Method for optimizing control variables of autonomous ship, and apparatus therefor

Abstract

The present invention relates to a method for optimizing control variables that control a ship and, more particularly, to a method for optimizing control variables of a ship operated autonomously, and an apparatus for implementing the method.

Description

Method for optimizing control variables of an autonomous ship and its device

[0001] The present invention relates to a method for optimizing control variables for controlling a ship, and more specifically, to a method for optimizing control variables for a ship operated autonomously and a device for implementing the method.

[0002] In the process of installing an autonomous navigation system on a ship, a dynamic model is created through a test run to set the initial control variables. However, the process is complex and requires precise work, taking a long time, and there is a problem that it is difficult for non-experts to install the autonomous navigation system.

[0003] In addition, existing methods using limited dynamic models have technical limitations in that they cannot derive control variables that reflect various operating conditions and ship characteristics.

[0004] Figure 1 is a drawing for explaining a conventional method for optimizing the control variables of a ship.

[0005] Referring to Fig. 1, a ship at a first trial run position (AA) can perform a lateral movement to a second trial run position (AA') during the trial run, and then perform a trial run through the first trial run path (BB) to move to a third trial run position (CC). At this time, since the first trial run path (BB) is not a path designed to allow the ship to perform left turns, right turns, forward movements, and backward movements when moving along the first trial run path (BB), there is a problem that the ship must perform trial runs multiple times through trial run paths other than the first trial run path (BB), and therefore, there is a need to introduce a technology to solve this problem.

[0006] Furthermore, conventional methods suffer from the inconvenience of requiring vessels to return to their initial position after each sea trial. Furthermore, to minimize the effects of external disturbances, sea trials are conducted only during times or environments with weak currents or winds, resulting in excessive time and cost. Therefore, a methodology is needed that enables vessel sea trials even in the presence of external disturbances, thereby optimizing control gain.

[0007] The technical problem to be solved by the present invention is to provide a method for optimizing control variables of an autonomous ship and a device for implementing the method.

[0008] According to one embodiment of the present invention for solving the above technical problem, the method comprises the steps of: when a first sea trial process of a ship is completed based on a set value, obtaining first result information for the first sea trial process; generating at least one dynamic model based on the obtained first result information; estimating a first control variable of the ship based on the generated dynamic model; and when a second sea trial process of the ship is completed using the estimated first control variable, generating a second control variable that optimizes the first control variable based on second result information for the second sea trial process.

[0009] According to another embodiment of the present invention for solving the above technical problem, a device comprises: a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, wherein the processor, when a first sea trial process of a ship is completed based on a set value, obtains first result information for the first sea trial process, generates at least one dynamic model based on the obtained first result information, estimates a first control variable of the ship based on the generated dynamic model, and when a second sea trial process for the ship is completed using the estimated first control variable, generates a second control variable that optimizes the first control variable based on second result information for the second sea trial process.

[0010] A method for optimizing a dynamic model according to one embodiment of the present invention for solving the above technical problem includes the steps of receiving input for calibration of a dynamic model of a ship and determining the type of calibration of the received input; processing the calibration based on the determined type of calibration; and controlling a state change of a UI (user interface) output to a user terminal based on the processed calibration.

[0011] According to another embodiment of the present invention for solving the above technical problem, a dynamic model optimization device includes a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, wherein the processor receives an input for calibration of a dynamic model of a ship, determines the type of calibration of the received input, processes the calibration based on the determined type of calibration, and controls a state change of a UI (user interface) output to a user terminal based on the processed calibration.

[0012] One embodiment of the present invention can provide a computer-readable recording medium storing a program for executing the above method.

[0013] According to the present invention, an interface can be implemented that allows non-expert users to easily set ship control variables even without a deep understanding of the ship's dynamic model, thereby significantly improving user accessibility.

[0014] Furthermore, the present invention enables the determination of an optimal dynamic model for a vessel, thereby maximizing the efficiency and stability of the vessel model. This allows the user to universally set control gains regardless of vessel shape or type.

[0015] Furthermore, the present invention enables precise control of a vessel through the application of a customized model. This allows for more precise and efficient control tailored to the vessel's diverse operating conditions and characteristics, thereby enhancing vessel safety and providing economic benefits such as reduced fuel consumption.

[0016] The present invention enables commissioners to more easily perform calibration of a vessel, thereby reducing the time and cost involved in preparing the vessel before delivering it to a customer.

[0017] Figure 1 is a drawing for explaining a conventional method for optimizing the control variables of a ship.

[0018] Figure 2 is a drawing schematically showing the overall configuration of a system in which a method according to the present invention is implemented.

[0019] FIG. 3 is a block diagram showing another example of a control variable optimization device according to the present invention.

[0020] FIG. 4 is a drawing for explaining a sub-module included in the processor of FIG. 3.

[0021] Figure 5 is a drawing for explaining the disturbance of currents applied to a ship.

[0022] Figure 6 is a drawing for explaining an example of the first test drive process.

[0023] Figure 7 is a drawing illustrating another example of the first test drive process.

[0024] Figure 8 is a diagram exemplarily showing the first result information obtained through the first test drive process.

[0025] Figure 9 is a diagram for explaining the process of estimating the first control variable.

[0026] Figure 10 is a diagram for explaining the process by which a control variable optimization device estimates coefficients of a dynamic model.

[0027] Figure 11 is a schematic diagram showing a process in which a control variable optimization device optimizes a second control variable.

[0028] Figure 12 is a drawing showing an example of a test route used by a vessel during its second sea trial run.

[0029] Figures 13a to 13d are diagrams exemplarily showing a process of generating a second test route by integrating a plurality of first test routes.

[0030] Fig. 14 is a schematic flowchart showing the data processing process of the control variable optimization device described through Figs. 5 to 13d.

[0031] FIG. 15 is a drawing for explaining a dynamic model optimization device according to one embodiment of the present invention.

[0032] Figure 16 is a drawing for explaining the process of the first correction department.

[0033] Figure 17 is a flowchart illustrating a method by which the first correction unit performs correction using information collected during the driving process of Figure 16.

[0034] Figure 18 is a drawing for explaining the process of the second correction department.

[0035] Figure 19 is a flowchart illustrating a method by which the second correction unit performs correction using information collected during the zigzag driving process of Figure 18.

[0036] Figure 20 is a drawing for explaining the process of the third correction department.

[0037] Figure 21 is a flowchart illustrating a method by which the third correction department performs correction using information collected during the descent process of Figure 20.

[0038] FIG. 22 is a drawing illustrating a first embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0039] FIG. 23 is a drawing illustrating a second embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0040] FIG. 24 is a drawing illustrating a third embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0041] FIG. 25 is a drawing illustrating a fourth embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0042] FIG. 26 is a drawing illustrating a fifth embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0043] Figure 27 is a drawing for explaining the ship size information menu.

[0044] Figure 28 is a drawing for explaining the test drive information menu.

[0045] Figure 29 is a drawing for explaining the dynamic coefficient menu.

[0046] FIG. 30 and FIG. 31 are drawings showing the sixth and seventh embodiments of the interface screen output to the user terminal by the interface control unit of FIG. 15.

[0047] Figure 32 is a flowchart illustrating an example of a method performed by the dynamic model optimization device described in Figures 15 to 31.

[0048] According to one embodiment of the present invention for solving the above technical problem, the method comprises the steps of: when a first sea trial process of a ship is completed based on a set value, obtaining first result information for the first sea trial process; generating at least one dynamic model based on the obtained first result information; estimating a first control variable of the ship based on the generated dynamic model; and when a second sea trial process of the ship is completed using the estimated first control variable, generating a second control variable that optimizes the first control variable based on second result information for the second sea trial process.

[0049] In the above method, the step of generating the dynamic model may include selecting at least two dynamic models, verifying coefficients of each dynamic model, and then generating one dynamic model based on the verified coefficients.

[0050] In the above method, the dynamic model is a model corresponding to the equation of motion of the ship, and the equation of motion may be a formula capable of predicting the future movement of the ship by using at least one of the state of the ship, a control command, and a disturbance as an input variable.

[0051] In the above method, the step of estimating the first control variable of the ship can estimate a control gain calculated by combining the dynamic model and the control force of the controller as the first control variable.

[0052] In the above method, the setting value may be a value set based on a user's input.

[0053] In the above method, the second test run process may be a process performed through a second test route in which a starting point, a turning point, and an ending point are designed based on predetermined rules.

[0054] In the above method, the second test run process may be a process performed through a second test route designed in consideration of the disturbance of the ship.

[0055] In the above method, the external disturbance may be an environmental disturbance of the current near the sea where the ship operates.

[0056] In the above method, the second test drive process may be a process of repeating driving on the first test route included in the second test route a predetermined number of times.

[0057] In the above method, the second test drive process may be a process of repeating driving while changing directions along the first test route included in the second test route a predetermined number of times.

[0058] In the above method, the second test drive process may be a process of obtaining the second result information while controlling the vessel to run at a speed lower than a preset speed limit.

[0059] In the above method, the second test run process may be a process of setting the entire operating space of the ship based on an empty packing algorithm, including a plurality of mini test routes, and generating a second test route by organically combining the plurality of mini test routes to minimize the number of starting points and ending points of the plurality of mini test routes, and controlling the ship to run on the generated second test route while obtaining the second result information.

[0060] In the above method, the step of generating the second control variable may include performing the second test run process on a second test route in the shape of a figure of eight with the start point and the end point being the same, thereby generating the second result information, and generating the second control variable based on the second result information.

[0061] In the above method, the step of generating the second control variable may include performing the second test run process on a second test route in the shape of a four-leaf clover with the start point and end point being the same, thereby generating the second result information, and generating the second control variable based on the second result information.

[0062] According to another embodiment of the present invention for solving the above technical problem, a device comprises: a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, wherein the processor, when a first sea trial process of a ship is completed based on a set value, obtains first result information for the first sea trial process, generates at least one dynamic model based on the obtained first result information, estimates a first control variable of the ship based on the generated dynamic model, and when a second sea trial process for the ship is completed using the estimated first control variable, generates a second control variable that optimizes the first control variable based on second result information for the second sea trial process.

[0063] A method for optimizing a dynamic model according to one embodiment of the present invention for solving the above technical problem includes the steps of receiving input for calibration of a dynamic model of a ship and determining the type of calibration of the received input; processing the calibration based on the determined type of calibration; and controlling a state change of a UI (user interface) output to a user terminal based on the processed calibration.

[0064] In the above method, the type of the input may be at least one of speed correction, steering correction, and berthing correction of the vessel.

[0065] In the above method, the step of determining the type of correction may receive at least one of model information and size information of the ship before receiving input for the correction.

[0066] In the above method, the size information of the vessel can be automatically entered when the model information is entered.

[0067] In the above method, the step of controlling the state change can be controlled to output to the user terminal by activating a fine-tuning input unit for adjusting the completed correction value when correction for the type of correction input is completed.

[0068] In the above method, the step of controlling the state change can be controlled so that when the correction for the type of correction input is completed, the date on which the correction is completed is output on the initial screen.

[0069] In the above method, the step of processing the correction may include controlling a correction UI corresponding to the type of the correction to be output, and the correction UI may include an output section for the status of the overall movement (maneuver) and individual movements (submaneuver) of the ship in the test run process.

[0070] In the above method, the overall movement may be one of a speed test and a zigzag test of the vessel.

[0071] In the above method, the individual movement may be one of forward and reverse movement of the vessel along a specified path.

[0072] In the above method, the step of processing the correction may output a modal including menus for outputting at least one of the overall movement, individual movement, size information, trial operation information, and dynamic coefficient of the ship when a predetermined input is received.

[0073] In the above method, the size information output to the user terminal as input to the menu may be information input before starting the correction of the ship.

[0074] In the above method, the test run information output to the user terminal as an input for the menu may be first result information obtained through a test run on the initial path of the ship.

[0075] In the above method, the dynamic coefficient output to the user terminal as an input for the menu may be at least one of a coefficient currently applied to the ship, an initial coefficient, and an optimized coefficient.

[0076] According to another embodiment of the present invention for solving the above technical problem, a dynamic model optimization device includes a memory in which at least one program is stored; and a processor that performs a calculation by executing the at least one program, wherein the processor receives an input for calibration of a dynamic model of a ship, determines the type of calibration of the received input, processes the calibration based on the determined type of calibration, and controls a state change of a UI (user interface) output to a user terminal based on the processed calibration.

[0077] One embodiment of the present invention can provide a computer-readable recording medium storing a program for executing the above method.

[0078] The present invention is capable of various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, as well as the methods for achieving them, will become clearer with reference to the embodiments described in detail below, along with the drawings. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various forms.

[0079] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same drawing reference numerals, and redundant descriptions thereof will be omitted.

[0080] In the following examples, the terms first, second, etc. are not used in a limiting sense, but are used for the purpose of distinguishing one component from another.

[0081] In the examples below, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0082] In the following examples, terms such as “include” or “have” mean that a feature or component described in the specification is present, and do not preclude the possibility that one or more other features or components may be added.

[0083] In some embodiments, where the implementation is otherwise feasible, a particular process sequence may be performed in a different order than described. For example, two processes described in succession may be performed substantially simultaneously, or in a reverse order from the described order.

[0084] Figure 2 is a drawing schematically showing the overall configuration of a system in which a method according to the present invention is implemented.

[0085] Referring to FIG. 2, it can be seen that a system (1) according to one embodiment of the present invention includes a ship (10) equipped with a control variable optimization device (11), a server (20), and a user terminal (50). Here, the control variable optimization device (11), the server (20), and the user terminal (50) can be electrically connected via a communication network (30).

[0086] A user may directly board the vessel (10) and operate the vessel (10), or may control the operation of the vessel (10) through an external device without boarding the vessel (10). That is, in the present invention, the user is a person related to the vessel (10), and may be nicknamed a driver, depending on the embodiment. In particular, in the present invention, the user may be an owner who has purchased and owns the vessel (10), or, depending on the embodiment, may be a shared user who has been granted sharing rights to the vessel (10) from the owner of the vessel (10) and can use the vessel (10) for a predetermined period of time.

[0087] The control variable optimization device (11) is a type of telematics terminal and can be mounted or installed on a ship (10). The control variable optimization device (11) may include a processor (13) that performs various data processing or calculations, a memory (14) that stores various data used by the processor (13), and a communication module (12) for communication with external devices such as a server (20).

[0088] Memory (14) is hardware that stores various data processed by the control variable optimization device (11) and the processor (13), and can store a program for processing and controlling the processor (13). Memory (14) may include random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM, Blu-ray or other optical disk storage, hard disk drive (HDD), solid state drive (SSD), or flash memory.

[0089] The communication module (12) may include one or more components that enable wired / wireless communication with external devices. For example, the communication module (12) may include at least one piece of hardware necessary to implement short-range communication, such as Wi-Fi or Bluetooth, in a network provided by the communication network (30), or to implement various communications, including the Internet, when a LAN cable is connected.

[0090] The processor (13) can control the overall operation of the control variable optimization device (11). For example, the processor (13) can control the operation of the input unit (not shown), display (not shown), memory (14), communication module (12), etc. included in the control variable optimization device (11) by executing programs stored in the memory (14).

[0091] When the control variable optimization device (11) is implemented as a physical device, the processor (13) may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0092] In addition, when the control variable optimization device (11) of the present invention is implemented in the form of an application (program) that runs on a data processing device, the processor (13) and memory (14) included in the control variable optimization device (11) may be implemented in the form of a virtual machine that implements hardware such as DSPs, microcontrollers, RAM, ROM, HDD, etc. as software (command script).

[0093] The server (20) can support telematics services for a ship (10) equipped with a control variable optimization device (11). To this end, the server (20) includes a communication module (21) and can be electrically connected to the ship (10) (e.g., the control variable optimization device (11) of the ship (10)) through the communication module (21).

[0094] In addition, the server (20) includes a memory (23) that stores a program (25) that supports a telematics service. The processor (22) of the server (20) can execute the program (25) stored in the memory (23) to perform data processing or calculations for the telematics service.

[0095] The processor (22) can load commands or data into the memory (23) (e.g., volatile memory), process the stored commands or data, and store the resulting data in the memory (23) (e.g., non-volatile memory). As an example, the server (20) may be a cloud server, but is not limited thereto. Various information about the ship (10) may be stored in the memory (23) of the server (20). According to one embodiment, information about the control variable optimization device (11) may be the serial number of the telematics terminal mounted on the ship (10). In addition, information about a driver who is on board the ship (10) and drives the ship (10) may be stored in the memory (23) of the server (20).

[0096] The communication network (30) performs the function of connecting the ship (10), the server (20), and the user terminal (50), which are components of the overall system (1), and may include various wired and wireless communication networks such as a data network, a mobile communication network, and the Internet. In particular, in the present invention, the communication network (30) includes not only the currently used mobile communication network, but also the old-generation mobile communication network that has already been used and then abandoned, and the next-generation mobile communication network whose infrastructure is scheduled to be built and used in the future, and thus may be one of GSM (Global System for Mobile communications), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), CDMA 2000, LTE (Long Term Evolution), LTE-A (Long Term Evolution Advanced), 5G (5-Generation), and the 6G mobile communication network scheduled for service in 2030. In addition, the communication network (30) may also include a network implemented through satellite communication, such as STARLINK.

[0097] The user terminal (50) is an electronic device of a purchaser who has purchased a vessel (10) equipped with a control variable optimization device (11). Examples of such devices include, but are not limited to, a desktop PC, tablet PC, laptop, or smartphone. In addition, the user terminal (50) can store programs (e.g., applications) for implementing various telematics services.

[0098] In addition, as an optional embodiment of the present invention, when the control variable optimization device (11) is implemented in the form of an application, the control variable optimization device (11) can be logically included in the user terminal (50), unlike that illustrated in FIG. 2. That is, the application required to implement the method according to the present invention can be installed in the user terminal (50).

[0099] The present invention is not limited to the individual components illustrated in FIG. 2. For example, the system (1) may further include other components in addition to the control variable optimization device (11), the server (20), and the user terminal (50). In addition, other components may be added to each of the ship (10), the control variable optimization device (11), the server (20), and the user terminal (50), or some components may be omitted.

[0100] FIG. 3 is a block diagram showing another example of a control variable optimization device according to the present invention.

[0101] The control variable optimization device (11) described in the system (1) of FIG. 2 is limited to an embodiment in which the control variable optimization device (11) is implemented in a form that is externally mounted on the ship (10) or in a form that is installed adjacent to the main controller of the ship (10), and FIG. 3 is a block diagram for exemplarily explaining a general-purpose control variable optimization device (300) that can be physically or logically included in various other devices including the ship (10). That is, the control variable optimization device (300) described in FIG. 3 may be physically mounted on the ship (10), logically included in the ship (10) in the form of a program in the memory of the ship (10), or may be implemented in a form that is physically or logically included in a user terminal (50) or an external device that can communicate with the ship (10) through a communication network (30).

[0102] Referring to FIG. 3, it can be seen that the control variable optimization device (300) includes a communication unit (310), a processor (330), and a memory (350). The communication unit (310) may include one or more components that enable wired / wireless communication with an external device. For example, the communication unit (310) may include at least one piece of hardware necessary to implement short-range communication, such as Wifi or Bluetooth, in a network provided by a communication network, or to implement various communications, including the Internet, when a LAN cable is connected. In addition, the communication unit (310) may also include a communication module that supports VHF (Very High Frequency) communication used in communication between ships.

[0103] The memory (350) is hardware that stores various data processed within the control variable optimization device (300), and can store a program for processing and controlling the processor (330). The memory (350) may include a random access memory (RAM) such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a CD-ROM, a Blu-ray or other optical disk storage, a hard disk drive (HDD), a solid state drive (SSD), or a flash memory.

[0104] The processor (330) can control the overall operation of the control variable optimization device (300). For example, the processor (330) can control the operation of the input unit (not shown), display (not shown), communication unit (310), memory (350), etc. included in the control variable optimization device (300) by executing programs stored in the memory (350).

[0105] As an example, when the first sea trial process of the ship is completed based on the set value, the processor (330) may obtain first result information for the first sea trial process, generate at least one dynamic model based on the obtained first result information, estimate a first control variable of the ship based on the generated dynamic model, and when the second sea trial process for the ship is completed using the estimated first control variable, generate a second control variable that optimizes the first control variable based on the second result information for the second sea trial process. The process by which the processor (330) generates the second control variable will be described later with reference to FIGS. 4 to 14.

[0106] When the control variable optimization device (300) is implemented as a physical device, the processor (330) may be implemented using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0107] In addition, when the control variable optimization device (300) of the present invention is implemented in the form of an application (program) that runs on an integrated data processing device, the processor (330) and memory (350) included in the control variable optimization device (300) may be implemented in the form of a virtual machine that implements hardware such as DSPs, microcontrollers, RAM, ROM, HDD, etc. as software (command script).

[0108] Below, the process of the control variable optimization device (300) will be described by example.

[0109] In one embodiment, the control variable optimization device (300) may obtain first result information for the first sea trial process when the first sea trial process of the vessel is completed based on the set value, generate at least one dynamic model based on the obtained first result information, estimate the first control variable of the vessel based on the generated dynamic model, and generate a second control variable that optimizes the first control variable based on the second result information for the second sea trial process when the second sea trial process for the vessel is completed using the estimated first control variable.

[0110] In one embodiment, the control variable optimization device (300) can select at least two dynamic models, verify coefficients of each dynamic model, and then generate one dynamic model based on the verified coefficients.

[0111] In one embodiment, the dynamic model of the control variable optimization device (300) is a model corresponding to the equation of motion of the ship, and the equation of motion may be a formula that can predict the future movement of the ship by using at least one of the state of the ship, a control command, and a disturbance as an input variable.

[0112] In one embodiment, the control variable optimization device (300) can estimate a control gain calculated by combining a dynamic model and a control force of a controller as a first control variable.

[0113] In one embodiment, the set value in the control variable optimization device (300) may be a value set based on a user's input.

[0114] In one embodiment, the second test run process of the control variable optimization device (300) may be a process performed through a second test route in which a starting point, a turning point, and an ending point are designed based on predetermined rules.

[0115] In one embodiment, the second test run process of the control variable optimization device (300) may be a process performed through a second test route designed in consideration of the disturbance of the ship.

[0116] In one embodiment, the disturbance in the control variable optimization device (300) may be an environmental disturbance of the current near the sea where the ship operates.

[0117] In one embodiment, the second test drive process of the control variable optimization device (300) may be a process of repeating driving the first test route included in the second test route a predetermined number of times.

[0118] In one embodiment, the second test drive process of the control variable optimization device (300) may be a process of repeating driving a first test route included in a second test route while changing directions a predetermined number of times.

[0119] In one embodiment, the second test run process of the control variable optimization device (300) may be a process of obtaining second result information while controlling the ship to run at a speed lower than a preset speed limit.

[0120] In one embodiment, the second test run process of the control variable optimization device (300) may be a process of setting the entire operating space of the ship based on a bin packing algorithm, including a plurality of mini test routes, but generating a second test route by organically combining the plurality of mini test routes to minimize the number of starting points and ending points of the plurality of mini test routes, and controlling the ship to run on the generated second test route while obtaining second result information.

[0121] In one embodiment, the control variable optimization device (300) may perform the second test run process on a second test route in the shape of a figure of eight with the same starting point and ending point, thereby generating the second result information, and may generate the second control variable based on the second result information.

[0122] In one embodiment, the control variable optimization device (300) may perform the second test run process on a second test route in the shape of a four-leaf clover with the same start point and end point, thereby generating the second result information, and may generate the second control variable based on the second result information.

[0123] The concepts and processes of each word used in the above-described embodiments will be described later with reference to FIGS. 4 to 22.

[0124] FIG. 4 is a drawing for explaining a sub-module included in the processor of FIG. 3.

[0125] Referring to FIG. 4, it can be seen that the processor (330) includes a test run execution unit (331), an information acquisition unit (332), a dynamic coefficient estimation unit (333), an initial control gain estimation unit (334), an initial control test execution unit (335), and a control gain optimization processing unit (336). The test run execution unit (331), the information acquisition unit (332), the dynamic coefficient estimation unit (333), the initial control gain estimation unit (334), the initial control test execution unit (335), and the control gain optimization processing unit (336) illustrated in FIG. 4 are modules that are logically and conceptually separated in order to explain the process performed by the processor (330) in the process of implementing the method according to the present invention, and therefore, although six sub-modules are illustrated in FIG. 4, the processor (330) may include fewer than six or more than six sub-modules depending on the embodiment.

[0126] In addition, the test run execution unit (331), information acquisition unit (332), dynamic coefficient estimation unit (333), initial control gain estimation unit (334), initial control test execution unit (335), and control gain optimization processing unit (336) of FIG. 4 are sub-modules of the processor (330), and thus, like the processor (330), they can be implemented using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0127] The test run execution unit (331) can determine information about previously known test run procedures and each test run procedure. When the test run of a ship is initiated, the test run execution unit (331) can process a test run judgment process that can identify which procedure (or which procedure) the test run procedure currently in progress is among multiple procedures, and a visual output process that controls the processing of the test run procedure currently in progress or the user's control commands applied to the ship into visualized information and outputs it through the display of the ship (10) or the user terminal (50). The visual output process processed by the test run execution unit (331) will be described later with reference to FIGS. 23 to 32.

[0128] The information acquisition unit (332) can acquire the results of the vessel's trial run in bulk. For example, the control variable optimization device (300) can acquire first result information and second result information as results of the first trial run process and the second trial run process, and the first result information and the second result information can be stored in the information acquisition unit (332). In addition, the information acquisition unit (332) can collect data from various sensors (speed sensors, distance sensors, cameras, etc.) installed (attached) to the vessel, classify the data by trial run procedure, and store the collected data.

[0129] The dynamic coefficient estimation unit (333) can estimate the dynamic coefficients required to construct a dynamic model that best suits the vessel based on the test run data stored in the information acquisition unit (332) and information on multiple previously known dynamic models. The dynamic coefficient estimation unit (333) determines and evaluates the validity of the estimated coefficients, thereby enabling the control variable optimization device (300) to determine one of the multiple dynamic models that have been stored in advance. Additional embodiments of the dynamic coefficient estimation unit (333) will be described later with reference to FIG. 10.

[0130] The initial control gain estimation unit (334) can estimate the initial control gain through a dynamic model constructed based on the dynamic coefficients estimated by the dynamic coefficient estimation unit (333). In FIGS. 2 and 3, the initial control gain estimated by the initial control gain estimation unit (334) is referred to as the first control variable. For convenience of explanation, the initial control gain is hereinafter considered to be identical to the first control variable.

[0131] The initial control test execution unit (335) can send a control command to the ship to perform an initial control test after the initial control gain (first control variable) estimated by the initial control gain estimation unit (334) is reflected in the ship's controller (main controller). Here, the initial control test may be the second trial run process described in FIGS. 2 and 3.

[0132] The control gain optimization processing unit (336) can identify the deviation between the dynamic model and the actual ship's control period, and optimize the initial control gain estimated by the initial control gain estimation unit (334) based on the identified information. Here, the deviation between the dynamic model and the actual ship's control period is referred to as the second result information in FIGS. 2 and 3 , and the optimized initial control gain is referred to as the second control variable in FIGS. 2 and 3 . For convenience of explanation, the optimized initial control gain is hereinafter considered to be identical to the second control variable.

[0133] The second control variable output by the control gain optimization processing unit (336) is transmitted to the main controller of the ship, and can function as basic data (raw data) used by the main controller of the ship to implement autonomous operation of the ship.

[0134] In this embodiment, the first test run process is a process for obtaining first result information by making the ship sail through a predetermined initial path, and the second test run process is a process for making the ship sail through a predetermined test path once again in order to optimize the first control variable.

[0135] In addition, the first result information is basic information for generating a dynamics model, the first control variable is information output from the dynamics model and is a variable used to control the ship for autonomous navigation of the ship, and the second control variable means the result of the control variable optimization device (300) optimizing the first control variable. In the present invention, the second control variable is information optimized for the ship as information necessary for stable autonomous navigation of the ship, and therefore, a ship to which the second control variable is applied can precisely realize autonomous navigation.

[0136] In the present invention, at least one dynamic model is included in the memory (350) of the control variable optimization device (300). The dynamic model is a model that complies with the ship's dynamic equations of motion and various laws of physics, and can predict the future movement of the ship by receiving, as input, ship status information (sensor information), ship control commands, and external forces applied to the ship.

[0137] The control variable optimization device (300) described in FIGS. 2 to 4 has the advantage of being able to optimize the control gain applicable to the ship's controller by performing a test run even in the presence of disturbances, unlike existing test run processes. However, in order to minimize the influence of disturbances, it is necessary to maintain the ship's speed below a certain speed, and it is necessary to perform the test run through a test route having morphological characteristics. In particular, the control variable optimization device (300) according to the present invention determines only the current as the largest factor among disturbances in order to perform fast calculations, and does not consider disturbances caused by wind or waves.

[0138] Figure 5 is a drawing for explaining the disturbance of currents applied to a ship.

[0139] In the drawing shown in Fig. 5, the speed components and various parameters of the ship when the speed of the tidal current is applied as a disturbance to the ship are expressed as mathematical formulas as in mathematical formulas 1 to 5.

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] In mathematical expressions 1 to 4, r is the yaw angular velocity, ψ cr refers to the angle of the ship based on the heading angle of the current, u and v refer to the velocity components of the ship about the x-axis and y-axis, respectively, and u r , v r The heading angle of the tide is ψ cr It refers to the velocity components of the ship about the x-axis and y-axis, respectively. , is the heading angle of the bird ψ crIt means the acceleration components of the ship's x-axis and y-axis based on the standard, and in mathematical expression 5, U c is the velocity of the current, U r Each represents the speed of the ship. As illustrated in Fig. 5, environmental disturbances such as tidal currents and the speed / acceleration of the ship have a mathematical correlation that can be expressed as a formula, and this can be similarly applied even when the type of environmental disturbance is not tidal currents but wind or waves. However, in the present invention, the sea trial of the ship can be performed continuously and quickly within a preset time limit so as not to consider the influence of wind and waves.

[0146] Figure 6 is a drawing for explaining an example of the first test drive process.

[0147] The control variable optimization device (300) according to the present invention can perform a first trial run process based on set values ​​and obtain first result information for the first trial run process. Hereinafter, the path along which a vessel travels through the first trial run process will be abbreviated as an initial path. Although multiple vessels are shown in the initial path of Fig. 6, this is only a schematic representation of a single vessel sequentially performing the first trial run, and does not mean that multiple vessels perform trial runs simultaneously on a single initial path.

[0148] Here, the setting value refers to various control information required to perform the first test drive process, and may be information stored in the test drive execution unit (331) of Fig. 4. Depending on the embodiment, the setting value may also be information entered by a user.

[0149] In Fig. 6, the vessel can perform a first trial run according to the starting route defined in the set values. The control variable optimization device (300) can control the vessel to complete the first trial run while following the appropriate procedures on the starting route by referring to the set values.

[0150] First, the ship stopped at the anchorage point (6) moves to the starting point (6'), and the control variable optimization device (300) detects that the ship has moved to the starting point (6') of the starting route and controls the ship to navigate based on the set value. At this time, the ship's controller (main controller) inputs a throttle control command (Throttle CMD) as a predetermined constant value to control the ship to navigate in a straight line.

[0151] Next, the ship can be controlled by a steering control command (Steering CMD) having a predetermined change pattern while the throttle control command (Throttle CMD) is maintained at a predetermined constant value. Referring to Fig. 6, it can be seen that after the ship has driven straight for a certain period of time, the value of the steering control command, which was maintained at 0, is repeatedly input as a positive / negative value, and thus the ship repeatedly turns left / right along the starting path. The control variable optimization device (300) can terminate the first trial run process and obtain the first result information after the ship reaches the end point (6'') of the starting path stored in the set value.

[0152] Figure 7 is a drawing illustrating another example of the first test drive process.

[0153] The starting path illustrated in Fig. 7 may exist in a narrower area than the starting path illustrated in Fig. 6. In the starting path illustrated in Fig. 6, the vessel only temporarily turns left / right, without completely changing its forward direction, since the steering control command is input repeatedly in a cross-over manner with positive / negative values ​​while the throttle control command is maintained at a predetermined constant value. In other words, in Fig. 6, the vessel does not travel backwards from the initially determined straight ahead direction.

[0154] Meanwhile, the starting route illustrated in FIG. 7 consists of a total of three starting points and ending points. First, the vessel can move forward from the starting point S1 to the ending point E1. Next, the vessel can perform a turning operation from the starting point S2 to the ending point E2. Finally, the vessel can complete the first trial run process for the entire starting route by moving from the starting point S3 to the ending point E3. At this time, the information acquisition unit (332) of FIG. 4 can store measurement values ​​excluding the path for turning in the first result information, which is the result of the first trial run process. In other words, the measurement values ​​acquired during the turning operation from the starting point S2 to the ending point E2 can be removed.

[0155] As described through FIGS. 6 and 7, the vessel can perform the first test run in various ways according to the existing test run procedures stored in the test run execution unit (331).

[0156] Figure 8 is a diagram exemplarily showing the first result information obtained through the first test drive process.

[0157] As illustrated in FIG. 8, when the first sea trial process of the ship is completed, the control variable optimization device (300) can obtain first result information including at least one of the longitudinal velocity u, the lateral velocity v, the yaw rate r, the propeller revolutions per second rps, and the rudder angle del of the ship.

[0158] In Fig. 8, the longitudinal speed of the ship is the speed at which the ship moves forward or backward, the transverse speed of the ship is the speed indicating how fast the ship moves sideways, the transverse rotation speed of the ship is the speed indicating how fast the ship rotates around the vertical axis, the propeller rotation speed per second is the number of times the ship's propeller rotates in one second, and the rudder angle of the ship means the value measured in degrees at which the ship's rudder (rudder angle) rotates from the centerline.

[0159] The control variable optimization device (300) according to the present invention, when the first result information is obtained as a result of the first test run process as shown in FIG. 8, estimates the dynamic model coefficients based on the first result information, determines the dynamic model with the estimated dynamic model coefficients, and estimates the first control variable of the ship based on the determined dynamic model. As described above, the first control variable refers to the initial control gain applied to the controller (main controller) of the ship, and the graph characteristics of the control variable will be described in FIG. 9.

[0160] Figure 9 is a diagram for explaining the process of estimating the first control variable.

[0161] In FIG. 4, the initial control gain estimation unit (334) included in the processor (330) of the control variable optimization device (300) can estimate the first control variable (initial control gain) based on the dynamic model after the first test run process is completed. Referring to FIG. 9, the initial control gain estimation unit (334) can obtain PD control force (Proportional and Derivative Control force) from the result value of the dynamic model after the dynamic model is optimized, and estimate the first control variable based on the obtained PD control force. As illustrated in FIG. 9, the PD control force acts as a force to resist disturbances (particularly, currents) without deviating from the path while the ship moves along the initial path, and is calculated based on proportional control (error size) and derivative control (error change rate), and therefore, can be combined with the first result information described in FIG. 8 to become basic data for estimating the first control variable.

[0162] Figure 10 is a diagram for explaining the process by which a control variable optimization device estimates coefficients of a dynamic model.

[0163] FIG. 10 illustrates a sub-module of the dynamic coefficient estimation unit (333) described in FIG. 4. The dynamic coefficient estimation unit (333) estimates and verifies coefficients for at least two dynamic models, and then synthesizes the verification results to ultimately create one dynamic model.

[0164] Referring to FIG. 10, it can be seen that the dynamic coefficient estimation unit (333) includes a first dynamic coefficient estimation unit (3331), a second dynamic coefficient estimation unit (3333), a first dynamic coefficient verification unit (3335), a second dynamic coefficient verification unit (3337), and a dynamic model judgment unit (3339). The first dynamic coefficient estimation unit (3331), the second dynamic coefficient estimation unit (3333), the first dynamic coefficient verification unit (3335), the second dynamic coefficient verification unit (3337), and the dynamic model judgment unit (3339) illustrated in FIG. 3 are modules logically and conceptually separated to explain the process performed by the dynamic coefficient estimation unit (333) in the process of implementing the method according to the present invention, and therefore, although five sub-modules are illustrated in FIG. 10, the number of sub-modules may be less than five or more than five, depending on the embodiment. In addition, the first dynamic coefficient estimation unit (3331), the second dynamic coefficient estimation unit (3333), the first dynamic coefficient verification unit (3335), the second dynamic coefficient verification unit (3337), and the dynamic model judgment unit (3339) of FIG. 10 are sub-modules of the dynamic coefficient estimation unit (333), and thus, like the dynamic coefficient estimation unit (333), they can be implemented using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0165] When the first sea trial process of a ship is completed and the first result information is obtained, the first dynamic coefficient estimation unit (3331) can estimate the first dynamic coefficient for the first dynamic model among the plurality of dynamic models that matches the first result information. Similarly, when the first sea trial process of a ship is completed and the first result information is obtained, the second dynamic coefficient estimation unit (3333) can estimate the second dynamic coefficient for the second dynamic model among the plurality of dynamic models that matches the first result information.

[0166] The first dynamic coefficient verification unit (3335) can verify the estimated first dynamic coefficient, and the second dynamic coefficient verification unit (3337) can verify the estimated second dynamic coefficient.

[0167] The dynamic model judgment unit (3339) can compare and analyze the verification results of the first dynamic coefficient verification unit (3335) and the second dynamic coefficient verification unit (3337), determine one dynamic coefficient that best corresponds to the first result information, and determine a dynamic model based on the determined coefficient. The dynamic model determined by the dynamic model judgment unit (3339) can output the first control variable as an output value using the determined dynamic coefficient as described above.

[0168] Figure 11 is a schematic diagram showing a process in which a control variable optimization device optimizes a second control variable.

[0169] Hereinafter, the description will be made with reference to FIG. 4 and FIG. 9 simultaneously.

[0170] In the present invention, the control variable optimization device (300) estimates a first control variable based on the coefficients of a dynamic model, and then optimizes the first control variable using the second result information obtained after the ship is allowed to sail a test route (second sea trial) based on the estimated first control variable, thereby generating a second control variable.

[0171] Figure 11 illustrates a process in which a control variable optimization device (300) optimizes a first control variable into a second control variable using second result information through a total of four individual processes.

[0172] The first reference drawing (1110) is a mini-map showing a vessel anchored at a dock moving northward and disengaging from the dock. The first reference drawing (1110) displays information on the distance traveled northward by the vessel during the process of disengaging from the dock and information on the steering command (steering command modulation) received while moving northward (turning angle information). This information may be included in the second result information.

[0173] The second reference drawing (1130) is a diagram schematically showing the relationship of the PD control coefficients used in the process of generating the second control variable from the first control variable of the ship. More specifically, the second reference drawing (1130) is a three-dimensional diagram, in which the x-axis, y-axis, and z-axis each represent the proportional control coefficient K. P , differential control coefficient K D , corresponds to a value obtained by subtracting the overshoot value from the steady state error. It has already been explained that the control variable optimization device (300) uses the PD control force in the process of estimating the first control variable in FIG. 9. In the second reference drawing (1130), the proportional control coefficient K P , differential control coefficient K D Is It may be a coefficient for PD control force. In addition, the steady-state error is an error that remains even though the control amount reaches a certain range of the target amount and does not disappear, and the overshoot means the highest value recorded in the PD control process of the ship's controller, respectively. The steady-state error and overshoot can be obtained in the PD control process of the dynamic model while estimating the first control variable as shown in FIG. 9. The second reference drawing (1130) schematically shows that the first control variable is optimized for the second control variable through the second result information when Kp is 0.8, KD is 0.16, and Z is 8.25968 degrees.

[0174] The third reference drawing (1150) shows the proportional control coefficient K for the overshoot (OS) in the PD control process in Fig. 9. P This is a drawing that shows the results of partial differentiation in a table. According to the third reference drawing (1150), the control variable optimization device (300) controls the overshoot (OS) by proportional control coefficient K. P When the result of partial differentiation is 0.8, it is considered to meet the preset standard, and the overshoot and proportional control coefficient at that time can be used as the second result information.

[0175] The fourth reference drawing (1170) is a proportional control coefficient K for overshoot (OS) in the third reference drawing (1150). P The result of partial differentiation is defined as overshoot sensitivity (os sensitivity), and the overshoot sensitivity and proportional control coefficient K PThis is a diagram showing the correlation between the two in a graph. The overshoot sensitivity λ is a value that quantifies how sensitively the steering action of the ship responds, and is a coefficient that represents the balance between the motion response and stability in the ship's dynamic model. For example, if λ is small, the ship's agility and responsiveness are low, so the steering stability can be evaluated as high. In another example, if λ is large, the ship's agility and responsiveness are high, so the user can quickly steer the ship, but there is a high possibility that an excessive response (overshoot) in response to the ship's control input will occur. Referring to the fourth reference drawing (1170), K included in the first control variable P was 0.16, but it can be seen that it changed to 0.126 during the optimization process with the second control variable.

[0176] The control gain optimization processing unit (336) included in the processor (330) of the control variable optimization device (300) can optimize the first control variable into the second control variable using the process illustrated in FIG. 11.

[0177] Figure 12 is a drawing showing an example of a test route used by a vessel during its second sea trial run.

[0178] The left route (1210) of Fig. 12 refers to a figure-of-eight test route where the ship's starting and ending points are the same. When a ship performs a second test run along the left route (1210) of Fig. 12, the ship can perform forward, backward, left, and right turns at a certain rate during the process of completing the second test run, so the control variable optimization device (300) can sufficiently obtain the second result information necessary to generate the second control variable.

[0179] Likewise, the right route (1230) of Fig. 12 also illustrates a test route where the ship's starting point and ending point are the same. The right route (1230) of Fig. 12 is a four-leaf clover-shaped test route. When the ship performs a second trial run along the right route (1230) of Fig. 12, the ship can perform forward, backward, left, and right turns at a certain ratio during the process of completing the second trial run, so the control variable optimization device (300) can sufficiently obtain the second result information necessary to generate the second control variable.

[0180] According to one embodiment of the present invention, the test route may be distinguished and referred to as a first test route and a second test route. The first test route is a test route with a determined start point and an end point, and may be a small-scale test route included in the second test route. The second test route is a test route created by combining at least two or more first test routes, and the control variable optimization device (300) can sufficiently obtain second result information necessary to create the second control variable of the vessel by having the vessel perform a second sea trial process along the second test route.

[0181] For example, the left route (1210) of FIG. 12 is a second test route formed by combining one first test route in which the vessel turns right and returns from the starting point to the ending point, and one first test route in which the vessel turns left and returns from the starting point to the ending point. Similarly, the right route (1230) of FIG. 12 is a second test route formed by combining four first test routes in different directions.

[0182] Figures 13a to 13d are diagrams exemplarily showing a process of generating a second test route by integrating a plurality of first test routes.

[0183] Figure 13a illustrates three different first test routes.

[0184] The first test route (1330a) on the left side of Fig. 13a is included in the entire navigation space (1310) and has the characteristic that the ship's starting and ending points coincide. In the first test route (1330a) on the left side of Fig. 13a, the ship continues to maintain a left turn.

[0185] Next, the first test route (1350a) in the center of Fig. 13a is included in the entire operating space (1310) and is a route for sequentially driving straight, turning left, going straight, turning right, and going straight. The starting point and ending point of the first test route (1350a) in the center of Fig. 13a are different points.

[0186] The first test route (1370a) on the right side of Fig. 13a is included in the entire navigation space (1310) and is a route for sequentially driving a left turn followed by straight driving and a right turn followed by straight driving. The starting and ending points of the first test route (1370a) on the right side of Fig. 13a are different points, and the ending point of the vessel is located forward of the starting point based on the vessel's heading direction.

[0187] Fig. 13b illustrates simplified diagrams of the three first test routes described in Fig. 13a. More specifically, Fig. 13b illustrates the results of simplifying the three test routes of Fig. 13a into a minimum boundary, a starting point, and an ending point. As a result, the first test route (1330a) on the left side of Fig. 13a, the first test route (1350a) in the center of Fig. 13a, and the first test route (1370a) on the right side of Fig. 13a can be simplified into the first test route (1330b) on the left side of Fig. 13b, the first test route (1350b) in the center of Fig. 13b, and the first test route (1370b) on the right side of Fig. 13b, respectively. In particular, the first test route (1330b) on the left in Fig. 13b is depicted in the form of overlapping circles and squares with the start and end points being the same.

[0188] Fig. 13c is a diagram exemplarily illustrating a process of combining the three simplified first test routes illustrated in Fig. 13b. The control variable optimization device (300) can combine the three randomly arranged first test routes, as shown on the left side of Fig. 13c, in a direction that minimizes the number of start and end points, thereby generating a result, as shown on the right side of Fig. 13b. In this case, the control variable optimization device (300) can utilize a bin packing algorithm.

[0189] Figure 13d is a diagram showing the result of restoring the drawing on the right side of Figure 13c, which had been simplified. The vessel can perform the second sea trial process using the route illustrated in Figure 13d.

[0190] As described through FIGS. 13a to 13d, the control variable optimization device (300) sets the entire operating space (1310) of the ship based on an empty packing algorithm, and includes a plurality of mini test routes (first test routes), but in order to minimize the number of starting points and ending points of the plurality of mini test routes, the plurality of mini test routes can be organically combined to generate a second test route.

[0191] Fig. 14 is a schematic flowchart showing the data processing process of the control variable optimization device described through Figs. 5 to 13.

[0192] The control variable optimization device (300) sets a first test drive procedure and controls the ship to perform the first test drive process (S1410), and can obtain first result information about the first test drive process from various sensors installed on the ship (S1420).

[0193] The control variable optimization device (300) can generate a dynamic model based on the first result information and estimate / verify the dynamic model coefficients, as described in FIG. 10 (S1430).

[0194] In addition, the control variable optimization device (300) can estimate (generate) the first control variable, which is the initial control variable of the ship, based on the estimated dynamic model coefficients (S1440).

[0195] The control variable optimization device (300) controls the ship to perform a second test run process with the initial control variables estimated in step S1440 (S1450), and can generate a second control variable by optimizing the first control variable generated in step S1440 with the second result information acquired in the second test run process (S1460).

[0196] FIG. 15 is a drawing for explaining a dynamic model optimization device according to one embodiment of the present invention.

[0197] The dynamic model optimization device (1500) illustrated in FIG. 15 may be a device that performs a process of optimizing a dynamic model among the processes described in FIGS. 2 to 14. The dynamic model optimization device (1500) can estimate and verify the coefficients of the dynamic model through the first sea trial process of the ship in order to estimate the first control variable of the ship, and the process of the dynamic coefficient estimation unit (333) has been briefly described in FIGS. 4 and 10.

[0198] However, the process of creating a dynamic model and estimating the coefficients of the dynamic model according to the existing method is complex and requires elaborate work and takes a lot of time, so it is difficult for a non-expert to handle it alone. In addition, it was difficult to calculate dynamic coefficients that reflect various operating conditions and characteristics of the ship using only the existing dynamic model prepared in advance, but the dynamic model optimization device (1500) according to the present invention allows even a non-expert to easily perform the basic settings for estimating the coefficients of the dynamic model through an application configured with an intuitive UI.

[0199] The dynamic model optimization device (1500) illustrated in Fig. 15 may be implemented as a physical device or a logical device. As an example, the dynamic model optimization device (1500) may be implemented in the form of an application (program) for implementing visual changes in various information and UI output on the screen (1590b) of a user terminal (1590a) used by a user.

[0200] Referring to FIG. 15, it can be seen that the dynamic model optimization device (1500) according to the present invention includes an input judgment unit (1510), a first correction unit (1520), a second correction unit (1530), a third correction unit (1540), a fine adjustment unit (1550), and an interface control unit (1560). The input judgment unit (1510), the first correction unit (1520), the second correction unit (1530), the third correction unit (1540), the fine adjustment unit (1550), and the interface control unit (1560) illustrated in FIG. 15 are modules that are logically and conceptually separated in order to intuitively explain the process that the dynamic model optimization device (1500) performs in the process of implementing the method according to the present invention. Therefore, although six sub-modules are illustrated in FIG. 15, the number of sub-modules may be less than six or more than six, depending on the embodiment.

[0201] In addition, the input judgment unit (1510), the first correction unit (1520), the second correction unit (1530), the third correction unit (1540), the fine adjustment unit (1550), and the interface control unit (1560) of FIG. 15 may be physically implemented as a device capable of transmitting and receiving or processing information, or may be logically implemented as a script for processing the corresponding process. When the input judgment unit (1510), the first correction unit (1520), the second correction unit (1530), the third correction unit (1540), the fine adjustment unit (1550), and the interface control unit (1560) are physically implemented, they can be implemented using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions, similar to the processor (330) of FIG. 3.

[0202] The input judgment unit (1510) can receive an input, determine what kind of input the received input is, and transmit the input to at least one of the first correction unit (1520), the second correction unit (1530), and the third correction unit (1540). As an example, the input judgment unit (1510) can receive an input for correction of a ship's dynamic model, and determine the type of correction of the received input. As a result of the judgment, if the type of correction is the first correction, the input judgment unit (1510) can transmit the received input to the first correction unit (1520), and can process the remaining second and third corrections in the same manner.

[0203] The first calibration unit (1520) can receive input for the first calibration and process the calibration. The calibrated values ​​are transmitted to the interface control unit (1560), and the results of the calibration can be output through the display of the user terminal. Here, the first calibration refers to the calibration of the speed among the dynamic model coefficients (speed calibration).

[0204] The second calibration unit (1530) can receive input for the second calibration and process the calibration. The calibrated values ​​are transmitted to the interface control unit (1560), and the results of the calibration can be output through the display of the user terminal. Here, the second calibration refers to steering calibration among the dynamic model coefficients.

[0205] The third calibration unit (1540) can receive input for the third calibration and process the calibration. The calibrated values ​​are transmitted to the interface control unit (1560), and the results of the calibration can be output through the display of the user terminal. Here, the third calibration refers to the docking calibration among the dynamic model coefficients.

[0206] According to an embodiment, the first correction unit (1520) to the third correction unit (1540) may be integrated into a single correction unit module. The first correction unit (1520) to the third correction unit (1540) may process a process corresponding to the process of estimating the first control variable among the processes described in FIGS. 2 to 14 in terms of optimizing the dynamic model and estimating the result value.

[0207] The fine-tuning unit (1550) can fine-tune the processing results of the first correction unit (1520) to the third correction unit (1540). The fine-tuning unit (1550) can process a process corresponding to the process of generating the second control variable among the processes described in FIGS. 2 to 14, in that it fine-tunes and optimizes the first control variable.

[0208] The interface control unit (1560) can perform data processing to output the current screen and the next screen output from the user terminal in conjunction with the correction results of the first correction unit (1520) to the third correction unit (1540). As an example, the interface control unit (1560) can control the state change of the UI output to the user terminal based on the correction results of at least one of the first correction unit (1520) to the third correction unit (1540).

[0209] Figure 16 is a drawing for explaining the process of the first correction department.

[0210] Below, the explanation will be given with reference to Fig. 15.

[0211] As an example, the first correction unit (1520) may provide teaching to ensure that information required for the first correction is appropriately collected through the ship's display or user terminal in order to receive input for the first correction and process the correction. First, for the speed correction (first correction) of the ship, as illustrated in FIG. 16, a test run process must be performed in which the ship travels straight on Route 1, turns on Route 2, and then travels straight on Route 3 in the opposite direction to Route 1. During this process, the ship must sequentially pass the starting point and ending point (1610S, 1610E) of Route 1 and the starting point and ending point (1630S, 1630E) of Route 3.

[0212] Figure 17 is a flowchart illustrating a method by which the first correction unit performs correction using information collected during the driving process of Figure 16.

[0213] The first correction unit (1520) can determine the propulsion control value and ship speed relationship of the ship through the result of the ship traveling in a straight line in route 1 of Fig. 16 (S1710).

[0214] The first correction unit (1520) can determine whether the ship has turned its heading 180 degrees in route 2 of Figure 16 (S1720).

[0215] The first correction unit (1520) can obtain information for compensating for the influence of external disturbance during the ship's driving process through the result of the ship driving in a straight line on route 3 of FIG. 16, as in step S1710 (S1730).

[0216] Figure 18 is a drawing for explaining the process of the second correction department.

[0217] As an example, the second correction unit (1530) may receive input for the second correction and provide teaching to ensure that information required for the second correction is appropriately collected through the ship's display or user terminal to process the correction.

[0218] First, for the steering correction (second correction) of the ship, a trial run process must be performed in which the ship zigzags along Route 1, turns along Route 2, and then zigzags along Route 3 in the opposite direction to Route 1, as shown in Fig. 18. During this process, the ship must sequentially pass the starting and ending points of Route 1 and Route 3 in Fig. 18. In addition, for the steering correction of the ship, a trial run process must be performed in which the ship zigzags along Route 4 in Fig. 18, turns along Route 5, and then zigzags along Route 6 in the opposite direction to Route 4.

[0219] Figure 19 is a flowchart illustrating a method by which the second correction unit performs correction using information collected during the zigzag driving process of Figure 18.

[0220] The second correction department (1530) can determine the direction changing performance of the ship through the result of the ship running in a zigzag manner on route 1 of Fig. 18 (S1910).

[0221] The second correction unit (1530) can determine whether the vessel has turned 180 degrees in heading in route 2 of Figure 18 and collect that information (S1920).

[0222] The second correction unit (1530) can obtain information for compensating for the influence of external disturbance during the zigzag driving of the ship through the result of the ship zigzag driving on route 3 of FIG. 18, as in step S1910 (S1930).

[0223] The second correction unit (1530) can collect information necessary for steering correction by additionally repeating steps S1910 to S1930 once through paths 4 to 6 of FIG. 18 (S1940).

[0224] Figure 20 is a drawing for explaining the process of the third correction department.

[0225] As an example, the third correction unit (1540) may receive input for the third correction and provide teaching to ensure that information required for the third correction is appropriately collected through the ship's display or user terminal to process the correction.

[0226] First, for the ship's berthing correction (third correction), the ship must perform a trial run process that performs various linear motions and turning motions, as illustrated in Fig. 20. The linear motions may include low-speed linear motions (2010), backward motions (2020), lateral motions (2030), and turning motions (2040). The turning motions may include a total of four types of turning motions (2050, 2060, 2070, 2080). Through the information collected from the ship's linear motions, the third correction unit (1540) can obtain information on the relationship between the ship's speed and propulsion control values ​​in the low-speed region during the berthing process, as well as information on the relationship between the ship's angle and thrust, and determine the influence of disturbance compensation on these. Additionally, through information collected from the turning movement of the vessel, the third correction unit (1540) can determine the vessel's turning performance in the low-speed range during the berthing process.

[0227] Figure 21 is a flowchart illustrating a method by which the third correction department performs correction using information collected during the descent process of Figure 20.

[0228] The third correction department (1540) can determine the relationship between ship speed and propulsion control values ​​as a result of the ship's low-speed straight-line movement (S2110).

[0229] The 3rd Correction Department (1540) can determine whether the vessel has turned 180 degrees in heading and collect that information (S2120).

[0230] The third correction unit (1540) can obtain information to compensate for the influence of external disturbance during the ship's berthing process through the results of the ship berthing in the opposite direction as in step S2110 (S2130).

[0231] The third correction unit (1540) can collect information necessary for eyepiece correction by repeating steps S2110 to S2130 once more for another straight movement (S2140).

[0232] The third correction unit (1540) can perform a crawling motion toward the port and starboard directions of the vessel during the turning movement of the vessel, and obtain information to compensate for the resulting disturbance effects (S2150). The third correction unit (1540) can collect information on the results of performing a clockwise / counterclockwise turning operation during the turning movement of the vessel (S2160). If a turning operation in the port / starboard direction is performed using both forward and backward movements during the turning movement, the third correction unit (1540) can utilize the information as information for the third correction (S2170).

[0233] As described in FIGS. 16 to 21, the first correction unit (1520) to the third correction unit (1540) can generate a first control variable by estimating coefficients of a dynamic model suitable for the ship based on various information collected during the ship's test run, and the first control variable can be optimized by the method described in FIGS. 2 to 14 and processed into a second control variable.

[0234] FIG. 22 is a drawing illustrating a first embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0235] Below, the explanation will be given with reference to Fig. 15.

[0236] The input judgment unit (1510) may receive at least one of the model information and size information of the ship before receiving an input for correction. The user may input the model information of the ship in a setting screen such as FIG. 22 before optimizing the coefficients of the ship's dynamic model. The user may check the screen displayed on the user terminal and input the model selection input unit (2210) to select a model identical to the ship's model, or input the model direct input unit (2230) to directly input the model information of the ship.

[0237] FIG. 23 is a drawing illustrating a second embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0238] Figure 23 illustrates an interface screen for a user to input vessel size information. The entered vessel size information (2310) may include information such as vessel length, vessel width, vessel mass, and fuel tank size. After entering the vessel size information, the user may input the information into the storage input unit (2320) to store the information. The information stored by the storage input unit (2320) may be maintained until the new vessel size information is updated. The user may input the OK input unit (2330) to display the next screen.

[0239] As an example, vessel size information can be automatically entered when vessel model information is entered, as shown in FIG. 22. In this case, the size information corresponding to the vessel model information is pre-stored and then entered, preventing the user from having to manually enter vessel size information or from making input errors during the input process.

[0240] FIG. 24 is a drawing illustrating a third embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0241] Fig. 24 is an initial screen displayed on a user terminal after model information and size information of a ship are input. The user can perform a process of estimating and correcting dynamic model coefficients by inputting input to the calibration input unit (2410). The user can also fine-tune the first control variable of the ship that has already been determined by inputting input to the fine-tuning input unit (2430). Since the fine-tuning is effectively performed after the control variable for the ship is generated, depending on the embodiment, if only the model information and size information for the ship are input and no calibration is performed, the fine-tuning input unit (2430) may be deactivated. Although not illustrated in Fig. 24, the date on which each process (calibration, fine-tuning) is completed may be recorded in the calibration input unit (2410) and the fine-tuning input unit (2430).

[0242] FIG. 25 is a drawing illustrating a fourth embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0243] More specifically, Fig. 25 is a drawing exemplarily showing a calibration UI that is output in response to the type of calibration when the type of calibration (first calibration, second calibration, third calibration) is determined by the user's input. The calibration UI of Fig. 25 may include an output section for the status of the overall movement (maneuver) and individual movements (submaneuver) of the vessel in the trial operation process. As illustrated in Fig. 25, the overall movement of the vessel may be one of a speed test and a zigzag test, and the individual movement of the vessel may be one of the forward and reverse runs of the vessel along a designated path. In this case, the designated path may be the initial path described in Figs. 2 to 14. The user may check and change various setting values ​​for calibration in the calibration UI as shown in Fig. 25, and may change the type of calibration to a different type of calibration by inputting an input to the calibration type change input section (2510). Referring to Figure 25, it can be seen that the user has inputted an input to the correction type change input section (2510) to change the screen for the current speed correction (first correction) to an interface for eye correction (third correction) (2530).

[0244] FIG. 26 is a drawing illustrating a fifth embodiment of an interface screen output to a user terminal by the interface control unit of FIG. 15.

[0245] More specifically, Fig. 26 illustrates an example of an integrated modal (2600) called by inputting an input to the integrated modal input unit (2550) of Fig. 25. The integrated modal (2600) includes menus for outputting at least one of overall movement, individual movement, size information, test run information, and dynamic coefficients through a calibration UI.

[0246] The user can input the overall movement menu (2610) and individual movement menu (2620) of the integrated modal (2600) to call up a screen that allows the user to set the overall movement and individual movement during the ship's test run based on the user's input.

[0247] A user can input information into the size information menu (2630) of the integrated modal (2600) to call up a screen for ship size information. Here, the ship size information may be one of the ship model information and size information input in FIGS. 22 and 23.

[0248] Figure 27 is a drawing for explaining the ship size information menu.

[0249] The size information of the vessel shown in Fig. 27 can be changed and saved by the user.

[0250] Figure 28 is a drawing for explaining the test drive information menu.

[0251] The user can input the trial run information menu (2640) of the integrated modal (2600) to call up the information obtained through the trial run on the initial path of the vessel on the screen of the user terminal. Here, the information obtained through the trial run is the first result information described in FIGS. 2 to 14, and may include various information such as the longitudinal speed, transverse speed, transverse rotation speed, heading value, RPM P, RPM S, etc. of the vessel. The user can check the trial run information and, if necessary, modify some of the values.

[0252] Figure 29 is a drawing for explaining the dynamic coefficient menu.

[0253] A user can input the dynamic coefficient menu (2650) of the integrated modal (2600) of Fig. 26 to display the dynamic coefficient on the screen of the user terminal. Here, the user can check the coefficient currently applied to the vessel, the initial coefficient, and the optimized coefficient at a glance.

[0254] FIG. 30 and FIG. 31 are drawings showing the sixth and seventh embodiments of the interface screen output to the user terminal by the interface control unit of FIG. 15.

[0255] Figure 30 is an example of a screen output to a user terminal when optimization of dynamic coefficients is successfully completed using information about a ship input by a user and result information obtained during a ship test run process.

[0256] Figure 31 is an example of a screen displayed on a user terminal when optimization of dynamic coefficients fails due to insufficient information about the vessel entered by the user and some of the results obtained during the vessel's test run. A screen like Figure 31 may also be displayed in situations where external disturbances are excessively large due to inclement weather.

[0257] Figure 32 is a flowchart illustrating an example of a method performed by the dynamic model optimization device described in Figures 15 to 31.

[0258] The method according to Fig. 32 can be implemented by the dynamic model optimization device (1500) described in Fig. 15 and the sub-modules included in the dynamic model optimization device (1500), so it will be described below with reference to Figs. 15 to 31, and any description that overlaps with the contents already described will be omitted.

[0259] The dynamic model optimization device (1500) receives a correction input for the dynamic model (S3210) and can determine whether the type of the received correction is one of the first correction, the second correction, and the third correction (S3220).

[0260] The dynamic model optimization device (1500) can perform correction processing according to the type of correction determined through the first correction unit (1520), the second correction unit (1530), and the third correction unit (1540). As previously explained, for the correction processing to be performed normally, the user must input certain information about the vessel and the vessel's test run process must also be completed.

[0261] The dynamic model optimization device (1500) can control the output in a direction that changes the UI (user interface) of the user terminal according to the processing result (S3240). Step S3240 can be performed by the interface control unit (1560) included in the dynamic model optimization device (1500).

[0262] According to the present invention, an interface can be implemented that allows non-expert users to easily set ship control variables even without a deep understanding of the ship's dynamic model, thereby significantly improving user accessibility.

[0263] Furthermore, the present invention enables the determination of an optimal dynamic model for a vessel, thereby maximizing the efficiency and stability of the vessel model. This allows the user to universally set control gains regardless of vessel shape or type.

[0264] Furthermore, the present invention enables precise control of a vessel through the application of a customized model. This allows for more precise and efficient control tailored to the vessel's diverse operating conditions and characteristics, thereby enhancing vessel safety and providing economic benefits such as reduced fuel consumption.

[0265] The present invention enables commissioners to more easily perform calibration of a vessel, thereby reducing the time and cost involved in preparing the vessel before delivering it to a customer.

[0266] The embodiments of the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded on a computer-readable medium. At this time, the medium may include a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape, an optical recording medium such as a CD-ROM and a DVD, a magneto-optical medium such as a floptical disk, and a hardware device specifically configured to store and execute program instructions, such as a ROM, a RAM, a flash memory, etc.

[0267] Meanwhile, the computer program may be specifically designed and constructed for the present invention, or may be one known and available to those skilled in the computer software field. Examples of computer programs may include not only machine language code, such as that generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

[0268] The specific implementations described in the present invention are exemplary embodiments and do not limit the scope of the present invention in any way. For the sake of brevity, descriptions of conventional electronic components, control systems, software, and other functional aspects of the systems may be omitted. In addition, the lines connecting or connecting members between components illustrated in the drawings are merely representative of functional connections and / or physical or circuit connections, and may be replaced or represented as various additional functional connections, physical connections, or circuit connections in an actual device. In addition, unless specifically mentioned as “essential,” “important,” etc., a component may not be absolutely necessary for the application of the present invention.

[0269] The use of the term "above" and similar referential terms in the specification of the present invention (especially in the claims) may refer to both singular and plural. Furthermore, if a range is described in the present invention, it includes inventions that apply individual values ​​within the range (unless otherwise stated), and is equivalent to describing each individual value constituting the range in the detailed description of the invention. Finally, unless the order of the steps constituting the method according to the present invention is explicitly stated or otherwise stated to the contrary, the steps may be performed in any appropriate order. The present invention is not necessarily limited by the order in which the steps are described. The use of all examples or exemplary terms (e.g., "for example," etc.) in the present invention is merely intended to illustrate the present invention in detail, and the scope of the present invention is not limited by the examples or exemplary terms, unless otherwise defined by the claims. Furthermore, those skilled in the art will appreciate that various modifications, combinations, and variations can be made within the scope of the appended claims or their equivalents, depending on design conditions and factors.

Claims

1. When the first test run process of the vessel is completed based on the set value, a step of obtaining first result information for the first test run process; A step of generating at least one dynamic model based on the first result information obtained above; A step of estimating a first control variable of the ship based on the generated dynamic model; A method for optimizing control variables of an autonomous ship, comprising the step of generating a second control variable that optimizes the first control variable based on second result information for the second sea trial process when the second sea trial process for the ship is completed using the estimated first control variable.

2. In paragraph 1, The steps of generating the above dynamic model are: A method for optimizing control variables of an autonomous ship, which selects at least two dynamic models, verifies the coefficients of each dynamic model, and then creates a single dynamic model based on the verified coefficients.

3. In paragraph 1, The above dynamic model is, This is a model corresponding to the equation of motion of the above ship, The above equation of motion is, A method for optimizing control variables of an autonomous ship, which is a formula capable of predicting the future movement of a ship by using at least one of the state of the ship, a control command, and a disturbance as input variables.

4. In paragraph 1, The step of estimating the first control variable of the above vessel is: A method for optimizing control variables of an autonomous ship, wherein the control gain calculated by combining the above dynamic model and the control power of the controller is estimated as the first control variable.

5. In paragraph 1, The above settings are, A method for optimizing control variables of an autonomous ship, the values ​​of which are set based on user input.

6. In paragraph 1, The above second test drive process is, A method for optimizing control variables of an autonomous ship, the process being performed through a second test route designed based on predetermined rules, with a starting point, turning point, and end point.

7. In paragraph 1, The above second test drive process is, A method for optimizing control variables of an autonomous ship, the process being performed through a second test route designed in consideration of the disturbance of the above ship.

8. In paragraph 7, The above disturbance is, A method for optimizing control variables of an autonomous ship, which is an environmental disturbance for currents near the sea where the ship operates.

9. In paragraph 1, The above second test drive process is, A method for optimizing control variables of an autonomous ship, which is a process of repeating driving on a first test route included in a second test route a predetermined number of times.

10. In paragraph 1, The above second test drive process is, A method for optimizing control variables of an autonomous ship, which is a process of repeating a predetermined number of times the first test route included in the second test route while changing directions.

11. In paragraph 1, The above second test drive process is, A method for optimizing control variables of an autonomous ship, the method comprising: a process of obtaining the second result information while controlling the ship to run at a speed lower than a preset speed limit.

12. In paragraph 1, The above second test drive process is, Based on a bin packing algorithm, the entire operating space of the ship is set, and a plurality of mini-test routes are included, but in order to minimize the number of starting points and ending points of the plurality of mini-test routes, the plurality of mini-test routes are organically combined to create a second test route. A method for optimizing control variables of an autonomous ship, which is a process of obtaining the second result information while controlling the ship to run on the second test route generated above.

13. In paragraph 1, The step of generating the second control variable is: A method for optimizing control variables of an autonomous ship, wherein the second test run process is performed on a second test route in the form of a figure of eight with the same starting point and ending point, the second result information is generated, and the second control variable is generated based on the second result information.

14. In paragraph 1, The step of generating the second control variable is: A method for optimizing control variables of an autonomous ship, wherein the second test run process is performed on a second test route in the shape of a four-leaf clover with the same start and end points, the second result information is generated, and the second control variable is generated based on the second result information.

15. Memory in which at least one program is stored; and By executing at least one program, a processor is included that performs an operation, The above processor, When the first trial run process of the vessel is completed based on the set value, first result information for the first trial run process is obtained, At least one dynamic model is generated based on the first result information obtained above, Estimate the first control variable of the ship based on the dynamic model generated above, A device for optimizing control variables of an autonomous ship, which generates a second control variable that optimizes the first control variable based on second result information for the second test run process when the second test run process for the ship is completed using the estimated first control variable.

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