Multi-unmanned ship cooperative control propulsion system and method based on Roland C positioning

Abstract

The invention provides a multi-unmanned-ship cooperative control propulsion system and method based on Roland-C positioning, and relates to the field of unmanned ship control, the system comprises a Roland-C positioning system, a data processing module, a path planning module, a cooperative control module and a propulsion module, the Roland-C positioning system is used for providing real-time position information and course data of multiple unmanned ships, and the Roland-C positioning system is used for providing real-time course data of the unmanned ships; and the data processing module is used for transmitting the processed position information and environment data to the path planning module and the cooperative control module, dynamically adjusting the tracking propulsion path of the unmanned ship according to the optimal route, and generating a control instruction to enable the propulsion module to control the unmanned ships to keep formation and cooperatively work. Through a cooperative control strategy with strong robustness, formation stability and safe navigation of the unmanned ship are ensured so as to realize high-precision positioning, intelligent path planning and robustness cooperative control in a complex water area environment.

Description

Technical Field

[0001] The present invention relates to the field of unmanned ship control, in particular to the field of medical detection technology, and specifically to a multi-unmanned ship cooperative control propulsion system and method based on Loran C positioning. Background Art

[0002] At present, there have been many studies and applications of unmanned ship technology in positioning, path planning and collaborative control. Most unmanned ship solutions use GPS or Beidou navigation systems for positioning, combined with traditional path planning algorithms (such as A* algorithm, Dijkstra algorithm, etc.) to achieve autonomous navigation. However, these technologies still have limitations in complex water environments, such as limited positioning accuracy and reliability, insufficient dynamic adaptability of path planning algorithms, and complexity in the collaborative control of multiple unmanned ships. Specifically:

[0003] The GPS and BeiDou navigation systems have high positioning accuracy in open waters, but when the signals are blocked or interfered with (such as by tall buildings, islands or in bad weather), the positioning accuracy will drop significantly, or even signal loss will occur, affecting the navigation and control of unmanned ships and limiting positioning accuracy and reliability.

[0004] Traditional path planning algorithms are usually based on static environments and are difficult to cope with real-time changing environments (such as water flow interference and wind changes), resulting in insufficient flexibility and adaptability of the planned path. In addition, these algorithms fail to make full use of real-time environmental data for dynamic adjustments, which can easily lead to untimely obstacle avoidance or suboptimal paths, resulting in insufficient dynamic adaptability of the path planning algorithm.

[0005] In multi-ship systems, traditional collaborative control methods mostly use centralized control or distributed control based on preset strategies. These methods have high requirements on the robustness of the system and it is difficult to maintain the stability and collaborative consistency of multiple ships in complex environments. In addition, when faced with external interference, the control accuracy and response speed of traditional methods are limited, making it difficult to achieve efficient multi-ship collaborative operation, resulting in the complexity of multi-unmanned ship collaborative control.

[0006] In contrast, the Roland C system, as a ground radio navigation system, can provide stable high-precision positioning, and is particularly suitable for navigation in large areas and areas with many obstacles. Its advantages are: strong anti-interference ability: the propagation characteristics of the Roland C signal enable it to maintain a high positioning accuracy even when it is blocked or in harsh environments, which is suitable for the application of unmanned ships in complex water environments; wide coverage: the signal coverage of the Roland C system is large, which can meet the needs of long-distance navigation, and is particularly suitable for large-scale positioning and tracking in multi-unmanned ship collaborative tasks. Although the Roland C system has obvious advantages, there are still challenges in combining the propulsion module to achieve high-precision path planning and collaborative control of unmanned ships. Therefore, a system and method that can introduce a multi-agent reinforcement learning algorithm and combine the high-precision positioning of Roland C to achieve intelligent path planning and dynamic collaborative control of multiple unmanned ships is needed to overcome the shortcomings of the existing technology. Summary of the invention

[0007] In view of this, the purpose of the present invention is to propose a multi-unmanned ship cooperative control propulsion system and method based on Loran C positioning, aiming to overcome the shortcomings of unmanned ship positioning, path planning and cooperative control in the prior art, improve the accuracy and robustness of multi-unmanned ship cooperative navigation and control, and deeply integrate Loran C positioning technology with the propulsion module of the unmanned ship, so that each unmanned ship can obtain high-precision ground radio navigation data in real time, independently adjust the attitude and speed, and achieve precise synchronous control. An intelligent path planning and tracking algorithm based on multi-agent reinforcement learning (MARL) is introduced. The system adopts a centralized training and distributed execution strategy, so that each unmanned ship can dynamically optimize the motion trajectory according to real-time positioning data and environmental information, maintain the stability of the formation and achieve efficient obstacle avoidance and path planning. In view of the problem of cooperation of multiple unmanned ships in complex water environments, the present invention proposes a strong robust cooperative control strategy, which designs an adaptive reward function to deal with external factors such as water flow interference and wind changes, ensures the stability of the formation and safe navigation of the unmanned ship, and achieves high-precision positioning, intelligent path planning and robust cooperative control in complex water environments.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] Based on the above objectives, in a first aspect, the present invention provides a multi-unmanned ship cooperative control propulsion system based on Loran C positioning, comprising the following components:

[0010] Roland C positioning system, used to provide real-time position information and heading data for multiple unmanned ships;

[0011] A data processing module is used to collect and process the position information and heading data from the Loran-C positioning system and the environmental data from the environmental sensor, and transmit the processed position information and environmental data to the path planning module and the collaborative control module;

[0012] The path planning module is used to dynamically optimize the path according to the received location information and environmental data, and dynamically adjust the tracking and propulsion path of the unmanned ship according to the optimal route;

[0013] A collaborative control module is used to generate control instructions based on the received position information and environmental data to enable multiple unmanned ships to maintain formation and work collaboratively;

[0014] The propulsion module is used to receive the control commands generated by the path planning module and the collaborative control module, and control the navigation status of the unmanned ship's attitude, heading and speed for tracking and propulsion.

[0015] As a further solution of the present invention, the Loran C positioning system is used to calculate the current position and heading of the unmanned ship by time difference ranging method, and to perform positioning using low-frequency long-wave signals transmitted from a ground base station. The Loran C positioning system includes:

[0016] A signal receiving module, which is equipped on each unmanned ship and includes a multi-channel receiver and a signal processing unit;

[0017] The multi-channel receiver is used to receive signals from at least three Loran C ground base stations;

[0018] The signal processing unit is used to demodulate and analyze the timing of the received signal, extract the signal arrival time, and transmit the signal to the data processing module for time difference ranging calculation.

[0019] As a further solution of the present invention, when the data processing module performs time difference ranging calculation, the current position and heading of the unmanned ship are calculated based on the received signal delay difference; wherein, the signal delay difference is obtained by the signal processing unit by comparing the signal arrival time of each base station with the signal arrival time of the reference base station to obtain the arrival time difference; when calculating the current position of the unmanned ship, the arrival time difference data of at least three base stations are used to construct a set of equations to solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; when calculating the heading of the unmanned ship, based on the calculated current position of the unmanned ship, the direction of movement of the unmanned ship is calculated by comparing the positioning results at consecutive moments to obtain the heading information.

[0020] As a further solution of the present invention, the data processing module is also used to transmit the positioning data to the path planning module and the collaborative control module to guide the tracking and propulsion control of the propulsion module, wherein the positioning information is updated within a preset time interval, and the data processing module also uses the inertial navigation system data of the unmanned ship to correct errors in the positioning results.

[0021] As a further solution of the present invention, the propulsion module includes a thruster, a rudder and an attitude sensor, wherein the thruster is responsible for providing forward thrust and the power required for steering; the rudder is used to change the heading of the unmanned ship; the attitude sensor includes a gyroscope, an accelerometer and a magnetometer, which are used to monitor the attitude and acceleration information of the unmanned ship in real time; the propulsion module is used to adjust the thrust of the thruster and the angle of the rudder in real time according to the control commands generated by the receiving path planning module and the collaborative control module, so that the unmanned ship can navigate along a predetermined path.

[0022] As a further solution of the present invention, the path planning module is used to obtain the position information and heading data of the unmanned ship through the Loran C positioning system, and form environmental perception in combination with the environmental data provided by the environmental sensor, calculate the optimal navigation path based on the real-time environmental data, and automatically adjust the path when obstacles or environmental changes are detected.

[0023] As a further solution of the present invention, the data processing module includes a data acquisition unit, a data preprocessing unit and a communication unit. The data acquisition unit is used to collect and process position information and heading data from the Loran C positioning system and environmental data from the environmental sensor; the data preprocessing unit is used to process the position information, heading data and environmental data, and the communication unit is used to transmit the processed position information and environmental data to the path planning module and the collaborative control module.

[0024] In a second aspect, the present invention provides a multi-unmanned ship cooperative control propulsion method based on Loran C positioning, comprising the following steps:

[0025] On each unmanned ship, a signal receiving module receives low-frequency long-wave signals from at least three Loran C ground base stations;

[0026] The received signal is demodulated and time-series analyzed by the signal processing unit, the signal arrival time is extracted, and the signal arrival time is transmitted to the data processing module;

[0027] The data processing module calculates the current position and heading of the unmanned ship based on the signal delay difference, compares the signal arrival time of each base station with the signal arrival time of the reference base station, and obtains the arrival time difference;

[0028] Using the arrival time difference data of at least three base stations to construct an equation group, solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; according to the positioning results at consecutive moments, calculate the movement direction of the unmanned ship to obtain the heading information;

[0029] The data processing module updates the positioning data within a preset time interval, and uses the inertial navigation system data of the unmanned ship to correct the error of the positioning result;

[0030] The path planning module obtains the location information and heading data of the unmanned ship, and performs environmental perception in combination with the environmental data provided by the environmental sensor; calculates the optimal navigation path based on real-time environmental data, and automatically adjusts the path when obstacles or environmental changes are detected;

[0031] The collaborative control module generates control instructions based on the received position information and environmental data, so that multiple unmanned ships maintain formation and work collaboratively, and transmits the generated control instructions to the propulsion module;

[0032] The propulsion module receives the control commands generated by the path planning module and the collaborative control module, and adjusts the thrust of the propeller and the angle of the rudder in real time according to the control commands to control the attitude, heading and speed of the unmanned ship to ensure that the unmanned ship sails along the predetermined path.

[0033] Through the above steps, the multi-unmanned ship cooperative control propulsion method of the present invention can achieve efficient and accurate unmanned ship positioning and control, ensuring that multiple unmanned ships can flexibly respond to various changes in complex environments and achieve collaborative operations.

[0034] In another aspect of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned methods for cooperative control and propulsion of multiple unmanned ships based on Loran C positioning according to the present invention is executed.

[0035] In another aspect of the present invention, a computer-readable storage medium is provided, which stores computer program instructions. When the computer program instructions are executed, any one of the above-mentioned methods for cooperative control and propulsion of multiple unmanned ships based on Loran C positioning according to the present invention is implemented.

[0036] Compared with the prior art, the multi-unmanned ship cooperative control propulsion system and method based on Loran C positioning proposed in the present invention has the following beneficial effects:

[0037] 1. The present invention can achieve high-precision positioning by receiving signals from multiple Loran C ground base stations. This positioning method uses the principle of time difference ranging, which can effectively reduce the positioning error caused by environmental interference and ensure the position certainty of the unmanned ship in complex waters.

[0038] 2. The method of the present invention realizes real-time monitoring and dynamic adjustment of the position and heading of the unmanned ship through the inertial navigation system and signal update mechanism. This real-time performance ensures that the unmanned ship can quickly respond to environmental changes during navigation, improving navigation safety; by combining the data of environmental sensors for environmental perception, obstacles and other potential dangers can be discovered in a timely manner. This enables the unmanned ship to intelligently plan the optimal navigation path, reduce the risk of collision, and improve the safety and efficiency of navigation.

[0039] 3. The method of the present invention also realizes a collaborative control mechanism between multiple unmanned ships, enabling them to cooperate to complete complex tasks. By sharing position information and control instructions, the unmanned ships can maintain formation, perform group actions, and improve the efficiency and coordination of task execution. By automatically generating control instructions, the possibility of manual intervention and operational errors is reduced. This automated control mechanism improves the reliability of operation and enables unmanned ships to navigate autonomously in a wider range of application scenarios.

[0040] In summary, the multi-unmanned ship cooperative control propulsion method based on Roland C positioning can significantly reduce labor costs and operational risks through the cooperative operation and automated control of multiple unmanned ships. At the same time, optimized path planning can also reduce energy consumption and improve navigation efficiency, thereby further reducing operating costs, real-time monitoring of the attitude and acceleration information of the unmanned ship, ensuring navigation stability, and reducing navigation deviations caused by technical failures or external factors. It not only improves the positioning accuracy and navigation safety of the unmanned ship, but also enhances the automation and collaborative operation capabilities of the system, and has broad application prospects and significant economic benefits.

[0041] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the exemplary embodiments or related technical descriptions. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0043] Figure 1 The present invention is a block diagram of a multi-unmanned ship cooperative control propulsion system based on Loran C positioning according to an embodiment of the present invention.

[0044] Figure 2 The present invention is a flowchart of a method for cooperative control and propulsion of multiple unmanned ships based on Loran C positioning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product or device that includes a series of steps or units.

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0050] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0051] See also Figure 1As shown, an embodiment of the present invention provides a multi-unmanned ship cooperative control propulsion system based on Loran C positioning, which includes a Loran C positioning system, a data processing module, a path planning module, a cooperative control module and a propulsion module. The Loran C positioning system is used to provide real-time position information and heading data of multiple unmanned ships; the data processing module is used to collect and process the position information and heading data from the Loran C positioning system and the environmental data of the environmental sensor, and transmit the processed position information and environmental data to the path planning module and the cooperative control module; the path planning module is used to perform dynamic path optimization according to the received position information and environmental data, and dynamically adjust the tracking propulsion path of the unmanned ship according to the optimal route; the cooperative control module is used to generate control instructions based on the received position information and environmental data to keep the multiple unmanned ships in formation and work in coordination; the propulsion module is used to receive the control commands generated by the path planning module and the cooperative control module, and control the navigation state of the unmanned ship's attitude, heading and speed for tracking and propulsion.

[0052] The multi-unmanned ship cooperative control propulsion system based on Roland C positioning provided in this embodiment overcomes the deficiencies in unmanned ship positioning, path planning and cooperative control in the prior art, and realizes high-precision positioning, intelligent path planning and robust cooperative control in complex water environments. Through the deep integration of Roland C positioning technology and unmanned ship propulsion module, and combined with multi-agent reinforcement learning (MARL) algorithm, the system can dynamically optimize the path and cooperative control, so that multiple unmanned ships can effectively cope with various disturbances in complex waters, and achieve efficient synchronous navigation and task execution. Through the deep integration of Roland C positioning and propulsion modules, Roland C signals are used to provide high-precision positioning data for each unmanned ship. The long-wave signal of the Roland C system has strong anti-interference ability and a large coverage range, which is suitable for navigation in complex waters. The unmanned ship receives signals from multiple Roland C ground base stations, and calculates accurate position information and heading data through time difference ranging (TDOA), which can maintain a high positioning accuracy even in multiple obstacles or severe weather conditions.

[0053] In this embodiment, the Loran C positioning system is used to calculate the current position and heading of the unmanned ship through the time difference of arrival (TDOA) method, and uses the low-frequency long-wave signal transmitted from the ground base station for positioning. Since the long-wave signal has a strong anti-interference ability and a large propagation range, the Loran C system is particularly suitable for realizing high-precision unmanned ship positioning in multiple obstacles or complex water environments. The Loran C positioning system includes:

[0054] A signal receiving module, which is equipped on each unmanned ship and includes a multi-channel receiver and a signal processing unit;

[0055] The multi-channel receiver is used to receive signals from at least three Loran C ground base stations;

[0056] The signal processing unit is used to demodulate and analyze the timing of the received signal, extract the signal arrival time (TOA, Time of Arrival), and transmit the signal to the data processing module for time difference ranging calculation.

[0057] In this embodiment, when the data processing module performs time difference ranging calculation, the current position and heading of the unmanned ship are calculated based on the received signal delay difference; wherein, the signal delay difference is obtained by the signal processing unit by comparing the signal arrival time of each base station with the signal arrival time of the reference base station to obtain the arrival time difference; when calculating the current position of the unmanned ship, the arrival time difference data of at least three base stations are used to construct a set of equations to solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; when calculating the heading of the unmanned ship, based on the calculated current position of the unmanned ship, the direction of movement of the unmanned ship is calculated by comparing the positioning results at consecutive moments to obtain the heading information.

[0058] Among them, the Loran C positioning system is a ground radio navigation system that uses multiple base stations for synchronous broadcasting. It mainly achieves positioning by measuring the time difference of radio signals. This system belongs to the Hyperbolic Navigation System. By determining the distance difference between the receiving point and multiple transmitting stations, the position of the receiving point can be obtained. Among them, pulse signal: The Loran C transmitting station will periodically broadcast a set of radio pulse signals of a specific frequency. Time difference positioning: The receiving station (such as ships, aircraft, etc.) determines its own position by receiving these signals and calculating their arrival time difference.

[0059] The Roland C system includes multiple transmitters and a receiving terminal. Usually, a working chain consists of a master station and several slave stations. Master Station: mainly controls the transmission of signals and the time synchronization of the system. Secondary Stations: after receiving the master station signal, they send synchronization signals according to the preset time delay. These stations jointly transmit radio pulse signals, which are broadcast at low frequencies (90-110kHz). This frequency range is conducive to the long-distance propagation of radio waves. The working principle of the Roland C system is:

[0060] (1) Transmitting signal:

[0061] The master station first transmits a series of high-precision radio pulse signals.

[0062] After receiving the signal from the master station, the slave station transmits a set of synchronization signals after a fixed delay time.

[0063] (2) Signal transmission and reception:

[0064] The receiving device (receiver on the unmanned ship) will receive signals from the master station and several slave stations.

[0065] Due to the different distances between the transmitting station and the receiver, the propagation time of the signal is also different.

[0066] (3) Time difference measurement:

[0067] The receiver can measure the Time Difference of Arrival (TDOA) between the signals received from the master station and each slave station.

[0068] These time differences represent the relative distance difference between the receiver and each transmitting station. Based on the signal time difference of two transmitting stations, the receiver is located on a hyperbola, and based on the signal time difference of three or more transmitting stations, the exact position of the receiver can be determined.

[0069] (4) Position calculation:

[0070] Using the known geographic locations of the transmitting stations and the signal's propagation time differences, the receiver's position can be calculated using hyperbolic positioning.

[0071] On a plane, the curve formed by the distance difference between the two transmitting stations and the receiver is a hyperbola, and combined with another hyperbola formed by another transmitting station, the intersection is the position of the receiver.

[0072] Among them, Roland C uses the hyperbola positioning method to determine the position by measuring the distance difference between the receiver and different transmitting stations. The distance difference between each two transmitting stations will define a hyperbola, and the position of the receiver is at a certain point on this hyperbola. Using the intersection of multiple hyperbolas, the specific position of the receiver can be determined. Among them, in the hyperbola equation, for two transmitting stations A and B, the positions of the transmitting stations are (x A ,y A ) and (x B ,y B ), and the receiver's position is (x, y). Based on the time difference measurement, the distance difference d between the two can be obtained A -d B , from which we can get a hyperbola:

[0073]

[0074] By solving multiple hyperbolic equations, the position of the receiver can be found.

[0075] Loran-C positioning can achieve a positioning accuracy of within hundreds of meters, which is suitable for long-distance positioning in ocean environments; Loran-C uses low-frequency radio waves with long propagation distance and strong penetration ability, and is suitable for various harsh environmental conditions; through the signal collaboration of multiple transmitting stations, accurate synchronous positioning and tracking can be achieved, which is very suitable for scenarios such as unmanned ships that require multiple devices to work together.

[0076] When Roland C is used in unmanned ships, it performs synchronization and collaborative control. For multiple unmanned ships, the Roland C signal can provide consistent and synchronized position information, thereby achieving precise synchronization and collaborative control between multiple ships. Dynamic path tracking can also be performed. Combined with the propulsion module of the unmanned ship, the Roland C signal can be used for dynamic path planning and tracking to ensure that the unmanned ship can autonomously adjust the path in complex waters, avoid obstacles, and always maintain the set formation. The Roland C positioning system can achieve high-precision positioning of the receiver by measuring the propagation time difference of the radio signal and using the hyperbolic positioning method. This positioning method is particularly suitable for scenarios that require long-distance navigation and multi-device collaboration. For example, in a multi-unmanned ship system, using Roland C for high-precision positioning, combined with an intelligent propulsion module and control algorithm, it can achieve positioning and tracking of multiple unmanned ships, effectively solving the positioning accuracy and reliability problems in multi-ship collaborative work.

[0077] In this embodiment, the data processing module is also used to transmit the positioning data to the path planning module and the collaborative control module to guide the tracking and propulsion control of the propulsion module, wherein the positioning information is updated within a preset time interval, and the data processing module also uses the inertial navigation system data of the unmanned ship to perform error correction on the positioning result to improve the positioning accuracy. The Roland C positioning data provides the path planning module with real-time and accurate position information, so that the path planning algorithm can optimize the path based on the latest position in each time step. For example, when the path planning algorithm detects that the unmanned ship deviates from the preset trajectory, the direction of travel can be adjusted in time according to the latest positioning data to avoid hitting obstacles or deviating from the target path. The collaborative control module uses the Roland C positioning information to monitor the relative positions between multiple unmanned ships to ensure the stability of the formation. For example, the collaborative control module can calculate the distance and relative position changes between the unmanned ships in real time to determine whether the formation deviates from the preset formation. If a deviation is detected, the collaborative control module will restore the formation by adjusting the thrust or direction of the propulsion module.

[0078] In this embodiment, the propulsion module includes a thruster, a rudder and an attitude sensor. The thruster is responsible for providing forward thrust and the power required for steering; the rudder is used to change the heading of the unmanned ship; the attitude sensor includes a gyroscope, an accelerometer and a magnetometer, which are used to monitor the attitude and acceleration information of the unmanned ship in real time; the propulsion module is used to adjust the thrust of the thruster and the angle of the rudder in real time according to the control commands generated by the receiving path planning module and the collaborative control module, so that the unmanned ship can sail according to the predetermined path.

[0079] When the propulsion module control command is executed, the propulsion module receives control commands from the path planning module and the collaborative control module. These commands usually include parameters such as the required speed, thrust direction, and rudder angle. The system adjusts the thruster and rudder according to these commands to achieve precise control of the motion state of the unmanned ship. The specific process is as follows:

[0080] Speed ​​control: adjust the thrust of the propeller according to the control command to increase or decrease the speed of the ship. The thrust of the propeller can be achieved by adjusting the engine power or the angle of the propeller.

[0081] Direction control: Control the angle of the rudder to change the heading of the unmanned boat. The adjustment of the rudder angle is related to the speed of the boat. At low speed, a larger rudder angle is required to achieve steering, while at high speed, a smaller rudder angle is required to maintain a stable sailing.

[0082] In complex navigation environments, the propulsion module uses a model predictive control (MPC) algorithm to predict the future motion state of the ship and control it. MPC solves the optimization problem to determine the optimal thrust and rudder angle of the propeller, so that the unmanned ship moves along the planned trajectory. The algorithm can comprehensively consider multiple constraints (such as maximum thrust, steering angle limit) and environmental interference (such as water flow and wind) to ensure the stability and safety of navigation.

[0083] In this embodiment, the attitude sensor (gyroscope, accelerometer) monitors the attitude changes of the unmanned ship in real time, and dynamically adjusts the propeller through feedback control to ensure the stability of navigation. The specific steps are as follows:

[0084] Attitude data collection: The attitude sensor continuously collects the angular velocity, linear acceleration and azimuth information of the ship and transmits this data to the control system.

[0085] Attitude control feedback: The control system calculates the attitude changes of the unmanned ship based on the feedback data from the sensors, such as roll angle deviation, pitch angle fluctuation, etc. If the attitude deviates from the target state, the system will correct it by adjusting the thrust of the propeller or the rudder angle.

[0086] Stability control: When encountering external disturbances (such as water flow and wind), the propulsion module will automatically make compensation adjustments based on the feedback from the attitude sensor to maintain smooth sailing. For example, when the lateral swing is detected to increase, the system will eliminate the swing by reverse thrust and adjusting the rudder.

[0087] Among them, during adaptive adjustment, each unmanned ship adjusts its course and speed according to the intensity of the disturbance to restore the formation to the ideal state.

[0088] When adjusting the course and speed, each unmanned ship autonomously calculates the adjustment amount of course and speed based on the detected disturbance intensity.

[0089] When adjusting the heading, adjust the heading angle θ based on the wave height direction. adjust To offset external disturbances. The calculation formula is as follows:

[0090]

[0091] Among them, V disturb is the disturbance velocity, θ disturb is the disturbance direction, θ current is the current heading.

[0092] When adjusting the speed, increase or decrease the thrust of the propeller to offset the effect of water flow or wind on the speed. Calculate the adjusted speed V adjust for:

[0093] V adjust =V base + k·(V disturb -V threshold )

[0094] Among them, V base is the basic speed, and k is the adaptive gain coefficient.

[0095] Among them, during disturbance detection, if external factors such as wave height and wind speed exceed the set threshold, the system activates the disturbance detection mechanism.

[0096] When collecting sensors and setting thresholds, each unmanned ship is equipped with a wave height sensor and anemometer to monitor changes in environmental factors in real time. Set thresholds for water flow speed, wave height, and wind speed, such as:

[0097] Wave height threshold: H threshold = 1 meter;

[0098] Wind speed threshold: W threshold =10 m / s;

[0099] During data processing, the sensor collects data once per second and uses a sliding average algorithm to smooth the data to filter out random noise. The formula is:

[0100]

[0101] Wherein, X(t) is the current wave height or wind speed data, and N is the sliding window length (for example, set to 5 seconds).

[0102] Disturbance over-limit detection: Once the smoothed wave height or wind speed data exceeds the set threshold, the system immediately triggers the disturbance detection mechanism.

[0103] The propulsion module is deeply integrated with the Roland C positioning system. The real-time position data provided by Roland C can be used to correct the control accuracy of the propulsion module. For example, when the Roland C positioning detects that the unmanned ship deviates from the predetermined route, the propulsion module will automatically adjust the angle of the rudder and the thrust of the propeller according to the size of the error to restore the unmanned ship to the target path. The motion trajectory and speed commands generated by the path planning module are executed by the propulsion module to ensure that the unmanned ship travels along the predetermined path and speed. The propulsion module receives the update instructions of the path planning module in real time and makes dynamic adjustments to cope with environmental changes (such as water flow speed and direction).

[0104] Among them, regarding the efficiency optimization of the propeller, variable pitch propellers and electric propulsion modules are used. The variable pitch propeller can adjust the pitch according to the sailing speed and water flow conditions to achieve higher propulsion efficiency. The electric propulsion module can further reduce energy consumption by adjusting the output power of the motor, which is very suitable for the energy-saving needs of unmanned ships.

[0105] In this embodiment, the path planning module is used to obtain the position information and heading data of the unmanned ship through the Loran C positioning system, and form environmental perception in combination with the environmental data provided by the environmental sensor, calculate the optimal navigation path based on real-time environmental data, and automatically adjust the path when obstacles or environmental changes are detected.

[0106] The path planning module uses a multi-agent reinforcement learning (MARL) algorithm to dynamically optimize the path based on real-time environmental data (such as the position of the unmanned ship, water flow, wind speed and obstacle information) to ensure that the unmanned ship system can complete the task efficiently and safely in a complex water environment. The module uses an adaptive path planning strategy to adjust the route of the unmanned ship in real time, enabling it to avoid obstacles, save energy and maintain formation.

[0107] During the algorithm training phase, the path planning module adopts the centralized training and distributed execution (CTDE) strategy, which enables each unmanned ship to share state information during the training process, improving the global optimality of the strategy and the coordination ability of the system. The specific implementation steps are as follows:

[0108] Construction of virtual simulation environment: During the training process, a virtual simulation environment is first built to simulate a variety of dynamic environmental factors (such as water flow speed, wind direction changes, obstacle locations, etc.). These environmental conditions vary in order to enable the algorithm to adapt to different mission scenarios.

[0109] Application of multi-agent reinforcement learning algorithm: Use multi-agent reinforcement learning algorithm (such as Proximal Policy Optimization, PPO or Soft Actor-Critic, SAC) for training. During the training process, each unmanned ship (agent) learns to optimize the path in a complex environment by cooperating with each other and sharing state information.

[0110] Strategy optimization and update: At the end of each round of training, the algorithm will evaluate the performance of the current strategy and update the strategy based on the evaluation results. The evaluation criteria include path planning efficiency, obstacle avoidance success rate, energy consumption level, etc. Through multiple rounds of training iterations, the path planning strategy is continuously optimized to improve the overall performance of the unmanned ship system.

[0111] In the actual operation stage, the path planning module will independently perform path planning in the real environment according to the trained strategy. The path planning module receives the Loran C positioning data of the unmanned ship and the external environment data in real time to generate the optimal path that adapts to the current environment. The specific steps are as follows:

[0112] Real-time environmental perception and data input: The path planning module obtains the precise position and heading information of the unmanned ship through the Loran C positioning system, and combines the external data provided by environmental sensors (such as tachometers and anemometers) to form a complete environmental perception.

[0113] Dynamic execution of path planning strategy: Based on real-time environmental data, the path planning algorithm will quickly calculate the optimal navigation path. The algorithm will consider multiple objectives such as obstacle avoidance, formation maintenance, path length minimization, and energy consumption minimization, and dynamically adjust the path according to environmental changes.

[0114] Path replanning and feedback control: During operation, if new obstacles or environmental changes (such as sudden changes in water flow) are detected, the path planning module will automatically replan the path and adjust the route to avoid potential risks. The feedback control mechanism enables the unmanned ship to respond to environmental changes in real time and maintain a stable navigation state.

[0115] Each unmanned ship continuously monitors the deviation of its position and speed relative to the center of the formation and updates the control parameters in real time to ensure that the formation can quickly return to the ideal state. The feedback control formula is:

[0116]

[0117] Among them, ΔV is the speed adjustment, K p and K d is the feedback gain coefficient, D ideal and D actual For ideal and actual formation spacing.

[0118] The multi-objective reward function used by the path planning algorithm includes the following aspects to ensure the efficiency and safety of path planning:

[0119] Minimizing path length: By rewarding planning results with shorter paths, the unmanned ship is guided to reach the target point in the shortest distance to save time and energy.

[0120] Formation keeping: In multi-unmanned ship missions, rewards are given to ships that maintain the preset formation. This goal helps the collaborative control module maintain the relative positions between ships and ensure the overall stability of the system.

[0121] Obstacle avoidance success rate: The algorithm rewards planned paths that successfully avoid obstacles to improve the safety of the system. In complex waters, the obstacle avoidance success rate is an important indicator for evaluating the performance of path planning algorithms.

[0122] Minimizing energy consumption: By reducing unnecessary acceleration, deceleration, and turning actions, the path planning module can reduce the energy consumption of the propulsion module, thereby improving the energy efficiency of the unmanned ship system.

[0123] The local path optimization algorithm is combined with the global path planning to improve the real-time performance of the obstacle avoidance strategy and the global performance of the path planning. When an obstacle is detected, the local path planning will temporarily adjust the route to avoid collision, while the global path planning ensures that the unmanned ship reaches the destination along the optimal path. Enhance information sharing between multiple unmanned ships, send individual environmental perception and status data to other ships through wireless communication, and improve the overall performance of path planning. The collaborative decision-making algorithm enables the system to better handle group tasks and avoid mutual interference or repeated obstacle avoidance. When the water flow or wind changes, the path planning module can use a disturbance compensation mechanism to adjust the path. By predicting the impact of external disturbances, the path planning algorithm can pre-calculate the best avoidance action, thereby reducing deviations in actual navigation.

[0124] In this embodiment, the collaborative control module is designed to ensure that the multi-unmanned ship system maintains a stable and coordinated formation in a complex environment, especially in the face of external interference (such as water flow and wind changes), and can improve the robustness and stability of the system through adaptive adjustment strategies. Through distributed control, adaptive disturbance compensation and strategy optimization, the collaborative control module achieves efficient coordination and consistent actions among the unmanned ships.

[0125] In the multi-unmanned ship system, the collaborative control module adopts a distributed control strategy, which enables each unmanned ship to make independent decisions while sharing information with other ships. The specific implementation steps are as follows:

[0126] Configuration of communication module: Each unmanned ship is equipped with a wireless communication module for sending and receiving status information (such as position, speed, heading, etc.). This information is transmitted through the network, allowing each unmanned ship to understand the status of its teammates.

[0127] Execution of distributed control strategy: The collaborative control module adjusts the propulsion module parameters (such as thrust size and direction) of each unmanned ship based on the received status information to ensure that the relative positions between the ships remain stable. For example, if an unmanned ship deviates from the formation, the collaborative control module will adjust the thrust or rudder to return it to the preset position.

[0128] Frequency of information sharing: The frequency of information sharing is adjusted according to the task requirements and the complexity of the environment. In a highly dynamic environment (such as frequent changes in water flow speed), the frequency of information sharing should be increased to ensure the real-time response of the system.

[0129] The collaborative control module integrates an adaptive disturbance compensation algorithm to detect and respond to external disturbances (such as water velocity and wind speed changes), ensuring that the unmanned ship can make timely adjustments when disturbed. The specific steps are as follows:

[0130] Disturbance detection: Monitor environmental changes in real time through sensors (such as flow meters, anemometers), and input these disturbance data into the collaborative control module.

[0131] Disturbance compensation strategy: Dynamically adjust the control parameters of the propulsion module according to the type and intensity of the disturbance. For example, when the water flow speed increases, the system can increase the thrust or adjust the direction of the propeller to offset the impact of the water flow on the heading of the unmanned ship.

[0132] Adaptive adjustment: The disturbance compensation algorithm uses an adaptive control method to automatically adjust the rate of change of the control parameters according to the frequency and magnitude of environmental changes. For example, in the case of large disturbances, the system will quickly increase the adjustment force, while in the case of minor disturbances, the system will adjust more gently to avoid instability caused by over-response.

[0133] The collaborative control module uses the strategy improvement function of the multi-agent reinforcement learning (MARL) algorithm to achieve adaptive adjustment and optimization of the system. The specific implementation method is as follows:

[0134] MARL-based strategy learning: During the training phase, the system uses the collaborative data of multiple unmanned ships to learn strategies and optimize the collaborative control strategies of each ship. Through repeated training and strategy evaluation, the algorithm can learn how to keep the formation stable under sudden environmental changes.

[0135] Local adjustment and global coordination: During the execution process, if some unmanned ships deviate from the predetermined path or formation due to external interference, the system will first make local adjustments to these unmanned ships to ensure that they quickly return to the correct position. At the same time, the collaborative control module will evaluate the formation status of the entire system and make small global adjustments to the movements of other unmanned ships if necessary to maintain overall coordination.

[0136] Online strategy update: When faced with sudden environmental changes (such as sudden obstacles or drastic changes in water flow direction), the system will perform online updates based on the existing learning strategy. The collaborative control module will fine-tune the strategy based on real-time environmental feedback to make the system respond faster.

[0137] The collaborative control module acts as a bridge between the path planning and propulsion modules. The optimal path generated by the path planning module serves as the basis for collaborative control, while the collaborative control module adjusts the relative positions of the unmanned ships according to the results of path planning and changes in the environment to ensure the overall stability of the system. For example, the path planning module may generate a new path, and the collaborative control module is responsible for adjusting the positions and speeds of all unmanned ships so that they move synchronously toward the new path. The propulsion module executes the output commands of the collaborative control module and ensures that the unmanned ships act according to the instructions by adjusting the thrust of the propellers and the rudder angle. The collaborative control module further optimizes the control strategy based on the feedback data of the propulsion module (such as actual speed and direction) to achieve closed-loop control. In this way, the system can quickly adjust under the influence of external disturbances to improve navigation stability.

[0138] In this embodiment, the data processing module includes a data acquisition unit, a data preprocessing unit and a communication unit. The data acquisition unit is used to collect and process position information and heading data from the Loran C positioning system and environmental data from the environmental sensor; the data preprocessing unit is used to process the position information, heading data, and environmental data, and the communication unit is used to transmit the processed position information and environmental data to the path planning module and the collaborative control module.

[0139] Among them, in the communication protocol and data format, the system uses a low-latency wireless communication protocol (such as Wi-Fi or LoRa) to realize the status information sharing between unmanned ships. The shared information includes the current position, speed, heading and current mission status of each unmanned ship.

[0140] Data format of status information: Shared information is formatted uniformly. Each information includes the following fields:

[0141] Position coordinates (x, y): represents the two-dimensional coordinates of the current unmanned ship.

[0142] Speed ​​v: current speed of the unmanned ship.

[0143] Heading θ: The heading angle of the unmanned ship, used to adjust the formation direction.

[0144] Target position (x target ,y target ): The current mission target position of the unmanned ship.

[0145] When sharing frequency and synchronization, the sending frequency of status information is set to 2Hz to ensure that other unmanned ships receive updated data in time. Data is synchronized through timestamps to ensure the timeliness of information.

[0146] In order to further improve the positioning accuracy, the data processing module uses signal fusion technology to weighted average the positioning data from multiple base stations to eliminate the outliers that may appear in the signal of a single base station. At the same time, the anti-interference algorithm (adaptive filter) can be used to deal with positioning errors caused by water reflection or noise. The incremental positioning algorithm is used to make fine adjustments based on the previous positioning results in each positioning calculation to reduce the computational burden and improve real-time performance. This method can also be combined with the motion model of the unmanned ship (such as speed and acceleration data) for predictive corrections to improve the robustness of the positioning system. Using the attitude data provided by the inertial navigation system, the positioning results of the Roland C are fused with multi-source data to reduce positioning deviations caused by signal delays or multipath effects. This method can maintain continuous positioning of the unmanned ship when the Roland C signal quality is poor or the signal is interrupted.

[0147] A multi-unmanned ship cooperative control propulsion system based on Roland C positioning in this embodiment realizes dynamic control of the attitude and speed of the unmanned ship through the close coupling of the propulsion module with the Roland C positioning system. Each unmanned ship is equipped with a high-precision thruster and attitude sensor (such as a gyroscope and an accelerometer), which is adjusted in real time according to the Roland C positioning data. The thrust and direction of the thruster are adjusted through the PID control algorithm or the model predictive control (MPC) method to ensure that the unmanned ship travels along the predetermined path. The propulsion module can not only perform traditional heading adjustments, but also finely control the speed according to the navigation status, such as slowing down when maintaining a formation or speeding up when cruising over long distances to improve energy efficiency.

[0148] When adjusting the heading, if the unmanned ship detects a formation deviation, it calculates the relative position difference with the surrounding unmanned ships in real time, and adjusts the heading θ based on this position difference. The formula is as follows:

[0149]

[0150] Among them, (x avg ,y avg ) is the average position of adjacent unmanned ships.

[0151] When adjusting the speed, if the formation deviates from the ideal distance, adjust the speed v in real time to maintain the consistency of the formation. The adjustment formula is:

[0152] v=v base +k((D ideal -D actual )

[0153] Among them, v base is the reference speed, k is the control gain coefficient, D ideal and D actual are the ideal and actual spacings, respectively.

[0154] If an obstacle is detected by shared information or sensor data, the unmanned ship calculates an obstacle avoidance path and broadcasts the obstacle avoidance intention to other unmanned ships in the formation to avoid path conflicts.

[0155] The system introduces a path planning and tracking algorithm based on multi-agent reinforcement learning (MARL) to achieve dynamic optimization of the path of the unmanned ship. The design of the algorithm takes into account various dynamic environmental factors (such as water flow, wind speed, obstacles, etc.) of the multi-unmanned ship system in complex waters. Among them, the centralized training and distributed execution (CTDE) strategy is used to share the state information of each unmanned ship during the training phase to improve the learning efficiency of path planning and the global optimality of the strategy. Through deep reinforcement learning algorithms (such as Proximal Policy Optimization, PPO or Soft Actor-Critic, SAC), the system can update the path planning strategy in real time according to environmental information. The reward function design combines multiple goals such as shortest path length, formation maintenance, obstacle avoidance success rate, and energy consumption minimization to guide the algorithm to find a balance point, thereby achieving the optimality of the path and the efficiency of the task. For example, the reward function can be adjusted according to the time efficiency, obstacle avoidance and energy saving effect of path planning to adapt to different task requirements. In the real-time operation stage, each unmanned ship uses the trained strategy to generate control commands to control the thrusters and rudders to achieve dynamic adjustment of the motion trajectory. These control commands are generated by the system's path planning module, enabling the unmanned ship to avoid obstacles and maintain a preset formation.

[0156] Among them, in the path planning and collaborative control based on the multi-agent reinforcement learning (MARL) algorithm, the reward function of each unmanned ship consists of the following main parts:

[0157] Minimize the path length (R path ): Reward planning paths to be as short as possible to save time and resources.

[0158] Formation maintenance (R formation): In multi-unmanned ship missions, rewards are given to ships that maintain a preset formation to ensure that the relative positions of the ships are stable.

[0159] Obstacle avoidance success rate (R obstacle ): Reward paths that successfully avoid obstacles to improve safety.

[0160] Minimize energy consumption (R energy ): Encourage the reduction of energy consumption of propulsion modules and improve energy efficiency.

[0161] The combination of these parts forms the overall reward function, expressed as:

[0162] R global =w path ·R path +w formation ·R formation +w obstacle ·R obstacle +w energy ·R energy

[0163] Among them, w path 、w formation 、w obstacle , and w energy They are the weight coefficients of various rewards, which are adjusted according to task requirements and environmental dynamics.

[0164] The goal of path length minimization is to reduce the distance of the unmanned ship from the starting point to the end point as much as possible to save time. Let the current path length be d and the expected shortest path length be d min , then the path length reward can be expressed as:

[0165] R path =-α(dd min )

[0166] Among them, α is the coefficient that controls the penalty of path length. The shorter the path, the higher the R path The closer to zero, the higher the reward.

[0167] The formation keeping reward ensures that multiple unmanned ships can maintain a stable relative position when performing tasks. For any two unmanned ships i and j, their relative distance is d ij , the target distance is d target The formation keeping reward can be defined as:

[0168]

[0169] Among them, β controls the penalty intensity of formation deviation. The smaller the distance deviation, the higher the reward, which is conducive to maintaining the stability of the formation.

[0170] The obstacle avoidance success rate reward encourages the unmanned boat to avoid obstacles and ensure the safety of the path. Suppose the minimum safe distance between the obstacle and the unmanned boat is d safe , if the distance between the unmanned ship and the nearest obstacle is d ob ≥d safe , then the obstacle avoidance reward is:

[0171] R obstacle =γ·(d ob -d safe )

[0172] Where γ is the obstacle avoidance reward coefficient. ob Greater than d safe When the unmanned boat approaches an obstacle, it will be punished to ensure a safe distance.

[0173] The energy consumption minimization reward encourages reducing the energy consumption of the propulsion module. Assuming the current total energy consumption of the unmanned ship is EEE, and the expected energy consumption is EtargetE_{target}Etarget, the energy consumption reward is:

[0174] R energy = -δ·(EE target )

[0175] Where δ is the penalty coefficient for energy consumption. When the energy consumption is lower than or close to E target When the reward is higher, it helps to improve energy efficiency.

[0176] Moreover, in order to ensure that the multi-unmanned ship system maintains a stable formation and coordinated actions in complex environments such as water flow interference and wind changes, the present invention adopts a highly robust collaborative control strategy, and the system adopts a distributed control method. Each unmanned ship makes independent decisions but shares information. Each unmanned ship receives the status information (such as position, speed, and heading) of other unmanned ships through a communication module, and makes adaptive adjustments according to the status of the overall formation and task requirements. This information sharing mechanism can improve the response speed and stability of the system under various disturbance conditions. An adaptive disturbance compensation algorithm is introduced to detect changes in the external environment (such as water flow speed and direction, wind speed) in real time, and adjust the control parameters accordingly. For example, when an increase in water flow speed is detected, the system can increase the thrust of the propeller or adjust the heading to offset the impact of external interference. The strategy improvement function of the MARL algorithm is used to continuously optimize the control strategy and enhance the robustness of the system in a high-interference environment. Each unmanned ship can autonomously adjust the strategy based on the MARL algorithm to improve the overall collaborative control effect of the system.

[0177] See also Figure 2 As shown, an embodiment of the present invention also provides a multi-unmanned ship cooperative control propulsion method based on Loran C positioning, comprising the following steps:

[0178] Step S10: On each unmanned ship, a signal receiving module receives low-frequency long-wave signals from at least three Loran C ground base stations;

[0179] Step S20: demodulate and analyze the timing of the received signal through the signal processing unit, extract the signal arrival time, and transmit the signal arrival time to the data processing module;

[0180] Step S30: the data processing module calculates the current position and heading of the unmanned ship according to the signal delay difference, compares the signal arrival time of each base station with the signal arrival time of the reference base station, and obtains the arrival time difference;

[0181] Step S40: construct a set of equations using arrival time difference data of at least three base stations to solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; calculate the movement direction of the unmanned ship according to the positioning results at consecutive moments to obtain heading information;

[0182] Step S50: updating the positioning data within a preset time interval through the data processing module, and performing error correction on the positioning result using the inertial navigation system data of the unmanned ship;

[0183] Step S60: Obtain the location information and heading data of the unmanned ship through the path planning module, and perform environmental perception in combination with the environmental data provided by the environmental sensor; calculate the optimal navigation path based on the real-time environmental data, and automatically adjust the path when obstacles or environmental changes are detected;

[0184] Step S70: Generate control instructions through the collaborative control module according to the received position information and environmental data, so that the multiple unmanned ships maintain formation and work collaboratively, and transmit the generated control instructions to the propulsion module;

[0185] Step S80: The propulsion module receives the control commands generated by the path planning module and the collaborative control module, and adjusts the thrust of the propeller and the angle of the rudder in real time according to the control commands to control the attitude, heading and speed of the unmanned ship to ensure that the unmanned ship navigates according to the predetermined path.

[0186] Through the above steps, the multi-unmanned ship cooperative control propulsion method of the present invention can achieve efficient and accurate unmanned ship positioning and control, ensuring that multiple unmanned ships can flexibly respond to various changes in complex environments and achieve collaborative operations.

[0187] In the process of position calculation, the arrival time difference (ΔTOA) data of at least three base stations are used to calculate the two-dimensional or three-dimensional coordinates of the unmanned ship through geometric methods. The following are the specific calculation formulas and steps:

[0188] 1. Definition of pseudorange difference:

[0189] Assume that the known coordinates of base stations A, B, and C are (x A ,y A ,z A )、(x B ,y B ,z B )、(x C ,y C ,z C )The unknown coordinates of the unmanned ship are (x, y, z).

[0190] The pseudo-range difference between the base station and the unmanned ship is defined as: Δd ij =c·(ΔT ij ) where ΔT ij is the arrival time difference between base station i and base station j to the unmanned ship, and c is the propagation speed of electromagnetic waves in the air (about 3×108m / s).

[0191] 2. Construct pseudorange equation:

[0192] According to the pseudo-range difference formula, the distance relationship between base stations A, B, C and the position of the unmanned ship can be obtained. Assuming that base station A is the reference base station, the following equation group is constructed:

[0193]

[0194] 3. Position calculation process:

[0195] Plane simplification (two-dimensional case): If the unmanned ship is on a horizontal surface (assuming z = 0), the equations can be simplified to two dimensions:

[0196]

[0197] Three-dimensional solution: If you need to solve the three-dimensional coordinates, use the three independent equations of the pseudo-range equations and use an iterative algorithm (Newton-Raphson method) to gradually approach the optimal solution:

[0198] Initial guess of the coordinates of the unmanned ship (x0, y0, z0,).

[0199] Calculate the equation residual Δd based on the current coordinates ij .

[0200] Update the coordinate estimates:

[0201] x new =x old -J -1 ·Δd

[0202] Among them, J is the Jacobian matrix and Δd is the current residual value.

[0203] Repeat the iteration until the residual is smaller than the set threshold.

[0204] 4. Accuracy evaluation of solution results:

[0205] To ensure the accuracy of the position solution, the residual |Δd ij -c·(ΔT ij )|Evaluate to ensure that the residual is less than 0.5 meters to ensure that the positioning accuracy of the unmanned ship meets the mission requirements.

[0206] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0207] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0208] According to a third aspect of an embodiment of the present invention, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[0209] The computer device includes a processor and a memory, and may also include an input system and an output system. The processor, memory, input system and output system may be connected via a bus or other means, and the input system may receive input digital or character information, and generate signal input related to the migration of multi-unmanned ship cooperative control propulsion based on Loran C positioning. The output system may include display devices such as display screens.

[0210] As a non-volatile computer-readable storage medium, the memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the multi-unmanned ship cooperative control propulsion method based on Roland C positioning in the embodiment of the present application. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of the multi-unmanned ship cooperative control propulsion method based on Roland C positioning, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0211] The processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data. The processors of multiple computer devices of the computer device of this embodiment execute various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, that is, implementing the steps of the multi-unmanned ship cooperative control propulsion method based on Roland C positioning in the above method embodiment.

[0212] It should be understood that, to the extent that they do not conflict with each other, all the embodiments, features and advantages described above for the multi-unmanned ship collaborative control propulsion method based on Loran-C positioning according to the present invention are also applicable to the multi-unmanned ship collaborative control propulsion method based on Loran-C positioning and storage medium according to the present invention.

[0213] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0214] Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. As an example and not by way of limitation, a non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. A volatile memory may include a random access memory (RAM), which may act as an external cache memory. As an example and not by way of limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to include, but are not limited to, these and other suitable types of memory.

[0215] The various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure herein may be implemented or executed using the following components designed to perform the functions herein: a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and / or any other such configuration.

[0216] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0217] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0218] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A multi-unmanned ship cooperative control propulsion system based on Loran C positioning, characterized in that: It includes the following components: Roland C positioning system, used to provide real-time position information and heading data for multiple unmanned ships; A data processing module is used to collect and process the position information and heading data from the Loran-C positioning system and the environmental data from the environmental sensor, and transmit the processed position information and environmental data to the path planning module and the collaborative control module; The path planning module is used to dynamically optimize the path according to the received location information and environmental data, and dynamically adjust the tracking and propulsion path of the unmanned ship according to the optimal route; A collaborative control module is used to generate control instructions based on the received position information and environmental data to enable multiple unmanned ships to maintain formation and work collaboratively; The propulsion module is used to receive the control commands generated by the path planning module and the collaborative control module, and control the navigation status of the unmanned ship's attitude, heading and speed for tracking and propulsion.

2. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 1 is characterized in that The Loran C positioning system is used to calculate the current position and heading of the unmanned ship by using the time difference ranging method, and to perform positioning using the low-frequency long-wave signal transmitted from the ground base station. The Loran C positioning system includes: A signal receiving module, which is equipped on each unmanned ship and includes a multi-channel receiver and a signal processing unit; The multi-channel receiver is used to receive signals from at least three Loran C ground base stations; The signal processing unit is used to demodulate and analyze the timing of the received signal, extract the signal arrival time, and transmit the signal to the data processing module for time difference ranging calculation.

3. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 2 is characterized in that: When the data processing module performs time difference ranging calculation, the current position and heading of the unmanned ship are calculated based on the received signal delay difference; wherein, the signal delay difference is obtained by the signal processing unit by comparing the signal arrival time of each base station with the signal arrival time of the reference base station to obtain the arrival time difference; when calculating the current position of the unmanned ship, the arrival time difference data of at least three base stations are used to construct a set of equations to solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; when calculating the heading of the unmanned ship, the movement direction of the unmanned ship is calculated based on the calculated current position of the unmanned ship by comparing the positioning results at consecutive moments to obtain the heading information.

4. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 1, characterized in that: The data processing module is also used to transmit the positioning data to the path planning module and the collaborative control module to guide the tracking and propulsion control of the propulsion module, wherein the positioning information is updated within a preset time interval, and the data processing module also uses the inertial navigation system data of the unmanned ship to correct the error of the positioning result.

5. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 1, characterized in that: The propulsion module includes a thruster, a rudder and an attitude sensor. The thruster is responsible for providing forward thrust and the power required for steering; the rudder is used to change the heading of the unmanned ship; the attitude sensor includes a gyroscope, an accelerometer and a magnetometer, which are used to monitor the attitude and acceleration information of the unmanned ship in real time; the propulsion module is used to adjust the thrust of the thruster and the angle of the rudder in real time according to the control commands generated by the receiving path planning module and the collaborative control module, so that the unmanned ship can navigate along the predetermined path.

6. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 5, characterized in that: The path planning module is used to obtain the position information and heading data of the unmanned ship through the Loran C positioning system, and form environmental perception in combination with the environmental data provided by the environmental sensor, calculate the optimal navigation path based on real-time environmental data, and automatically adjust the path when obstacles or environmental changes are detected.

7. The multi-unmanned ship cooperative control propulsion system based on Loran C positioning as claimed in claim 6, characterized in that: The data processing module includes a data acquisition unit, a data preprocessing unit and a communication unit. The data acquisition unit is used to collect and process the position information and heading data from the Loran C positioning system and the environmental data of the environmental sensor; the data preprocessing unit is used to process the position information, heading data and environmental data, and the communication unit is used to transmit the processed position information and environmental data to the path planning module and the collaborative control module.

8. A multi-unmanned ship cooperative control propulsion method based on Loran C positioning, characterized in that: The method is executed based on the multi-unmanned ship cooperative control propulsion system based on Loran C positioning according to any one of claims 1 to 7, and the method comprises the following steps: On each unmanned ship, a signal receiving module receives low-frequency long-wave signals from at least three Loran C ground base stations; The received signal is demodulated and time-series analyzed by the signal processing unit, the signal arrival time is extracted, and the signal arrival time is transmitted to the data processing module; The data processing module calculates the current position and heading of the unmanned ship based on the signal delay difference, compares the signal arrival time of each base station with the signal arrival time of the reference base station, and obtains the arrival time difference; Using the arrival time difference data of at least three base stations to construct an equation group, solve the two-dimensional or three-dimensional position coordinates of the unmanned ship; according to the positioning results at consecutive moments, calculate the movement direction of the unmanned ship to obtain the heading information; The data processing module updates the positioning data within a preset time interval, and uses the inertial navigation system data of the unmanned ship to correct the error of the positioning result; The path planning module obtains the location information and heading data of the unmanned ship, and performs environmental perception in combination with the environmental data provided by the environmental sensor; calculates the optimal navigation path based on real-time environmental data, and automatically adjusts the path when obstacles or environmental changes are detected; The collaborative control module generates control instructions based on the received position information and environmental data, so that multiple unmanned ships maintain formation and work collaboratively, and transmits the generated control instructions to the propulsion module; The propulsion module receives the control commands generated by the path planning module and the collaborative control module, and adjusts the thrust of the propeller and the angle of the rudder in real time according to the control commands to control the attitude, heading and speed of the unmanned ship, ensuring that the unmanned ship sails along the predetermined path.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, which, when executed by the processor, executes the multi-unmanned ship cooperative control propulsion method based on Loran C positioning as described in claim 8.

10. A computer-readable storage medium storing computer program instructions, characterized in that: When the computer program instructions are executed, the multi-unmanned ship cooperative control propulsion method based on Loran C positioning described in claim 8 is performed.

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