Intelligent tug control system design method based on autonomous navigation accompanying technology

Abstract

The invention discloses a design method of an intelligent tug control system based on an autonomous navigation technology. The system comprises an autonomous navigation module, an environment sensing module, a control decision module, a communication module and a control execution module. The autonomous navigation module receives satellite signals and ship dynamic data to realize accurate positioning and path planning of the tug; the environment sensing module monitors the surrounding environment and obstacles in real time by using devices such as a GPS, a radar, a sonar, a camera and a LiDAR; the control decision module processes and analyzes the environmental data, realizes obstacle identification and tracking, and generates an optimal control strategy; the communication module supports data communication between the tug and a shore-based control center, and remote monitoring and intervention are achieved; and the control execution module automatically generates a control instruction according to a decision result, and controls the propeller and the steering engine to realize autonomous navigation and obstacle avoidance of the tug. According to the invention, the tug operation efficiency and safety are improved, the energy consumption is reduced, the modular design is adopted, and the system is convenient to maintain and upgrade.

Description

Technical Field

[0001] The present invention relates to the field of ship automatic control systems, and more specifically to a design method for an intelligent tugboat control system based on autonomous navigation technology. Background Art

[0002] With the rapid development of the global shipping industry, the requirements for ship safety, economy and environmental protection are increasing. With the expansion of global trade, the demand for efficient and safe tugboat services is growing. Users' expectations for tugboat services are also getting higher and higher, including faster response speed, higher operational accuracy and better service quality. At the same time, regulatory agencies such as the International Maritime Organization (IMO) have proposed stricter regulations and standards for ship automation and autonomous operation. These changes have promoted the research and development and application of autonomous escort technology to meet the safety and efficiency needs of the new era.

[0003] The operation of traditional tugboats mainly relies on manual labor, which is complicated and inefficient, especially in bad weather or complex water environments, which poses a great safety hazard. With the continuous advancement of autonomous navigation technology, the realization of automated operation of tugboats has become possible. Therefore, the development of an intelligent control system that can provide autonomous navigation in complex navigation environments is of great significance to improving the operating efficiency and safety of tugboats. Summary of the invention

[0004] In order to solve the problems existing in the prior art, a design method for an intelligent tugboat control system based on autonomous escort technology is provided. The specific scheme is as follows:

[0005] A design method for an intelligent tugboat control system based on autonomous escort technology, the intelligent tugboat control system comprises an autonomous navigation module, an environmental perception module, a control decision module, a communication module and a control execution module; the autonomous navigation module realizes accurate positioning and path planning of the tugboat by receiving satellite signals and combining dynamic data of the ship; the environmental perception module monitors the surrounding environment and obstacles in real time through sensors installed on the tugboat; the control decision module realizes obstacle recognition and tracking by processing and analyzing data collected by the environmental perception module; the communication module supports data communication between tugboats and between tugboats and shore-based control centers to realize remote monitoring and control; the control execution module automatically generates control instructions according to the analysis results of the control decision module, and realizes automatic navigation and obstacle avoidance of the tugboat through actuators such as steering gears and thrusters.

[0006] Furthermore, the design method comprises the following steps:

[0007] S1: Install environmental sensing equipment on the tugboat;

[0008] S2: Send the data obtained by the environmental perception device to the autonomous navigation module and the environmental perception module in real time;

[0009] S3: The control decision module receives this data and uses deep learning algorithms to analyze it to develop the optimal navigation route and control strategy.

[0010] S4: The communication module also maintains communication with the shore-based control center to enable remote monitoring and intervention when necessary.

[0011] S5: The communication module sends the command of the control decision module to the control execution module, and the control execution module controls the engine and steering gear of the tugboat to achieve automatic navigation.

[0012] Furthermore, the perception devices in S1 include GPS, radar, sonar, camera, and LiDAR.

[0013] Furthermore, the data processing flow of the control decision module includes: data acquisition, preprocessing, feature extraction, obstacle recognition and tracking.

[0014] Furthermore, data preprocessing includes filtering, denoising and coordinate conversion of data from GPS, radar, sonar, camera and LiDAR sensors. The positioning accuracy of GPS signals reaches ±2 meters, the detection distance of radar is more than 1 km, the effective detection depth of sonar is 200 meters, and the resolution of LiDAR is 0.2 degrees.

[0015] Furthermore, feature extraction includes feature analysis of dynamic and static objects in the environment, and the use of deep learning algorithms to model the shape, speed, and motion trajectory of objects; the LiDAR scanning frequency is above 10Hz, the camera image frame rate is 30 frames per second, and the radar scanning angle is 360 degrees.

[0016] Furthermore, obstacle recognition is achieved through a convolutional neural network (CNN) model, which classifies and processes sensor data to identify the type, size and orientation of obstacles; among them, the recognition accuracy reaches more than 95% and the system response time does not exceed 100 milliseconds.

[0017] Furthermore, the tracking module tracks the identified obstacles in real time and predicts the future position of the obstacles through the Kalman filter algorithm. The tracking error is less than ±1 meter, and the system updates the obstacle position no less than 5 times per second.

[0018] Beneficial effects:

[0019] The present invention provides a design method for an intelligent tugboat control system based on autonomous escort technology, which has the following advantages:

[0020] (1) By introducing autonomous navigation technology and reducing reliance on manual operation, tugboats can achieve autonomous navigation and decision-making, effectively improving operation efficiency, especially in complex waters or severe weather conditions, and can operate stably.

[0021] (2) The system's environmental perception module uses multiple sensors such as GPS, radar, sonar, camera and LiDAR to monitor the surrounding environment in real time, automatically identify and track obstacles, and achieve intelligent obstacle avoidance, greatly reducing the risk of collision and ensuring the safety of tugboat operations.

[0022] (3) By optimizing the navigation route through deep learning algorithms, the system can effectively reduce unnecessary energy consumption and shorten the tugboat’s travel distance and time, thereby reducing fuel consumption, reducing carbon emissions, and being more environmentally friendly.

[0023] (4) The system adopts a modular design to facilitate the independent maintenance, updating and upgrading of each functional module, so that the system can adapt to future technological developments and new regulatory requirements, and improve the flexibility and scalability of the system.

[0024] (5) The environmental perception equipment of this system has high-precision detection capabilities. The GPS positioning accuracy is ±2 meters, the LiDAR resolution is 0.2 degrees, the obstacle recognition accuracy is more than 95%, and the system response time does not exceed 100 milliseconds, ensuring that the tugboat can operate safely and smoothly under the premise of high precision and rapid response. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a general structure diagram of an intelligent tugboat control system based on autonomous escort technology.

[0026] Figure 2 This is the layout diagram of the environment perception module.

[0027] Figure 3 It is the data processing flow chart of the control decision module. DETAILED DESCRIPTION

[0028] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0029] Example:

[0030] Figure 1 It is a general structure diagram of an intelligent tugboat control system based on autonomous escort technology. Figure 1As shown, the intelligent tugboat control system includes an autonomous navigation module, an environmental perception module, a control decision module, a communication module and a control execution module; the autonomous navigation module realizes the precise positioning and path planning of the tugboat by receiving satellite signals and combining the dynamic data of the ship; the environmental perception module monitors the surrounding environment and obstacles in real time through sensors installed on the tugboat; the control decision module realizes the identification and tracking of obstacles by processing and analyzing the data collected by the environmental perception module; the communication module supports data communication between tugboats and tugboats, and between tugboats and shore-based control centers, and realizes remote monitoring and control; the control execution module automatically generates control instructions according to the analysis results of the control decision module, and realizes automatic navigation and obstacle avoidance of the tugboat through actuators such as steering gears and thrusters.

[0031] Figure 2 is the layout diagram of the environment perception module. Figure 2 It can be seen that the environmental perception module includes sensors, cameras and LIDAR devices, among which the sensors include GPS, radar, sonar, etc.

[0032] A design method of an intelligent tugboat control system based on autonomous escort technology includes the following steps:

[0033] S1: Install environmental sensing equipment on the tugboat;

[0034] S2: Send the data obtained by the environmental perception device to the autonomous navigation module and the environmental perception module in real time;

[0035] S3: The control decision module receives this data and uses deep learning algorithms to analyze it to develop the optimal navigation route and control strategy.

[0036] S4: The communication module also maintains communication with the shore-based control center to enable remote monitoring and intervention when necessary.

[0037] S5: The communication module sends the command of the control decision module to the control execution module, and the control execution module controls the engine and steering gear of the tugboat to achieve automatic navigation.

[0038] Figure 3 It is the data processing flow chart of the control decision module, such as Figure 3 As shown, the data processing flow includes: data acquisition, preprocessing, feature extraction, obstacle recognition and tracking.

[0039] Data preprocessing includes filtering, denoising and coordinate conversion of data from GPS, radar, sonar, camera and LiDAR sensors. The positioning accuracy of GPS signals reaches ±2 meters, the detection distance of radar is more than 1 kilometer, the effective detection depth of sonar is 200 meters, and the resolution of LiDAR is 0.2 degrees.

[0040] Feature extraction includes feature analysis of dynamic and static objects in the environment, and the use of deep learning algorithms to model the shape, speed, and motion trajectory of objects; the LiDAR scanning frequency is above 10Hz, the camera image frame rate is 30 frames per second, and the radar scanning angle is 360 degrees.

[0041] Obstacle recognition is achieved through a convolutional neural network model, which classifies and processes sensor data to identify the type, size and direction of obstacles. The recognition accuracy reaches more than 95% and the system response time does not exceed 100 milliseconds.

[0042] The tracking module tracks the identified obstacles in real time and predicts the future position of the obstacles through the Kalman filter algorithm. The tracking error is less than ±1 meter, and the system updates the obstacle position no less than 5 times per second.

[0043] This embodiment significantly improves the operating efficiency of the tugboat through autonomous escort technology, reduces dependence on manual operation, enables the tugboat to autonomously perform precise positioning, path planning and environmental perception, and effectively realizes automatic navigation and obstacle avoidance functions. The system uses multi-sensor data fusion, including GPS, radar, sonar, camera and LiDAR, to monitor the surrounding environment in real time, automatically identify and track obstacles, reduce the risk of collision, and enhance navigation safety. In addition, the system optimizes the navigation route through deep learning algorithms, reduces unnecessary energy consumption, reduces fuel consumption and carbon emissions, and is more environmentally friendly. The system adopts a modular design, which is easy to maintain and upgrade, ensuring good scalability and flexibility. At the same time, the system has high-precision environmental perception and rapid response capabilities, enabling the tugboat to operate safely, stably and efficiently in complex waters.

[0044] As a further improvement, the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A design method for an intelligent tugboat control system based on autonomous escort technology, characterized in that: The intelligent tugboat control system includes an autonomous navigation module, an environmental perception module, a control decision module, a communication module and a control execution module; the autonomous navigation module realizes the precise positioning and path planning of the tugboat by receiving satellite signals and combining the dynamic data of the ship; the environmental perception module monitors the surrounding environment and obstacles in real time through sensors installed on the tugboat; the control decision module realizes the identification and tracking of obstacles by processing and analyzing the data collected by the environmental perception module; the communication module supports data communication between tugboats and between tugboats and shore-based control centers to realize remote monitoring and control; the control execution module automatically generates control instructions according to the analysis results of the control decision module, and realizes automatic navigation and obstacle avoidance of the tugboat through actuators such as steering gears and thrusters.

2. According to claim 1, a design method for an intelligent tugboat control system based on autonomous escort technology is characterized in that: The design method comprises the following steps: S1: Install environmental sensing equipment on the tugboat; S2: Send the data obtained by the environmental perception device to the autonomous navigation module and the environmental perception module in real time; S3: The control decision module receives this data and uses deep learning algorithms to analyze it to develop the optimal navigation route and control strategy. S4: The communication module also maintains communication with the shore-based control center to enable remote monitoring and intervention when necessary. S5: The communication module sends the command of the control decision module to the control execution module, and the control execution module controls the engine and steering gear of the tugboat to achieve automatic navigation.

3. According to claim 2, a design method for an intelligent tugboat control system based on autonomous escort technology is characterized in that: The perception devices in S1 include GPS, radar, sonar, camera, and LiDAR.

4. According to claim 1, a design method for an intelligent tugboat control system based on autonomous escort technology is characterized in that: The data processing flow of the control decision module includes: data acquisition, preprocessing, feature extraction, obstacle recognition and tracking.

5. According to claim 1, a design method for an intelligent tugboat control system based on autonomous escort technology is characterized in that: Data preprocessing includes filtering, denoising and coordinate conversion of data from GPS, radar, sonar, camera and LiDAR sensors. The positioning accuracy of GPS signals reaches ±2 meters, the detection distance of radar is more than 1 km, the effective detection depth of sonar is 200 meters, and the resolution of LiDAR is 0.2 degrees.

6. The design method of an intelligent tugboat control system based on autonomous navigation technology according to claim 4 is characterized in that: Feature extraction includes feature analysis of dynamic and static objects in the environment, and the use of deep learning algorithms to model the shape, speed, and motion trajectory of objects; the LiDAR scanning frequency is above 10Hz, the camera image frame rate is 30 frames per second, and the radar scanning angle is 360 degrees.

7. The design method of an intelligent tugboat control system based on autonomous escort technology according to claim 4 is characterized in that: Obstacle recognition is achieved through a convolutional neural network model, which classifies and processes sensor data to identify the type, size and orientation of obstacles; the recognition accuracy reaches more than 95% and the system response time does not exceed 100 milliseconds.

8. The method for designing an intelligent tugboat control system based on autonomous navigation technology according to claim 4 is characterized in that: The tracking module tracks the identified obstacles in real time and predicts the future position of the obstacles through the Kalman filter algorithm. The tracking error is less than ±1 meter, and the system updates the obstacle position no less than 5 times per second.

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