A remote control system for unmanned vessels used in surveying and mapping

Abstract

The present invention discloses a remote control system for an unmanned ship based on surveying and mapping, which relates to the technical field of unmanned ships. The system includes an environmental perception module, a path planning module, a remote control module, and an adaptive surveying and mapping parameter module. By integrating a high-resolution lidar and a multispectral camera, the present invention realizes the precise perception of the surrounding environment, including key information such as water body color, water quality, underwater terrain features, and water flow velocity. These information are transmitted to the path planning module in real time. By constructing a dynamic environmental model and using a path fitness evaluation formula for global path planning, during the navigation process, according to local environmental changes, such as suddenly appearing dangerous obstacles and sharp changes in water flow, through a local path adjustment formula and an emergency path evaluation formula, the current path can be quickly evaluated and adjusted to ensure that the unmanned ship can avoid dangerous areas.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vessels, and specifically to a remote control system for an unmanned vessel for surveying and mapping. Background Art

[0002] With the continuous development of surveying and mapping technology and the increasing expansion of application fields, as an efficient and flexible surveying and mapping tool, unmanned vessels are playing an increasingly important role in the surveying and mapping operations of marine and lake waters. Unmanned vessel surveying and mapping technology not only improves the efficiency and accuracy of surveying and mapping operations, but also reduces labor costs and risks. However, in practical applications, unmanned vessels face complex and changeable water environments, such as water flow changes, uneven water depths, complex underwater terrains, and numerous obstacles, which pose severe challenges to the navigation and surveying and mapping operations of unmanned vessels.

[0003] When traditional unmanned vessel surveying and mapping technology conducts path planning and environmental modeling, it often relies on preset map data and simple obstacle avoidance algorithms, and it is difficult to reflect the dynamic changes of the water environment in real time and accurately. In terms of real-time path planning, traditional methods lack comprehensive consideration of environmental factors (such as water flow, water depth, underwater terrain, obstacles), resulting in the planned path may not be optimal and even have potential safety hazards. At the same time, when traditional methods handle emergencies, such as suddenly emerging obstacles and rapid changes in water flow, they often react slowly and are unable to adjust the path in time, thus increasing the risk of collision between the unmanned vessel and obstacles and falling into dangerous areas.

[0004] In addition, traditional unmanned vessel surveying and mapping technology also has deficiencies in constructing a dynamic environmental model. Due to limited means of obtaining and processing environmental information, traditional methods are difficult to construct an accurate and comprehensive environmental model, and cannot provide strong support for path planning and surveying and mapping operations. Therefore, in a complex and changeable water environment, the efficiency and safety of traditional unmanned vessel surveying and mapping technology are severely restricted.

[0005] Therefore, developing a remote control system for an unmanned vessel for surveying and mapping, accurately analyzing water surface factors, and using advanced intelligent algorithms to automatically plan the optimal navigation path and surveying and mapping route has important significance for promoting the wide application of unmanned vessel technology in the field of marine surveying and mapping. Summary of the Invention

[0006] The object of the present invention is to make up for the deficiencies of the prior art and provide a remote control system for an unmanned ship for surveying and mapping. It can perceive the environment in real time through a high-resolution lidar and a multispectral camera, construct a dynamic three-dimensional environment model, comprehensively analyze factors such as water flow, water depth, underwater terrain and obstacles, and use intelligent algorithms to realize real-time planning and optimization of global and local paths, ensuring that the unmanned ship can efficiently and safely complete the surveying and mapping tasks in a complex water environment. At the same time, the system can adaptively adjust the surveying and mapping parameters, improve the surveying and mapping accuracy and efficiency, and provide strong technical support for the unmanned ship surveying and mapping operation.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A remote control system for an unmanned ship for surveying and mapping, the system includes the following components:

[0008] The environment perception module: uses a high-resolution lidar and a multispectral camera for environment perception. The lidar emits laser beams and receives reflected signals, and determines the distance and position of the target object using the time difference. The multispectral camera takes multispectral images under different spectral bands, extracts spectral information of water body color, water quality and underwater terrain features, fuses the information obtained by the high-resolution lidar and the multispectral camera, and transmits it to other modules;

[0009] The path planning module: receives the three-dimensional information and spectral information provided by the environment perception module, performs real-time analysis and processing, constructs a dynamic environment model through a three-dimensional environment information fusion formula, and based on the dynamic environment model, according to the starting point and end point of the task target, uses the path fitness evaluation formula for global path planning to generate a path plan. During the navigation of the unmanned ship, local path adjustment is performed through a local path adjustment formula to optimize the path in real time. When the unmanned ship encounters suddenly emerging dangerous obstacles and rapid changes in water flow, through the emergency situation path evaluation formula, quickly evaluate and adjust the current path to avoid dangerous areas, calculate the optimal heading angle and speed value according to the adjusted path, and send control instructions to the navigation system of the unmanned ship. After the adjustment is completed, continuously evaluate and optimize the navigation path;

[0010] The path planning module uses the path fitness evaluation formula for global path planning. Let the path be , where represents a node on the path, and the path length The calculation formula is: , where, represents the node and The Euclidean distance between them, and the path safety evaluation value The calculation formula is: , where, is the maximum water flow velocity on the path, is the weight coefficient of the water flow velocity, is the safety water depth threshold, is the minimum water depth on the path, is the weight coefficient of the water depth, is the distance from the path to the nearest obstacle, is the weight coefficient of the obstacle, and the degree of avoidance of the path to the obstacle The calculation formula is: , where is the minimum distance from the path to the nearest obstacle, is the weight coefficient of the distance, is the angle between the path direction and the obstacle direction, represents the influence of the angle on the degree of avoidance, is the weight coefficient of the angle. Taken together, the path fitness evaluation formula is: , where , , are the weight coefficients of length, safety, and obstacle avoidance degree respectively;

[0011] The path planning module performs local path adjustment through the local path adjustment formula. Assume that the position of the unmanned ship at time is , and the target position is . Assume that the vector from the current position to the target position is , and the current speed vector of the unmanned ship is . The set of obstacles in the environment is , where the obstacle 's position is . Define the vector from the unmanned ship to the obstacle as . The repulsive force function , where is the distance from the unmanned ship to the obstacle, is a constant. The local path adjustment vector of the unmanned ship at time is calculated through the formula. The formula is: , where is the target attraction coefficient, is the repulsive force coefficient of the obstacle ;

[0012] The path planning module adjusts the path through the emergency path evaluation formula. The total path cost is composed of the actual cost from the starting point to the current position and the estimated cost from the current position to the target position : , the actual cost The calculation formula is as follows: , where represents the movement cost from node to node , and the estimated cost The calculation formula is as follows: where is the Euclidean distance from the current position to the target position, represents the distance estimation considering the influence of water flow, represents the distance estimation considering the influence of obstacles, is the corresponding weight coefficient;

[0013] The remote control module: uses wireless communication technologies such as 4G / 5G networks and satellite communication to establish communication with the unmanned ship, obtains the position, status, and mapping data of the unmanned ship, monitors the status information and mapping data of the unmanned ship in real time through the user interface, and sends commands to the unmanned ship and executes operations according to the actual situation;

[0014] The adaptive mapping parameter module: collects environmental information and historical data and preprocesses them, selects a mapping parameter adjustment function to establish a relationship model between mapping parameters and environmental factors, and when the unmanned ship enters a new area, performs operations according to the mapping parameters output by the model.

[0015] Furthermore, the path planning module constructs a dynamic environment model through a three-dimensional environmental information fusion formula. Let the set of three-dimensional information obtained by the lidar be , where , respectively represent the coordinate values in three-dimensional space, represents the specific lidar eigenvalue of this point. Let the set of spectral information obtained by the multispectral camera be , where , represents the eigenvalue in the th spectral band. The formula is: , where is the mean value of the lidar information, is the mean value of the spectral information, is the mean value of the joint information, is the weight coefficient.

[0016] Even further, the path length in the path planning module is obtained by calculating the sum of the distances between adjacent nodes, is the calculated path The distance between adjacent nodes is calculated using the Euclidean distance formula, and the Euclidean distance calculation formula is: , represents the node coordinates in the two-dimensional plane.

[0017] Furthermore, the path safety evaluation value in the path planning module is calculated by factors such as water flow velocity, water depth, and obstacle proximity. is the maximum water flow velocity on the path, and its reciprocal is taken , the smaller the water flow velocity, the higher the safety. is the weight coefficient of the water flow velocity, which adjusts the importance of the water flow velocity in the safety evaluation according to the actual situation, representing the water depth safety. is the weight coefficient of the water depth. is the safe water depth threshold. is the minimum water depth on the path, and the ratio of the difference between the two and the safe water depth threshold is calculated. is the distance from the path to the nearest obstacle, and its reciprocal is taken represents the safety of the obstacle proximity. is the weight coefficient of the obstacle.

[0018] Furthermore, the degree of obstacle avoidance of the path in the path planning module is determined by calculating the minimum distance between the path and the obstacle and the angle between the path direction and the obstacle direction. is the minimum distance from the path to the nearest obstacle, and its reciprocal is taken , representing the degree of avoidance. is the distance is the angle between the path direction and the obstacle direction, and is calculated. When the path is perpendicular to the obstacle direction, the angle is 90°, representing the best avoidance state. is the weight coefficient of the angle.

[0019] Furthermore, the actual cost in the path planning module is obtained by summing the movement costs between all adjacent nodes on the path. For the path , where represents a node on the path. represents from node to node 's movement cost, which is determined according to factors such as energy consumption and time cost.

[0020] Furthermore, the estimated cost in the path planning module is obtained by summing the adjusted Euclidean distance, water flow influence, and obstacle influence under different weights. is the Euclidean distance from the current position to the target position, calculated using the Euclidean distance formula. According to the water flow velocity of and the included angle between the water flow direction and the path direction of it is calculated, and the calculation formula is: where is an adjustment coefficient. It is determined by calculating the distance from the path to the nearest obstacle and the size and threat level of the obstacle. The calculation formula is: where is the minimum distance from the path to the nearest obstacle, is the adjustment coefficient.

[0021] Furthermore, the adaptive mapping parameter module selects a mapping parameter adjustment function to establish a relationship model between mapping parameters and environmental factors. Let the comprehensive environmental information obtained by the environmental perception module be and the current set of mapping task requirement parameters be where represents the set of mapping parameters after adjusting different mapping task requirement parameters as where represents different mapping parameters, and the function expression is: where, is a custom function, indicating the determination of mapping parameters according to environmental information and task requirements, is the weight coefficient, used to adjust the influence of environmental information and task requirement parameters on mapping parameters, is a component in the comprehensive environmental information corresponding to different environmental factors, is a parameter in the set of mapping task requirement parameters

[0022] Compared with the prior art, this remote control system for an unmanned ship based on mapping has the following beneficial effects:

[0023] ​1. The present invention integrates a high-resolution lidar and a multispectral camera to achieve precise perception of the surrounding environment, including key information such as water body color, water quality, underwater terrain features, and water flow velocity. This information is transmitted in real time to the path planning module. By constructing a dynamic environment model and using a path fitness evaluation formula for global path planning, during navigation, it can quickly evaluate and adjust the current path according to local environmental changes, such as suddenly appearing dangerous obstacles and sharp changes in water flow, through a local path adjustment formula and an emergency path evaluation formula, ensuring that the unmanned vessel can avoid dangerous areas and achieve efficient and safe surveying and mapping operations. This real-time path planning ability not only improves the surveying and mapping efficiency but also significantly enhances the safety and stability of the unmanned vessel in complex water environments.

[0024] 2. By adopting an advanced path optimization algorithm, the present invention can, on the basis of global path planning, combine local path adjustment and emergency path evaluation to achieve continuous optimization of the path. By calculating the path length, safety evaluation value, and the degree of obstacle avoidance of the path and assigning corresponding weight coefficients, the system can generate optimal heading angle and speed values to ensure that the unmanned vessel sails along the optimal path. In addition, it can also dynamically adjust surveying and mapping parameters according to environmental changes to meet the requirements of different surveying and mapping tasks. This intelligent optimization and dynamic adaptation path planning strategy enables the unmanned vessel to flexibly handle various complex situations in surveying and mapping operations, improving the surveying and mapping accuracy and efficiency.

[0025] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0027] Figure 1 It is a flowchart of a remote control system for a surveying and mapping unmanned vessel;

[0028] Figure 2 It is a flowchart of the path planning module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1:

[0031] This embodiment provides a remote control system for an unmanned ship used for surveying and mapping, which is used for surveying and mapping in a river with rapid water flow.

[0032] The high-resolution lidar emits laser beams and receives reflected signals, determines the distance and position of the target object by using the time difference, and constructs a three-dimensional model of the surrounding environment. Let the set of three-dimensional information obtained by the lidar be , where , respectively represent the coordinate values in the three-dimensional space, represents the specific lidar eigenvalue of this point. The multispectral camera takes multispectral images under different spectral bands and extracts the spectral information of the water body color, water quality and underwater terrain features. Let the set of spectral information obtained by the multispectral camera be , where , represents the eigenvalue in the th spectral band. The water flow velocity is analyzed by using the information of the lidar and the multispectral camera and relevant algorithms. For example, the water flow velocity is estimated by observing the displacement of objects in the water body combined with the time information , where represents the path, the water depth information is measured, and the safe water depth threshold and the minimum water depth on the path are determined. The position of the obstacles in the river is detected through the three-dimensional model construction of the lidar and the image analysis of the multispectral camera. Let the position of the obstacle be .

[0033] Receive the three-dimensional information and spectral information provided by the environmental perception module, perform real-time analysis and processing, fuse the information obtained by the high-resolution lidar and the multispectral camera, and transmit it to other modules. A dynamic environment model is constructed through the three-dimensional environment information fusion formula, and the fusion function is defined as: , where is the mean value of the lidar information, is the mean value of the spectral information, is the mean value of the joint information, is the weight coefficient.

[0034] According to the starting point and the ending point of the task objective, a dynamic environment model is constructed, and a global path planning is generated using the path fitness evaluation formula to generate a path plan. Let the path , where represents a node on the path, and the path length The calculation formula is: , where represents the node and The Euclidean distance between them, and the Euclidean distance calculation formula is: , represents the node coordinates in the two-dimensional plane, and the path safety evaluation value The calculation formula is: , where is the maximum water flow velocity on the path, is the weight coefficient of the water flow velocity, is the safe water depth threshold, is the minimum water depth on the path, is the weight coefficient of the water depth, is the distance from the path to the nearest obstacle, is the weight coefficient of the obstacle, and the degree of avoidance of the path to the obstacle The calculation formula is: , where is the minimum distance from the path to the nearest obstacle, is the weight coefficient of the distance, is the angle between the path direction and the obstacle direction, represents the influence of the angle on the degree of avoidance, is the weight coefficient of the angle. Combining them, the path fitness evaluation expression is: , where , , are the weight coefficients of length, safety, and obstacle avoidance degree respectively. Through the optimization algorithm, find the path that minimizes as the initial global path planning scheme.

[0035] Let the position of the unmanned ship at time be , and the target position be . Let the vector from the current position to the target position be , and the current speed vector of the unmanned ship be . The set of obstacles in the environment is . Define the vector from the unmanned ship to the obstacle as , the repulsive force function , where is the distance from the unmanned ship to the obstacle, is a constant, and the local path adjustment vector of the unmanned ship at time is calculated by the formula: , where , is the target attraction coefficient, is the repulsive force coefficient of the obstacle . During the navigation of the unmanned ship, it is continuously calculated according to the real-time environmental information to adjust the heading and speed of the unmanned ship to avoid suddenly appearing obstacles and cope with sudden changes in water flow.

[0036] When the unmanned ship encounters suddenly appearing dangerous obstacles and sudden changes in water flow, it quickly evaluates and adjusts the current path through the emergency path evaluation formula to avoid the dangerous area. The total path cost , where the actual cost , represents the movement cost from node to node , which is determined according to factors such as energy consumption and time cost. The estimated cost calculation formula is: , where is the Euclidean distance from the current position to the target position, calculated using the Euclidean distance formula, according to the water flow speed of and the angle between the water flow direction and the path direction of to calculate, and the calculation formula is: , where is an adjustment coefficient, which is determined by calculating the distance from the path to the nearest obstacle and the size and threat level of the obstacle. The calculation formula is: , where is the minimum distance from the path to the nearest obstacle, is the adjustment coefficient, and by recalculating , a new path is found to make the minimum to achieve path adjustment in an emergency.

[0037] Adopt wireless communication technologies such as 4G / 5G networks and satellite communication to establish communication with the unmanned ship, and real-time monitor the position, status (such as speed, heading) and survey data (including information on water flow speed and water depth) of the unmanned ship through the user interface. When it is found that the water flow speed in a certain area is too fast, send instructions to the unmanned ship according to the actual situation, such as adjusting the measurement frequency and resolution of the unmanned ship to improve the accuracy of the survey results. Let the original measurement frequency be , and the resolution be . Adjust according to the water flow speed and the preset threshold . If , adjust the measurement frequency to: , and adjust the resolution to: . Then send the adjustment instruction to the unmanned ship to execute the operation.

[0038] Collect environmental information (such as water flow speed, water depth) and historical data (survey data in similar river environments in the past), and perform preprocessing to remove noise and outliers. Select a survey parameter adjustment function to establish a relationship model between survey parameters and environmental factors. Let the comprehensive environmental information obtained by the environmental perception module be , and the current set of survey task requirement parameters be , where represents different survey task requirement parameters, and the adjusted set of survey parameters be , where represents different survey parameters. The survey parameter adjustment expression is: . Among them, is a custom function, which represents determining survey parameters according to environmental information and task requirements. is the weight coefficient, which is used to adjust the influence of environmental information and task requirement parameters on survey parameters. is a component in the comprehensive environmental information , corresponding to different environmental factors. is a parameter in the set of survey task requirement parameters . In areas with fast water flow, automatically adjust the survey parameters of the unmanned ship according to the model. For example, reduce the measurement frequency and resolution to reduce the energy consumption and operating costs of the unmanned ship. Assume the initial measurement frequency is , and the resolution is . Adjust according to the water flow speed and the preset adjustment function and . The adjusted measurement frequency is , and the resolution is , where and are determined according to the actual situation and experience. Generally speaking, the faster the water flow speed, and The smaller the value is.

[0039] Example Two:

[0040] This example introduces a remote control system for an unmanned ship used in surveying and mapping. Through autonomous environment perception, intelligent path planning, adaptive surveying and mapping parameter adjustment, and remote monitoring, it realizes efficient and safe surveying and mapping operations of the unmanned ship in complex water area environments.

[0041] The environment perception module uses a high-resolution lidar and a multispectral camera for environment perception. The lidar emits laser beams and receives reflected signals, and uses the time difference. The multispectral camera takes pictures in different spectral bands to extract spectral information on water body color, water quality, and underwater terrain features. After fusing the information obtained by the lidar and the camera, it is transmitted to other modules.

[0042] The path planning module receives the data from the environment perception module and constructs a dynamic environment model using a three-dimensional environment information fusion formula. The formula is: , This model combines the three-dimensional information of the lidar and the spectral information of the multispectral camera, calculates the comprehensive environment information through a fusion function, and based on the dynamic environment model, uses a path fitness evaluation formula for global path planning. The path fitness evaluation formula is: , where the path length The calculation formula is: , the path safety evaluation value The calculation formula is: , the degree of obstacle avoidance of the path The calculation formula is: , the path fitness considers the path length, safety, and obstacle avoidance degree. During navigation, real-time path optimization is performed through a local path adjustment formula. The formula is: , in case of an emergency, the path is quickly adjusted through an emergency situation path evaluation formula. The formula is , where the actual cost is: , the estimated cost is: , and finally calculates the optimal heading angle and speed value, and sends a control instruction to the navigation system of the unmanned ship.

[0043] The remote control module uses 4G / 5G networks and satellite communication to achieve remote communication with the unmanned ship, real-time monitors the position, status, and surveying and mapping data of the unmanned ship, and displays them through a user interface, sends instructions to the unmanned ship according to the actual situation, and performs corresponding operations.

[0044] An adaptive surveying and mapping parameter module collects environmental information and historical data, and establishes a relationship model between surveying and mapping parameters and environmental factors through a surveying and mapping parameter adjustment function. The function expression is: , when the unmanned ship enters a new area, suitable surveying and mapping parameters are output by the model according to the current environmental information.

[0045] Through the above embodiments, the remote control system of this unmanned ship can achieve autonomous environmental perception, intelligent path planning and adaptive surveying and mapping parameter adjustment in a complex water area environment, ensuring the efficiency and safety of surveying and mapping operations.

[0046] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A remote control system for an unmanned ship used for surveying and mapping, characterized in that: The system includes: an environment perception module, a path planning module, a remote control module and an adaptive mapping parameter module; The environmental perception module: uses high-resolution laser radar and multispectral camera for environmental perception. The laser radar determines the distance and position of the target object by emitting laser beams and receiving reflected signals using time difference. The multispectral camera obtains multispectral images by shooting in different spectral bands, extracts spectral information of water color, water quality and underwater terrain features, integrates the information obtained by the high-resolution laser radar and the multispectral camera, and transmits it to other modules; The path planning module receives the three-dimensional information and spectral information provided by the environment perception module, performs real-time analysis and processing, and constructs a dynamic environment model through a three-dimensional environment information fusion formula. According to the dynamic environment model, the path fitness evaluation formula is used to perform global path planning according to the starting point and end point of the mission target, and a path plan is generated. During the navigation of the unmanned ship, the local path adjustment formula is used to adjust the local path to optimize the path in real time. When the unmanned ship encounters sudden dangerous obstacles and drastic changes in water flow, the emergency path evaluation formula is used to quickly evaluate and adjust the current path to avoid dangerous areas. According to the adjusted path, the optimal heading angle and speed value are calculated, and control instructions are sent to the navigation system of the unmanned ship. After the adjustment is completed, the navigation path is continuously evaluated and optimized; The path planning module uses the path fitness evaluation formula to perform global path planning. ,in Represents a node on the path, the path length The calculation formula is: ,in, Representation Node and Euclidean distance between them, path safety evaluation value The calculation formula is: ,in, is the maximum water velocity on the path, is the weight coefficient of water velocity, is the safe water depth threshold, is the minimum water depth on the path, is the weight coefficient of water depth, is the distance from the path to the nearest obstacle, is the weight coefficient of the obstacle, the degree to which the path avoids obstacles The calculation formula is: ,That, is the minimum distance from the path to the nearest obstacle, is the distance weight coefficient, is the angle between the path direction and the obstacle direction, represents the effect of the angle on the avoidance degree, is the weight coefficient of the angle. In summary, the path fitness evaluation formula is: ,in, , , are the weight coefficients of length, safety, and obstacle avoidance, respectively; The path planning module performs local path adjustment through the local path adjustment formula. Assume that the unmanned ship is at time The location is , the target location is , let the vector from the current position to the target position be , the current velocity vector of the unmanned ship is , the obstacle set in the environment is , where obstacles The location is , define the unmanned ship to the obstacle The vector is , repulsive force function ,in, is the distance from the unmanned boat to the obstacle, is a constant, the unmanned ship is at all times The local path adjustment vector Calculated by formula, the formula is: ,in, is the target attractiveness coefficient, It is an obstacle The repulsion coefficient; The path planning module adjusts the path through the emergency path evaluation formula, and the total path cost The actual cost from the starting point to the current position and the estimated cost from the current position to the target position composition: , the actual cost The calculation formula is: ,in, Represents a slave node To Node The moving cost, estimated cost The calculation formula is: in, is the Euclidean distance from the current position to the target position, represents the distance estimation after considering the influence of water flow, represents the distance estimation after considering the influence of obstacles, is the corresponding weight coefficient; The remote control module: uses wireless communication technology such as 4G / 5G network and satellite communication to establish communication with the unmanned ship, obtain the position, status and mapping data of the unmanned ship, monitor the status information and mapping data of the unmanned ship in real time through the user interface, and send instructions to the unmanned ship and execute operations according to actual conditions; The adaptive mapping parameter module collects and pre-processes environmental information and historical data, selects a mapping parameter adjustment function to establish a mapping parameter and environmental factor relationship model, and when the unmanned ship enters a new area, it operates according to the mapping parameters output by the model based on the environmental information.

2. The remote control system for an unmanned ship for surveying and mapping according to claim 1 is characterized in that: The path planning module constructs a dynamic environment model through a three-dimensional environment information fusion formula. Suppose the three-dimensional information set obtained by the laser radar is ,in , Respectively represent the coordinate values ​​in three-dimensional space, represents the laser radar feature value corresponding to each element in the three-dimensional information set obtained by the laser radar. Suppose the spectral information set obtained by the multispectral camera is ,in , Indicated in The characteristic value under the spectral band is: ,in is the mean value of the lidar information, is the mean of the spectral information, is the mean of the joint information, is the weight coefficient.

3. The remote control system of an unmanned ship for surveying and mapping according to claim 1 is characterized in that: The path length in the path planning module By calculating the sum of the distances between adjacent nodes, is the calculation path The distance between adjacent nodes is calculated using the Euclidean distance formula, which is: , Represents the node coordinates on a two-dimensional plane.

4. The remote control system for an unmanned ship for surveying and mapping according to claim 1 is characterized in that: The path safety evaluation value in the path planning module The calculation is based on the factors of water velocity, water depth and the proximity of obstacles. is the maximum water velocity on the path, take its reciprocal , the smaller the water flow rate, the higher the safety. It is the weight coefficient of water flow velocity. It adjusts the importance of water flow velocity in safety assessment according to actual conditions and indicates water depth safety. is the weight coefficient of water depth, is the safe water depth threshold, is the minimum water depth on the path, and the ratio of the difference between the two and the safe water depth threshold is calculated. is the distance from the path to the nearest obstacle, taking its reciprocal Indicates the safety of the proximity of obstacles. is the weight coefficient of the obstacle.

5. The remote control system for unmanned ships used for surveying and mapping according to claim 1 is characterized in that: The degree to which the path in the path planning module avoids obstacles It is determined by calculating the minimum distance between the path and the obstacle and the angle between the path direction and the obstacle direction. is the minimum distance from the path to the nearest obstacle, taking its reciprocal , indicating the degree of avoidance, It is the distance is the angle between the path direction and the obstacle direction, calculate , when the path is perpendicular to the obstacle, the angle is 90°, indicating the best avoidance state. is the weight coefficient of the angle.

6. The remote control system for unmanned ships used for surveying and mapping according to claim 1 is characterized in that: The actual cost in the path planning module It is the sum of the movement costs between all adjacent nodes on the path. ,in Represents a node on the path, Represents a slave node To Node The cost of movement is determined based on energy consumption and time cost factors.

7. The remote control system for unmanned ships used for surveying and mapping according to claim 1 is characterized in that: The estimated cost in the path planning module , is obtained by adjusting the Euclidean distance, water flow influence and obstacle influence and summing them under different weights. is the Euclidean distance from the current position to the target position, calculated using the Euclidean distance formula. According to the water flow rate , the angle between the water flow direction and the path direction is To calculate, the calculation formula is: ,in is an adjustment factor, It is determined by calculating the distance from the path to the nearest obstacle and the size and threat level of the obstacle. The calculation formula is: ,in is the minimum distance from the path to the nearest obstacle, is the adjustment factor.

8. The remote control system for an unmanned ship for surveying and mapping according to claim 1 is characterized in that: The adaptive mapping parameter module selects the mapping parameter adjustment function to establish the relationship model between the mapping parameters and the environmental factors. The comprehensive environmental information obtained by the environmental perception module is , the current surveying and mapping task requirement parameter set is ,in The set of surveying and mapping parameters after adjustment represents different surveying and mapping task requirement parameters: ,in Represents different surveying and mapping parameters, and the function expression is: ,in, It is a custom function that determines the mapping parameters based on environmental information and task requirements. is the weight coefficient, which is used to adjust the impact of environmental information and task requirement parameters on surveying and mapping parameters. Comprehensive environmental information A component in corresponds to different environmental factors, Is a set of surveying and mapping task requirement parameters A parameter in .

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