

number

[0031] In parameter estimation according to this embodiment, multiple movement patterns related to navigation are recognized from the information shown in the navigation log 310, and data corresponding to the movement patterns are extracted (recognition process 311). Then, parameters are estimated in multiple stages using the data extracted according to the movement patterns. This reduces the number of parameters in each estimation process and reduces the processing load.

[0032] In the example of FIG. 3, acceleration / deceleration and turning are assumed as motion patterns, and parameter estimation is performed in a stepwise manner in this order. For convenience, these are shown as a first estimation process 320 and a second estimation process 330. The estimation results of the earlier stages are configured to be usable in the estimation processes of the later stages. In the example of FIG. 3, the second estimation process 330, which is the second stage, is the final estimation process, and as a result of the second estimation process 330, parameters of a motion model for the entire ship 200 are obtained. Note that the types and classifications of motion patterns are not limited to these, and may include, for example, constant speed navigation, fixed position keeping, and docking / undocking. Furthermore, the order and number of stages in which parameters are estimated are not limited to the configuration of FIG. 3, and the parameters may be estimated after being divided into stages according to the type and classification of the motion pattern, or the order may be reversed. Furthermore, rather than aiming to obtain parameters of the motion model for the entire ship 200, it is also possible to estimate only some parameters in a stepwise manner.

[0033] In this embodiment, a three-dimensional model 300 of the ship to be estimated is used. The three-dimensional model 300 may be configured as a mesh model, for example. The configuration and creation method of the three-dimensional model 300 are not particularly limited, and it is assumed that the three-dimensional model 300 is created in advance corresponding to the ship 200.

[0034] The three-dimensional model 300 is used to estimate the added mass and added moment of inertia (estimation process 301). For example, this estimation may use linear potential flow analysis using the boundary element method (BEM). Also, the added mass, damping coefficient, and wave excitation force (external force from waves) may be calculated in the frequency domain. There are no particular limitations on the processing method used, and known methods may be used. The following is obtained as the result 302 of the estimation process 301 based on the three-dimensional model 300.

[0035]

number

[0036] In the multi-stage parameter estimation shown in FIG. 3 , first estimation process 320 involves parameter estimation for the first stage of acceleration / deceleration. Response data 321 (e.g., X-direction velocity vx, X-direction acceleration ax, and propeller rotation speed rpm) in the acceleration / deceleration motion pattern is extracted from navigation log 310 and used. The response data 321 may be, for example, data representing a change in the propeller rotation speed and a predetermined state. A nonlinear model 322 corresponding to a predetermined acceleration / deceleration is also used. The nonlinear model 322 corresponding to acceleration / deceleration is not particularly limited, and may be, for example, a model using a primal-dual interior point method for solving nonlinear optimization problems or a simulated annealing method (e.g., dual annealing) for solving global optimization problems. Parameter estimation 323 of the motion model is performed using the nonlinear model 322 corresponding to acceleration / deceleration, the response data 321 extracted from navigation log 310, and the added mass and added moment of inertia. The model parameter estimation result 324 obtained here may include, for example, the following parameters:

[0037]

number

[0038] Next, in the multi-stage parameter estimation in FIG. 3 , the second-stage parameter estimation, which is the final stage, is performed as the second estimation process 3300. Response data 331 (angular velocity) in the turning motion pattern is extracted from the navigation log 310 and used. For example, transient response data from when the rudder angle changes during a turn while sailing at a constant speed until the angular velocity stabilizes may be used. A predefined nonlinear model 332 corresponding to the hull of the ship 200 is also used. The nonlinear model 332 corresponding to the hull of the ship 200 is not particularly limited, and may be, for example, a model using a simulated annealing method (such as dual annealing) for solving a global optimization problem. Parameter estimation of the motion model is performed using the nonlinear model 332 corresponding to the hull of the ship 200, the response data 331 extracted from the navigation log 310, and the results of the parameter estimation up to the previous stages. As a result of the model parameter estimation here, the motion model parameters of the entire ship 200, including the rotation speed vs. thrust characteristics of the main engine (propeller), etc., are obtained.

[0039] Then, the parameters of each motion model estimated in multiple stages are used to construct a control model for the ship 200 (construction process 340). By using the constructed control model, it becomes possible to control the automatic navigation of the ship 200.

[0040] [Processing flow] 4 is a flowchart of a process for estimating parameters of a motion model of the ship 200 according to this embodiment. This process flow is realized, for example, by the control unit 101 of the parameter estimation system 100 reading and executing a program and various data stored in the storage unit 102. Here, the description will be given assuming that the parameter estimation system 100 and the ship 200 are configured as separate devices. This process flow corresponds to a case in which parameters of the motion model are estimated through the two-stage process shown in FIG. 3. Therefore, the steps of the process flow may be adjusted as appropriate depending on the number of stages set in parameter estimation.

[0041] Before this processing flow is started, it is assumed that the target ship 200 has sailed and the sailing log 310 at that time is available. The contents of the sailing log 310 are not particularly limited, but it is assumed that the sailing log 310 includes data corresponding to the motion pattern of interest.

[0042] In step S401, the parameter estimation system 100 acquires the navigation log 310. The navigation log 310 to be acquired may be specified by the user, or may be the entire navigation log 310 collected over a certain period of time.

[0043] In step S402, the parameter estimation system 100 extracts data corresponding to each motion pattern from the navigation log 310 acquired in step S401. In the example of FIG. 3, response data corresponding to each of the acceleration / deceleration and turning motion patterns is extracted. The data to be extracted is assumed to be specified in advance according to the motion pattern. Note that the data extraction process may be performed collectively corresponding to each motion pattern, or may be divided into stages as in the parameter estimation process at a later stage.

[0044] In step S403, the parameter estimation system 100 uses the three-dimensional model 300 of the ship 200 to perform an estimation process 301 of the added mass and added moment of inertia of the ship.

[0045] In step S404, the parameter estimation system 100 performs a process of estimating parameters related to acceleration and deceleration using the response data 321 corresponding to the acceleration and deceleration motion pattern extracted in step S402, the data on the added mass and added moment of inertia estimated in step S403, and the nonlinear model 322 corresponding to acceleration and deceleration.

[0046] In step S405, the parameter estimation system 100 performs an estimation process of parameters related to the entire ship 200 using the response data 341 corresponding to the turning movement pattern extracted in step S402, the data estimated in step S404, and the nonlinear model 332 corresponding to the hull of the ship 200.

[0047] In step S406, the parameter estimation system 100 constructs a control model of the ship 200 using the parameters obtained in the processes of steps S403 to S405.

[0048] In step S407, the parameter estimation system 100 stores the control model of the vessel 200 constructed in step S406. The control model stored here is provided to the vessel 200 and used for control. Then, this processing flow ends.

[0049] As described above, this embodiment is configured to divide the estimation process into multiple stages to reduce the number of parameters handled at one time, and to use data extracted corresponding to each stage of the estimation process. This prevents the system from falling into a local optimum solution. Therefore, while using initial values ​​for added mass and the like based on a three-dimensional model of the ship, it is possible to accurately estimate the parameters of the motion model without being affected by a local optimum solution resulting from the initial values. This makes it possible to achieve highly accurate parameter estimation regardless of the size or specifications of the ship.

[0050] Furthermore, by configuring the estimation process in stages, it is possible to reduce the number of unknown parameters to be estimated in one estimation process and reduce the order of the optimization problem. This reduces the processing load of one estimation process and shortens the time required for the estimation process. Reducing the computational load enables effective use of computational resources such as processors and memory. This makes it possible, for example, to parallelize and distribute various processes, thereby improving the overall processing throughput.

[0051] Furthermore, in the above embodiment, when multi-stage processing is being performed, if an error occurs during the processing, there is no need to re-execute the entire processing, and correction can be made by re-executing only a portion of the processing, thereby improving the efficiency and maintainability of the system.

[0052] <Other embodiments> The specifications, characteristics, size, shape, etc. of a ship to which the above-described motion model parameter estimation process can be applied are not particularly limited. Furthermore, the navigation log may be collected by either automatic or manual operation of the ship.

[0053] In addition, in the present invention, a program or application for realizing the functions of one or more of the above-mentioned embodiments can be supplied to a system or device using a network or a storage medium, etc., and one or more processors in the computer of the system or device can read and execute the program.

[0054] Alternatively, it may be realized by a circuit that realizes one or more functions (for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array)).

[0055] Furthermore, in this specification, expressions such as "first" and "second" are used for convenience in describing components that are distinguished from other components, and are not intended to limit interpretation of specific components. Therefore, these expressions can be interpreted appropriately depending on, for example, the number of components and the order of processing.

[0056] Although various embodiments have been described above with reference to the drawings, it goes without saying that the present disclosure is not limited to these examples. It is clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents may be made within the scope of the claims, and it is understood that these also fall within the technical scope of the present disclosure. Furthermore, the components of the various embodiments described above may be combined in any manner without departing from the spirit of the invention.

[0057] As such, the present invention is not limited to the above-described embodiments, and the present invention also contemplates the mutual combination of the various components of the embodiments, as well as modifications and applications by those skilled in the art based on the description in the specification and well-known techniques, and these modifications and applications are included in the scope of protection sought.

[0058] As described above, the present specification discloses the following:

[0059] (Technology 1) A parameter estimation system (e.g., 100) for estimating parameters of a motion model of a vessel (e.g., 200), comprising: An acquisition unit (e.g., 101, 103) that acquires the added mass and added moment of inertia of the ship; an extraction unit (e.g., 101) that extracts a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation unit (e.g., 101) that estimates a first motion model parameter of the ship in a first motion pattern corresponding to the first parameter set, using the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set of the plurality of parameter sets extracted by the extraction unit; a second estimation unit (e.g., 101) that estimates second motion model parameters of the ship in a second motion pattern corresponding to the second parameter set, using a second parameter set of the plurality of parameter sets extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; A parameter estimation system having: With this configuration, for example, it is possible to reduce the processing load and improve accuracy in estimating parameters of a ship motion model using an optimization problem.

[0060] (Technology 2) The parameter estimation system described in Technology 1 further includes a third estimation unit (e.g., 101) that estimates a third motion model parameter of the ship in a third motion pattern corresponding to the third parameter set, using a third parameter set from the plurality of parameter sets extracted by the extraction unit and the second motion model parameter estimated by the second estimation unit. With this configuration, for example, the estimation process can be divided into more stages, and the processing load of one estimation process can be reduced.

[0061] (Technology 3) the first estimation unit estimates the first movement model parameters using a first nonlinear model corresponding to the first movement pattern; The parameter estimation system according to technique 1 or 2, wherein the second estimation unit estimates the second movement model parameters using a second nonlinear model corresponding to the second movement pattern. This configuration makes it possible to define and use a nonlinear model according to the parameter estimation at each stage, for example.

[0062] (Technology 4) The parameter estimation system according to technique 2, wherein the third estimation unit estimates the third motion model parameters using a third nonlinear model corresponding to the third motion pattern. This configuration makes it possible to define and use a nonlinear model according to the parameter estimation at each stage, for example.

[0063] (Technology 5) The parameter estimation system according to any one of Technology 1 to Technology 4, wherein the acquisition unit acquires the added mass and added moment of inertia of the ship by performing an estimation process using a three-dimensional model of the ship. With this configuration, for example, it is possible to estimate the added mass and added moment of inertia using a predefined three-dimensional model of the ship, and use the estimated values ​​as initial values ​​for parameter estimation.

[0064] (Technology 6) The parameter estimation system according to any one of Techniques 1 to 5, wherein the plurality of motion patterns are any one of acceleration / deceleration, constant speed navigation, turning, stationary position keeping, and docking / leaving. This configuration makes it possible to estimate parameters by assuming various movement patterns, for example.

[0065] (Technology 7) The parameter estimation system according to any one of techniques 1 to 6, wherein the navigation log includes a propeller rotation speed and a rudder angle as input data to the vessel, and a speed and an acceleration as output data from the vessel. With this configuration, for example, it becomes possible to use the control information of the ship and information about the ship that navigated based on the control information for parameter estimation.

[0066] (Technology 8) The extraction unit The speed, acceleration, and propeller rotation speed are extracted as a parameter set corresponding to the acceleration / deceleration motion pattern. The parameter estimation system according to any one of techniques 1 to 7, extracting a steering angle as a parameter set corresponding to a turning motion pattern. With this configuration, for example, it becomes possible to extract different data items from the navigation log depending on the ship's motion pattern and use them for parameter estimation.

[0067] (Technology 9) 1. A parameter estimation method for estimating parameters of a motion model of a vessel (e.g., 200), comprising: acquiring an added mass and an added moment of inertia of the vessel; an extraction step of extracting a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation step of estimating first motion model parameters of the ship in a first motion pattern corresponding to a first parameter set, using the added mass and added moment of inertia acquired in the acquisition step and a first parameter set of the plurality of parameter sets extracted in the extraction step; a second estimation step of estimating second motion model parameters of the ship in a second motion pattern corresponding to a second parameter set, using a second parameter set of the plurality of parameter sets extracted in the extraction step and the first motion model parameters estimated in the first estimation step; A parameter estimation method having With this configuration, for example, it is possible to reduce the processing load and improve accuracy in estimating parameters of a ship motion model using an optimization problem.

[0068] (Technology 10) A computer (e.g., 100) an acquisition unit (e.g., 101) for acquiring the added mass and added moment of inertia of the vessel; an extraction unit (e.g., 101) that extracts a plurality of parameter sets corresponding to a plurality of motion patterns from a navigation log when the ship is sailing; a first estimation unit (e.g., 101) that estimates a first motion model parameter of the ship in a first motion pattern corresponding to the first parameter set, using the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set of the plurality of parameter sets extracted by the extraction unit; a second estimation unit (e.g., 101) that estimates second motion model parameters of the ship in a second motion pattern corresponding to the second parameter set, using a second parameter set of the plurality of parameter sets extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; A program to function as a With this configuration, for example, it is possible to reduce the processing load and improve accuracy in estimating parameters of a ship motion model using an optimization problem. [Industrial Applicability]

[0069] The present invention is useful, for example, as an apparatus, system, method, and program for estimating parameters of a motion model of a ship. [Explanation of symbols]

[0070] 100...Parameter estimation system. 101...Control unit 102...Storage section 103...input section 104...Output section 105…Communications Department 200…ship 201...Control unit 202...Storage section 203...Operation unit 204...Drive unit 205...Output section 206...Sensor section 207...Antenna section 208...Camera

Claims

1. A parameter estimation system for estimating parameters of a nonlinear motion model of a ship, comprising: an acquisition unit that acquires an added mass and an added moment of inertia of the vessel; an extraction unit that identifies a motion pattern during navigation from among a plurality of different types of motion patterns based on a navigation log when the ship navigates, and extracts from the navigation log the values ​​of a parameter set in which parameter items are defined corresponding to the type of the identified motion pattern; a first estimation unit that estimates first motion model parameters of the ship in the first motion pattern by using a first nonlinear optimization problem that is set using as input the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set corresponding to the first motion pattern extracted by the extraction unit; and a second estimation unit that estimates second motion model parameters of the ship in the second motion pattern by using a second nonlinear optimization problem that is set using as input a second parameter set corresponding to a second motion pattern extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; and and A parameter estimation system, wherein the items of parameters constituting the first parameter set are different from the items of parameters constituting the second parameter set.

2. a third estimator that estimates a third motion model parameter of the ship in the third motion pattern by using a third nonlinear optimization problem that is set using as input a third parameter set corresponding to a third motion pattern extracted by the extractor, the first motion model parameter estimated by the first estimator, and the second motion model parameter estimated by the second estimator; The parameter estimation system according to claim 1 , wherein the items of the parameters constituting the third parameter set are different from the items of the parameters constituting the first parameter set and the second parameter set.

3. the first estimation unit estimates the first motion model parameters related to a first nonlinear motion model having a nonlinear term by solving an optimization problem corresponding to the first motion pattern; 2. The parameter estimation system according to claim 1, wherein the second estimation unit estimates the second motion model parameters for a second nonlinear motion model having nonlinear terms by solving an optimization problem corresponding to the second motion pattern.

4. 3. The parameter estimation system according to claim 2, wherein the third estimation unit estimates the third motion model parameters for a third nonlinear motion model having nonlinear terms by solving an optimization problem corresponding to the third motion pattern.

5. The parameter estimation system according to claim 1 , wherein the acquisition unit acquires the added mass and added moment of inertia of the ship by performing an estimation process using a computational fluid analysis based on a three-dimensional model of the ship.

6. The parameter estimation system according to claim 1 , wherein the plurality of different types of motion patterns are any one of acceleration / deceleration, constant speed cruising, turning, stationary position keeping, and docking / leaving.

7. 2. The parameter estimation system according to claim 1, wherein the navigation log includes a propeller rotation speed and a rudder angle as input data to the vessel, and a speed and an acceleration as output data from the vessel.

8. A parameter estimation method for estimating parameters of a ship motion model, implemented by a computer, comprising: acquiring an added mass and an added moment of inertia of the vessel; an extraction step of identifying a motion pattern during navigation from among a plurality of different types of motion patterns based on a navigation log when the ship was sailing, and extracting from the navigation log the values ​​of a parameter set in which parameter items are defined corresponding to the type of the identified motion pattern; a first estimation step of estimating first motion model parameters of the ship in the first motion pattern by using a first nonlinear optimization problem that is set using as input the added mass and added moment of inertia acquired in the acquisition step and a first parameter set corresponding to the first motion pattern extracted in the extraction step; a second estimation step of estimating second motion model parameters of the vessel in the second motion pattern by using a second nonlinear optimization problem that is set using as input a second parameter set corresponding to the second motion pattern extracted in the extraction step and the first motion model parameters estimated in the first estimation step; and A parameter estimation method in which the items of parameters constituting the first parameter set are different from the items of parameters constituting the second parameter set.

9. Computer, an acquisition unit that acquires the added mass and added moment of inertia of the vessel; an extraction unit that identifies a motion pattern during navigation from among a plurality of different types of motion patterns based on a navigation log when the ship navigates, and extracts from the navigation log the values ​​of a parameter set in which parameter items are defined corresponding to the type of the identified motion pattern; a first estimation unit that estimates first motion model parameters of the ship in the first motion pattern by using a first nonlinear optimization problem that is set using as input the added mass and added moment of inertia acquired by the acquisition unit and a first parameter set corresponding to the first motion pattern extracted by the extraction unit; a second estimation unit that estimates second motion model parameters of the ship in the second motion pattern by using a second nonlinear optimization problem that is set using as input a second parameter set corresponding to the second motion pattern extracted by the extraction unit and the first motion model parameters estimated by the first estimation unit; It functions as A program in which the parameter items constituting the first parameter set are different from the parameter items constituting the second parameter set.

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