Safety management intelligent system of tug
Through multi-sensor fusion technology and real-time data processing, the tug safety management system has achieved comprehensive environmental awareness and dynamic path planning, solving the problems of insufficient perception of existing systems and static paths, and improving the safety and handling performance of the tug.
Patent Information
- Application Number
- CN202510701742.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-08-22
AI Technical Summary
The existing tug safety management system has insufficient perception capabilities, cannot fully perceive the environment, the path planning is static and cannot be adjusted in real time, and the lack of effective feedback mechanisms leads to insufficient adaptability and flexibility in complex environments, increasing the risk of collision.
The multi-sensor fusion technology is adopted, combined with real-time data processing, and the environmental information around the tug is obtained through the data acquisition module. The path planning module calculates collision avoidance paths in real time, the decision and control module dynamically adjusts navigation strategies, the feedback module monitors navigation status in real time and optimizes paths and control strategies, and the execution module adjusts the speed and heading in real time.
It realizes efficient and accurate path planning and collision avoidance control in complex environments, can quickly respond to environmental changes, improve the safety and reliability of tugboats, ensure navigation stability and flexibility, and improve handling performance and navigation efficiency.
Smart Images
Figure CN120521602A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent shipping technology, and in particular to an intelligent safety management system for tugboats. Background Art
[0002] In the modern shipping industry, tugboats, as crucial equipment for guiding large vessels, shoulder the heavy responsibility of entering and exiting port waterways. The safety and efficiency of tugboats are directly linked to the safe navigation of ships and the smooth flow of waterways. However, due to the complex nature of waterways, unpredictable weather conditions, and the large number of vessels, traditional tugboat safety management systems often fail to meet the requirements for efficient collision avoidance, flexible response, and real-time adjustments.
[0003] Currently, existing tugboat safety management systems typically use single sensors, such as radar and automated information system (AIS), to monitor navigation safety. Radar can effectively detect obstacles and provide certain path planning capabilities, while AIS systems can obtain real-time information on the vessel's position, speed, and heading. Using these technologies, existing systems can provide relatively basic environmental awareness and basic collision avoidance capabilities. However, these systems' perception capabilities are limited by the use of a single sensor and are unable to cope with the diverse and changing factors in complex environments. While existing technologies can improve navigation safety to a certain extent, their ability to cope with dynamic environments remains limited.
[0004] However, existing technologies have significant shortcomings, primarily reflected in the following aspects: First, the limitations of a single sensor prevent existing systems from fully perceiving the environment surrounding the tugboat, resulting in insufficient precision in path planning and control decisions. Second, many traditional systems lack the ability to make real-time dynamic adjustments. They typically rely on static path planning and fail to fully consider the real-time obstacles and environmental changes encountered by the tugboat during navigation. Finally, existing control methods also lack an effective feedback mechanism, making it impossible to make timely adjustments based on the tugboat's actual motion state. These shortcomings significantly reduce the adaptability and flexibility of traditional systems in complex environments, increasing the potential risk of collisions. Summary of the Invention
[0005] In response to the deficiencies of the prior art, the present invention provides an intelligent safety management system for tugboats, which solves the problems of insufficient perception capability and static path planning that cannot be adjusted in real time in the prior art.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tugboat safety management intelligent system, comprising: A data acquisition module collects sensor data of the tugboat's surrounding environment, including obstacles around the tugboat, the relative position, speed, and heading of the vessel; A path planning module, connected to the data acquisition module, calculates the collision avoidance path of the tugboat based on the sensor data, and uses an optimal collision avoidance algorithm to adjust the collision avoidance path in real time in combination with current environmental information; A decision-making and control module, connected to the path planning module, generates speed, heading, and steering control instructions for the tugboat based on the collision avoidance path. The decision-making and control module adjusts the navigation strategy in real time according to the tugboat's current position, heading, and environmental changes; An execution module, connected to the decision and control module, adjusts the speed and course of the tugboat in real time based on the control instructions; The feedback module is connected to the execution module and the path planning module respectively. It is used to monitor the navigation status of the tugboat in real time through sensors and provide feedback data to the path planning module and the decision and control module. The feedback data is used to dynamically adjust the path planning and control strategies.
[0007] Preferably, the data acquisition module includes: AIS sensor, used to collect real-time information on the position, speed and heading of other vessels around the tugboat; Radar and LiDAR sensors are used to detect the relative position and distance of obstacles and other vessels around the tugboat and provide precise environmental awareness; Cameras are used to capture visual images around the tugboat, assist in identifying invisible obstacles and provide remote visual perception.
[0008] Preferably, the path planning module includes: The optimal collision avoidance path calculation unit uses optimal control theory and dynamic programming methods, combined with real-time environmental data and the tugboat's kinematic model to calculate the optimal collision avoidance path; Dynamic path adjustment unit, which dynamically adjusts the path planning based on real-time collected environmental data and the actual status of the tugboat; The path assessment unit conducts safety assessment on the planned path and eliminates dangerous areas.
[0009] Preferably, the decision and control module includes: The control strategy generation unit generates speed, heading and steering control instructions in real time based on the collision avoidance path provided by the path planning module and the tugboat's current position, speed, heading and steering conditions; Real-time adjustment unit, dynamically adjusts the control strategy according to the actual navigation status of the tugboat and changes in the surrounding environment; The navigation strategy optimization unit uses artificial intelligence algorithms for self-learning and optimization to avoid tugboat collisions.
[0010] Preferably, the execution module includes: The speed control unit receives speed instructions from the decision and control module and adjusts the tugboat's speed in real time; The heading control unit receives the heading instructions from the decision and control module and adjusts the tugboat's heading in real time; The steering control unit receives steering instructions from the decision and control module and accurately adjusts the steering rate of the tugboat.
[0011] Preferably, the feedback module includes: The status monitoring unit uses sensors to obtain the tugboat's current position, speed, heading, and deviation data in real time; Feedback data processing unit processes the tugboat status data monitored in real time and feeds the processed data back to the path planning module and decision and control module; The dynamic adjustment unit dynamically adjusts the path planning and control strategy according to the feedback data, so that the tugboat is always on the optimal collision avoidance path during navigation.
[0012] Preferably, the optimal collision avoidance path calculation unit includes: The environment modeling unit builds a navigation environment model of the tugboat based on the obstacle information and target ship information provided by the data acquisition module, providing a scenario basis for path calculation; The path generation unit generates the initial collision avoidance path based on the environmental modeling results using dynamic programming, AI algorithm and Dijkstra algorithm; The path optimization unit optimizes the generated initial path, taking into account factors such as path smoothness, energy consumption minimization, and steering constraints to ensure path feasibility and safety; The path replanning unit replans the collision avoidance path in real time when environmental information changes significantly and there is a potential collision risk.
[0013] Preferably, the navigation strategy optimization unit includes: Intelligent learning unit, based on machine learning and deep learning technologies, analyzes and models historical navigation data and continuously optimizes control strategies; Behavior prediction unit, used to predict the movement trends of surrounding target ships; The risk assessment unit conducts real-time assessment of the risk level caused by the current navigation strategy and provides risk information to the decision-making and control modules; The strategy adjustment unit dynamically adjusts the existing navigation strategy based on intelligent learning and risk assessment results.
[0014] Preferably, the steering control unit includes: The rudder angle control unit is used to accurately control the rudder angle of the tugboat steering gear according to the steering control instructions, so as to achieve fine-tuning and direction change of the tugboat; Steering rate adjustment unit, used to adjust the speed of rudder angle change during steering; The course deviation correction unit detects the deviation between the tugboat's actual course and the target course in real time and dynamically corrects the steering control instructions; The stability feedback unit monitors the dynamic stability parameters during the tugboat's steering process and helps optimize the steering control strategy.
[0015] Preferably, the feedback data processing unit includes: Data filtering unit, used to pre-process the raw sensor data and remove outliers and noise; The state analysis unit performs real-time analysis of the tugboat's position, speed, and heading parameters to extract key operational characteristics; The anomaly detection unit identifies abnormal behaviors or states during the tugboat's operation by setting thresholds or introducing anomaly detection models; The feedback optimization unit transmits the processed key feedback data to the path planning module and the decision and control module for adjusting the collision avoidance path and navigation strategy.
[0016] The present invention provides an intelligent safety management system for tugboats. It has the following beneficial effects: 1. This invention utilizes multi-sensor fusion technology, combined with real-time data processing, to accurately perceive the tugboat's surroundings, including the location, speed, and heading of other vessels, as well as the distribution of obstacles. This technical solution enables efficient and accurate path planning and collision avoidance control in complex environments. Compared to existing solutions that rely solely on a single sensor, this invention effectively addresses the issues of narrow environmental perception range and incomplete data, significantly improving the safety and reliability of tugboats.
[0017] 2. Through the close collaboration of the path planning module and the decision-making and control module, this invention dynamically adjusts the navigation path and control strategy in real time based on the tugboat's motion state and environmental changes. This technical solution enables the tugboat to rapidly respond to environmental changes based on real-time feedback, maintaining a safe course. Compared to traditional path planning systems, this invention automatically adapts to dynamic changes in the vessel's surroundings, avoiding the risk of collision caused by fixed paths or the inability to adjust them in real time.
[0018] 3. This invention incorporates a feedback module that monitors the tugboat's navigation status in real time and transmits this information to the path planning and decision-making control modules. This real-time feedback mechanism enables the system to continuously optimize path planning during navigation and adjust control strategies based on the tugboat's actual status. Compared to the existing one-way control mode, this invention solves the problem of the system's slow response to external environmental changes, resulting in more stable and flexible navigation.
[0019] 4. This invention incorporates advanced control algorithms, particularly for speed, heading, and steering control. By precisely adjusting the tugboat's power system and steering gear, it optimizes the tugboat's maneuverability. This technical solution not only ensures smooth navigation but also effectively improves navigation efficiency. Compared to traditional control methods, this invention can rapidly adjust speed and heading in diverse waters and complex conditions, resolving the issue of unstable maneuverability in dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 A schematic diagram of the system construction process of the present invention; Figure 2 This is a data acquisition module framework diagram of the present invention; Figure 3 This is a framework diagram of the path planning module of the present invention; Figure 4 This is a framework diagram of the decision-making and control module of the present invention; Figure 5 This is a framework diagram of the execution module of the present invention; Figure 6 This is a framework diagram of the feedback module of the present invention. DETAILED DESCRIPTION
[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0022] Please see the attached Figure 1 -Attached Figure 6 The embodiment of the present invention provides an intelligent safety management system for tugboats, comprising: The data acquisition module collects sensor data about the tugboat's surroundings, including obstacles around the tugboat, the relative position, speed, and heading of the vessel. As the perception unit of the intelligent tugboat safety management system, the data acquisition module's primary task is to collect critical data about the tugboat's surroundings in real time through multiple sensors. By accurately collecting information about the surrounding environment, the system provides comprehensive perception capabilities, providing crucial data support for subsequent path planning, decision-making, and control modules. The data acquisition module includes multiple sensors, such as AIS, radar, LiDAR, and cameras, covering various perception dimensions, ensuring the system's accurate and comprehensive environmental perception.
[0023] The data acquisition module is designed to take into account environmental information such as obstacles around the tugboat, the vessel's relative position, speed, and heading. By using different types of sensors, the system can dynamically acquire various environmental data and build an accurate navigation environment model.
[0024] AIS sensor: AIS sensors are primarily used to receive Automatic Identification System (AIS) signals from other vessels in the tugboat's vicinity, providing real-time information on their position, course, speed, and other dynamic data. The AIS system uses radio frequency bands to broadcast vessel position and navigation data. Therefore, tugboats can effectively obtain information such as the relative position, speed, and course of surrounding vessels through AIS sensors, enabling them to promptly assess potential collision risks.
[0025] The formula is: ; in, , and , are the relative position coordinates of the tugboat and the target vessel, respectively. This formula can be used to calculate the relative distance between the two vessels, thereby helping the path planning module calculate the collision avoidance path.
[0026] Radar Sensor: Radar sensors are used to detect obstacles and the relative positions of other vessels around the tugboat. Radar signals transmit and receive electromagnetic waves to detect the position and distance of surrounding objects. Radar's advantage lies in its ability to effectively detect targets in complex weather and low visibility environments. By continuously scanning the surrounding environment, the radar sensor acquires distance and angle information between the tugboat and obstacles and other vessels, providing real-time data for the path planning module.
[0027] Generally, the detection range and accuracy of a radar system are limited by power and antenna type. However, in this embodiment, a high-performance radar sensor is used, which can achieve relatively accurate obstacle detection and target tracking within a certain range.
[0028] LiDAR Sensor: LiDAR (Light Detection and Ranging) sensors use laser scanning technology to measure the distance to objects by emitting laser light and receiving the return signal. LiDAR sensors are characterized by their extremely high accuracy, providing detailed three-dimensional environmental data for path planning. The LiDAR sensor enables the system to obtain precise point cloud data of the tugboat's surroundings, enabling it to identify surrounding obstacles, especially small obstacles above the water surface.
[0029] In some embodiments, the LiDAR sensor is configured to scan the environment at a higher scanning frequency, providing real-time three-dimensional position data of surrounding obstacles. This data is directly fed into the path planning module to provide support for the collision avoidance algorithm.
[0030] Cameras: Camera sensors capture visual images of the tugboat's surroundings. While their performance may be limited in low-visibility environments, they are effective in capturing visual information such as floating objects, buoys, other vessels, and small obstacles on the water surface in clear weather and with good visibility. Using image processing technology, the cameras can identify objects, further enhancing the system's environmental perception.
[0031] Specifically, the camera, in conjunction with image processing algorithms, can perform real-time image segmentation, object detection and tracking, and identify static and dynamic obstacles around the tugboat, especially small objects on the hull that are difficult to detect.
[0032] In the data acquisition module, all sensor data is transmitted to the central processing system via a unified data acquisition interface. This data is processed and integrated in real time to form a comprehensive perception model of the tugboat's surroundings. This data includes not only the target vessel's position, speed, and heading, but also the distance, size, and direction of movement of potential obstacles, forming a multi-dimensional collection of environmental information. This information is then used in the subsequent path planning module to calculate the optimal collision avoidance path.
[0033] The data acquisition module dynamically monitors the tugboat's safe navigation by acquiring real-time sensor data from its surroundings. Each sensor type operates independently and transmits data to the system's central processing unit. Within the central processing unit, all sensor data is integrated and processed to form a comprehensive map of the surrounding environment. This map provides the path planning module with a basis for accurate collision avoidance path design.
[0034] In some embodiments, the data acquisition module can also enhance environmental perception accuracy by incorporating multi-sensor fusion technology, including radar and LiDAR. This fusion of multi-source data can effectively improve the reliability of obstacle detection, especially in complex environments. By integrating the characteristics of different sensors to optimize data processing algorithms, the system reduces the error of a single sensor, thereby improving the accuracy and safety of path planning.
[0035] Furthermore, to adapt to different navigation environments, the data acquisition module can flexibly switch between different sensors. For example, in conditions with good visibility, the camera can provide a good auxiliary function; in inclement weather or nighttime conditions, radar and LiDAR become the core sensors, providing the main support for environmental perception.
[0036] The path planning module is connected to the data acquisition module and calculates the tugboat's collision avoidance path based on sensor data. The path planning module uses the optimal collision avoidance algorithm and adjusts the collision avoidance path in real time based on current environmental information. The path planning module, a crucial component of the intelligent tugboat safety management system, is closely integrated with the data acquisition module. This module's primary task is to calculate the tugboat's collision avoidance path in real time based on sensor data from the data acquisition module and dynamically adjust it based on current environmental information. By employing an optimal collision avoidance algorithm, the path planning module ensures the tugboat's safe and efficient navigation in complex environments. The core function of the path planning module is to ensure that the tugboat can quickly respond and make adaptive adjustments in a changing navigation environment, effectively avoiding collision risks.
[0037] The path planning module first connects to the data acquisition module to obtain real-time information about the tugboat's surroundings. This information primarily includes the position, speed, and heading of other vessels, as well as the spatial distribution of surrounding obstacles. By processing and integrating this data, the path planning module constructs a dynamic environmental model and calculates the optimal collision avoidance path.
[0038] Environmental data input: The path planning module receives data including the distance, relative speed, direction, and location coordinates of surrounding obstacles. This information comes from AIS sensors, radar, LiDAR, and cameras, ensuring the accuracy and real-time nature of environmental data.
[0039] Optimal Collision Avoidance Algorithm: To achieve efficient and safe collision avoidance, the path planning module uses an optimal collision avoidance algorithm. Common path planning algorithms include dynamic programming, AI algorithms, Dijkstra's algorithm, and artificial potential field methods. In this embodiment, a hybrid algorithm based on dynamic programming and artificial potential field methods is used.
[0040] Dynamic Planning: This algorithm defines a starting point and a destination and calculates the shortest path from there. During path planning, the system considers environmental changes at every moment and dynamically updates the path based on sensor data. Dynamic planning allows the optimal path to be recalculated in a changing environment, adapting to changing navigation conditions.
[0041] The formula is: ; in, is the path cost function, Represents each point in the path, For arrive The cost of dynamically calculating the minimum cost path, Indicates a minimum operation.
[0042] Artificial Potential Field Method: This method treats the tugboat and obstacles as objects with attractive and repulsive forces, calculating the resultant force field and generating a path. The tugboat is attracted by the target point while avoiding the repulsive forces from the obstacle. This method is suitable for calculating collision avoidance paths in environments with multiple obstacles.
[0043] Specifically, the path planning module calculates the collision avoidance path based on the position, size, speed, etc. of the obstacle and target point, combined with the repulsive and attractive force formulas of the artificial potential field method. The obstacle repulsive force is usually calculated using the following formula: ; in, The repulsive force generated by the obstacle, is the rejection coefficient, is the distance between the tugboat and the obstacle. When the distance between the tugboat and the obstacle is close, the repulsive force increases, prompting the path planning module to adjust the collision avoidance path.
[0044] Real-time Path Adjustment: Because the tugboat's navigation environment is constantly changing, the path planning module continuously updates the route based on real-time sensor data. Whenever new data (such as changes in the vessel's position or the appearance of obstacles) is transmitted to the path planning module, the module recalculates the optimal path based on the latest data, avoiding the risk of unexpected collisions during the tugboat's navigation.
[0045] Specifically, during the path planning process, when new environmental data is input, the system first evaluates its impact on the current path. If new obstacles appear on the path, or if certain obstacles no longer exist, the path planning module dynamically adjusts the path to ensure the tugboat can still pass smoothly under the new environmental conditions.
[0046] Path Smoothing and Optimization: To ensure the tugboat's smooth navigation along the collision avoidance path, the path planning module performs path smoothing after calculating the collision avoidance path. This process aims to remove sharp turns and unnecessary deviations from the path, making the tugboat's navigation more stable and efficient. Path smoothing uses interpolation methods such as B-spline curves or spline interpolation to ensure the generated path is both safe and highly maneuverable.
[0047] In one possible implementation, path smoothing can reduce the steering angle by minimizing the curvature of the path, as follows: ; in, is the ordinate of the path point, Represents each point in the path, is the curvature of the path, Indicates the minimum operation. is the index in the path, used to indicate the position of the current point. Indicates the total number of points in the path.
[0048] The path planning module not only relies on sensor data to calculate the route in real time but also replans the route based on obstacles encountered during navigation and the vessel's dynamic behavior. Whenever the environment changes, the path planning module triggers a replanning mechanism to ensure the tugboat avoids new obstacles or vessels. Key to this process lies in the path planning module's ability to efficiently integrate multi-source data, intelligently analyze complex environments, and optimize the route.
[0049] In some embodiments, the path planning module can also be integrated with the tugboat's other control systems (such as the power system and steering system) to achieve comprehensive navigation control. For example, the path planning module not only calculates the collision avoidance path but also synchronously adjusts the speed and rudder angle to ensure smooth navigation of the tugboat along the collision avoidance path.
[0050] Furthermore, the path planning module can be integrated with the tugboat's intelligent learning system. By learning and analyzing navigation data, the path planning algorithm can be continuously optimized, enhancing the system's intelligence. For example, through machine learning algorithms, the system can learn the navigation characteristics of specific waters, predicting possible obstacles and vessel behavior in advance, thereby pre-determining a safer route.
[0051] The decision-making and control module is connected to the path planning module. It generates speed, heading, and steering control instructions for the tugboat based on the collision avoidance path. The decision-making and control module adjusts the navigation strategy in real time according to the tugboat's current position, heading, and environmental changes. The decision-making and control module, a key component of the intelligent tugboat safety management system, is closely connected to the path planning module. Its primary function is to generate real-time speed, heading, and steering control instructions for the tugboat based on the collision avoidance path generated by the path planning module. Furthermore, the decision-making and control module dynamically adjusts navigation strategies based on the tugboat's current position, heading, and environmental changes, ensuring smooth collision avoidance and stable navigation in complex navigation environments.
[0052] The decision-making and control module receives collision avoidance path data from the path planning module and further calculates the tugboat's speed, heading, and steering control instructions. This process takes into account the tugboat's actual position, current heading, and changes in the surrounding environment, ensuring real-time path adjustments and precise control of the tugboat.
[0053] Speed Control: The decision-making and control module adjusts the tugboat's speed in real time based on the collision avoidance path provided by the path planning module and the tugboat's current navigation status. Speed control is based on the tugboat's current position, the distance between the target point and the speed match, and the specific conditions of the waterway. The goal of speed control is to reduce drastic fluctuations during navigation by ensuring smooth speed changes, thereby ensuring collision avoidance and energy conservation during navigation.
[0054] Specifically, the speed adjustment formula is: ; in, is the updated speed, is the current speed, is the target speed, is the adjustment coefficient. This formula dynamically adjusts the tugboat’s speed based on the relative distance between the tugboat and the target point and the speed difference.
[0055] Heading control: Heading control is another key task within the decision and control module. Based on the collision avoidance path generated by the path planning module, the heading control module dynamically adjusts the tugboat's current position and heading. Heading adjustments are typically calculated based on the relative angle between the target point and the current position. To achieve smooth steering, the decision and control module considers the deviation between the current heading and the target heading and calculates the steering angle.
[0056] The calculation formula for heading control is: ; in, is the steering angle, is the target heading angle, is the current heading angle. If the heading angle difference is too large, the control module will appropriately reduce the adjustment rate to avoid sudden turns and unstable navigation.
[0057] Generally, the decision-making and control module dynamically adjusts the heading control strategy based on changes in current speed and heading. For example, in areas with narrow waterways or dense obstacles, the system may make smaller steering angle adjustments to maintain navigation stability and accuracy.
[0058] Steering control: Steering control is another critical function of the decision and control module, particularly when the tugboat needs to perform complex maneuvers or make turns. Based on the results of heading control calculations and the tugboat's dynamic characteristics, steering control generates real-time steering control commands, accurately guiding the tugboat along the intended path.
[0059] In steering control, the rudder angle adjustment formula is usually: ; in, is the updated rudder angle, is the current rudder angle, is the rudder angle adjustment coefficient, is the steering angle.
[0060] Steering control must take into account the tugboat's physical characteristics, such as inertia and steering response time, to ensure the system can respond promptly and execute smooth steering. Specifically, the system adjusts the rudder angle in real time and limits excessive steering to prevent the tugboat from yawing violently.
[0061] Real-time navigation strategy adjustment: The decision-making and control module not only generates control instructions based on the collision avoidance path but also continuously adjusts the navigation strategy based on the tugboat's current position and heading, as well as real-time environmental changes. Environmental changes may include vessel dynamics, the presence of obstacles, and weather conditions. The decision-making and control module can quickly respond and dynamically update navigation control instructions.
[0062] In one possible implementation, the decision-making and control module incorporates machine learning algorithms to adjust its strategy based on real-time data during navigation. For example, if the system detects a sudden approach by a vessel, the module automatically reduces speed and adjusts course to maintain a safe distance. This process relies on rapid detection and calculation of environmental changes, ensuring the tugboat maintains stable navigation under varying environmental conditions.
[0063] Real-time perception: The decision-making and control module receives data from the data acquisition module and the path planning module to obtain real-time information such as the tugboat's current position, heading, and environmental changes.
[0064] Calculating control commands: Based on the latest environmental information, the decision-making and control module generates corresponding speed, heading, and steering control commands. Each control command is generated taking into account the dynamic state of the tugboat and the real-time changes in the environment.
[0065] Command Execution and Adjustment: As the tugboat executes control commands, the decision-making and control module dynamically adjusts the control strategy based on the tugboat's actual motion state. For example, if the tugboat approaches an obstacle or another vessel, the system will automatically adjust its speed and course to avoid a collision.
[0066] In some embodiments, the decision-making and control module can also incorporate intelligent algorithms, such as deep learning and reinforcement learning, to further enhance the intelligence of navigation strategies. Through machine learning algorithms, the system can learn optimal navigation decisions from historical navigation data, enabling tugboats to select the most appropriate navigation strategy based on different navigation environments and mission requirements.
[0067] Furthermore, the decision-making and control module can be deeply integrated with other tugboat systems (such as the engine control system and steering gear control system) to achieve comprehensive automation of navigation control. By working in conjunction with the power system and steering gear system, the path planning and control module can more accurately control the tugboat's navigation, providing efficient and safe navigation.
[0068] The execution module is connected to the decision-making and control module and adjusts the tugboat's speed and course in real time based on the control instructions; As the core of the intelligent tugboat safety management system, the execution module is responsible for translating control commands generated by the decision-making and control module into actual navigation operations. Specifically, the execution module connects to the decision-making and control module and, based on the speed, heading, and steering control commands provided by the latter, adjusts the tugboat's speed and heading in real time, ensuring safe navigation and stable operation in complex environments. Through real-time feedback and adjustments, the execution module ensures that the tugboat accurately follows instructions, effectively avoiding obstacles and ensuring smooth navigation.
[0069] The execution module receives speed, heading, and steering commands from the decision and control module and adjusts the tugboat's navigational state in real time based on these commands, ensuring that the tugboat's behavior conforms to the predetermined control strategy. The execution module's work involves two major control aspects: speed control and heading control. These adjustments work together to ensure that the tugboat follows the optimal navigation path.
[0070] Speed Adjustment: The execution module adjusts the tugboat's engine power output and propulsion system operating status in real time based on speed commands transmitted by the decision and control module. By precisely controlling the tugboat's speed, the execution module ensures it maintains an appropriate speed in various navigational conditions, avoiding sudden acceleration or deceleration and improving navigation stability.
[0071] During the speed adjustment process, the system will control the engine power output and throttle adjustment to ensure that the tugboat can accelerate or decelerate smoothly. Specifically, the speed control is calculated by the following formula: ; in, is the rate of change of ship speed, is the speed adjustment coefficient, is the target speed, is the current speed. Through this formula, the system can quickly adjust the tugboat's propulsion system output according to the speed deviation, so that the tugboat can smoothly transition to the target speed.
[0072] Generally, speed adjustments take into account the relative speeds of the tugboat and other vessels, as well as the specific conditions of the waterway. For example, in narrow waters, the system will appropriately reduce speed to ensure navigation safety; in open waters, the system can appropriately increase speed to improve navigation efficiency.
[0073] Heading control: Heading control is another key task of the execution module. Based on the heading control instructions transmitted by the decision and control module, the execution module adjusts the rudder angle through the steering gear system, thereby changing the tugboat's heading. The system calculates the required steering angle in real time and controls the steering gear to execute the angle change.
[0074] The implementation process of heading control involves precise adjustment of the rudder angle. The specific rudder angle adjustment formula is: ; in, is the updated rudder angle, is the current rudder angle, is the target heading angle, is the current heading angle, is the steering adjustment coefficient. Based on the heading deviation, the system precisely adjusts the rudder angle to steer the tugboat towards the target heading.
[0075] Specifically, the execution module adjusts the steering strategy based on the tugboat's actual motion. For example, when the tugboat approaches an obstacle or another vessel, the system dynamically adjusts the rudder angle to avoid instability caused by oversteering. Through real-time feedback control, the execution module ensures smooth and safe course adjustments.
[0076] Real-time feedback and adjustments: The execution module not only operates according to fixed control instructions but also makes dynamic adjustments based on the tugboat's real-time status and feedback. For example, the tugboat's speed and heading may change during navigation. The execution module monitors the tugboat's current position and heading deviation to adjust the rudder angle and propulsion system output in a timely manner.
[0077] In one possible implementation, the execution module is equipped with a real-time feedback mechanism. This mechanism continuously monitors the tugboat's motion and fine-tunes control commands based on actual navigation conditions, ensuring the tugboat remains on a predetermined safe path. For example, in adverse weather conditions, the system automatically reduces steering angles to prevent violent swaying of the hull. Furthermore, in strong winds or currents, the system adjusts speed based on these external factors to maintain stable navigation.
[0078] Receiving instructions: The execution module receives speed, heading and steering control instructions from the decision and control module.
[0079] Adjusting the speed: The execution module adjusts the speed by controlling the tugboat's propulsion system according to the speed control instruction to ensure that the tugboat runs smoothly at the target speed.
[0080] Adjusting the course: The execution module adjusts the rudder angle through the steering gear system according to the course control instruction, changes the course of the tugboat, and ensures that the tugboat travels in the target direction.
[0081] Real-time feedback and correction: The execution module continuously monitors the tugboat’s navigation status and promptly adjusts control instructions based on real-time feedback to ensure the tugboat always maintains safe and stable navigation.
[0082] In some embodiments, the execution module can be deeply integrated with the tugboat's power system, navigation system, and other control modules. For example, the execution module can not only adjust the speed and heading in real time, but also work in conjunction with the power system to adjust engine output and steering gear response speed to meet the needs of rapid steering or emergency stops in special circumstances.
[0083] Furthermore, the intelligence level of the execution module can be further enhanced through machine learning and adaptive control algorithms. By learning from the tugboat's historical navigation data, the system can continuously optimize the speed and course control strategy, improving navigation efficiency and safety.
[0084] The feedback module is connected to the execution module and the path planning module respectively. It is used to monitor the navigation status of the tugboat in real time through sensors and provide feedback data to the path planning module and the decision and control module. The feedback data is used to dynamically adjust the path planning and control strategies; The feedback module, a crucial component of the intelligent tugboat safety management system, is connected to both the execution module and the path planning module. Its primary function is to monitor the tugboat's navigation status in real time through sensors, obtain real-time feedback from the tugboat, and transmit this feedback data to the path planning module and the decision-making and control module, enabling dynamic adjustments to the path planning and control strategies based on the tugboat's actual navigation conditions. The feedback module ensures that the system can promptly respond to changes in the environment and navigation conditions during navigation, optimizing the tugboat's collision avoidance path and navigation control strategy, thereby improving the tugboat's navigation safety and efficiency.
[0085] The core function of the feedback module is to monitor the tugboat's navigation status in real time and provide feedback to the path planning module and the decision and control module. This module utilizes a series of sensors (such as position sensors, speed sensors, and heading sensors) to obtain information about the tugboat's motion, including speed, heading, yaw, and position. The feedback module also monitors the tugboat's rudder angle, the operating status of the propulsion system, and other key control parameters. All this feedback information is used to adjust the path planning and control strategies to ensure the tugboat maintains stability and safety during navigation.
[0086] Connection with the Execution Module: The feedback module is closely connected to the execution module, providing real-time information on the tugboat's actual navigation status. The execution module adjusts the tugboat's speed, heading, and steering according to control commands, while the feedback module uses monitoring sensors to obtain real-time status data from the tugboat and verify the execution module's control effectiveness. Through its connection to the execution module, the feedback module achieves a closed-loop feedback loop between navigation status and control commands.
[0087] Specifically, the feedback module collects data such as speed, heading, steering angle, and rudder angle, which it then compares with the execution module to calculate the execution error of the current control command. If the feedback data deviates from the expected target, the feedback module transmits this information to the decision and control module, which dynamically adjusts the control strategy. For example, if the heading deviation is too large, the feedback module can immediately transmit this feedback information to the decision and control module, triggering a new heading adjustment command.
[0088] In one possible implementation, the navigation status feedback formula is: ; in, is the actual heading deviation, is the current heading angle, is the target heading angle. Based on this feedback information, the execution module can adjust the heading control strategy to reduce the error and ensure that the tugboat sails along the planned path.
[0089] Connection to the Path Planning Module: The feedback module also connects to the path planning module, feeding the tugboat's real-time navigation status data back to the path planning module to optimize the calculation of the collision avoidance path. The path planning module adjusts the path planning strategy based on this feedback data, ensuring that the tugboat always plans the optimal path based on the current environment and navigation status.
[0090] In this embodiment, the path planning module dynamically adjusts the collision avoidance path based on real-time data provided by the feedback module, such as the tugboat's yaw angle and position deviation. For example, if the tugboat deviates from its planned route, the feedback module will provide the deviation amount. The path planning module will then recalculate the collision avoidance path based on this feedback information to ensure the tugboat returns to a safe navigation route.
[0091] Specifically, the adjustment formula of the path planning module is: ; in, is the adjusted path, is the current path, is the path deviation, is the adjustment coefficient. Through real-time path adjustment, the path planning module can optimize the path to adapt to the dynamic motion state of the tugboat and changes in the surrounding environment.
[0092] Dynamically Adjusting Path Planning and Control Strategies: Real-time feedback from the feedback module not only corrects the navigation control of the execution module but also significantly influences the decision-making process of the path planning module. By monitoring the tugboat's navigation status, the feedback module can effectively identify potential navigation issues such as course drift, speed anomalies, and approaching obstacles. Based on this data, the path planning module can make timely adjustments to the path plan, and the decision and control module can also adjust the speed, heading, and steering strategy.
[0093] Typically, the feedback module continuously collects information about the tugboat's status, forming a closed loop with the path planning and decision-making and control modules. Whenever the feedback module detects a change in navigation status, the system automatically adjusts the path and control strategy to ensure the tugboat maintains safe navigation in any complex environment.
[0094] In one possible implementation, the feedback module's dynamic adjustment mechanism includes an error-based feedback control algorithm, such as PID control (proportional-integral-derivative control). This algorithm can dynamically adjust path planning and control strategies based on errors in feedback data, reducing deviations and optimizing the navigation path.
[0095] In some embodiments, the feedback information from the feedback module can be further enriched to include stability parameters of the tugboat, such as roll, pitch, etc. These parameters can help the path planning module make more precise path adjustments and avoid instability caused by oversteering or speed changes of the vessel.
[0096] Furthermore, the feedback module can be integrated with the tugboat's environmental perception system, enhancing the system's intelligence through the fusion of multi-dimensional sensor data. For example, by combining radar, LiDAR, and AIS data, the feedback module can comprehensively assess the tugboat's trajectory, providing more accurate feedback and improving the system's path adjustment capabilities.
[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. An intelligent safety management system for tugboats, characterized by: include: A data acquisition module collects sensor data of the tugboat's surrounding environment, including obstacles around the tugboat, the relative position, speed, and heading of the vessel; A path planning module, connected to the data acquisition module, calculates the collision avoidance path of the tugboat based on the sensor data, and uses an optimal collision avoidance algorithm to adjust the collision avoidance path in real time in combination with current environmental information; A decision-making and control module, connected to the path planning module, generates speed, heading, and steering control instructions for the tugboat based on the collision avoidance path. The decision-making and control module adjusts the navigation strategy in real time according to the tugboat's current position, heading, and environmental changes; An execution module, connected to the decision and control module, adjusts the speed and course of the tugboat in real time based on the control instructions; The feedback module is connected to the execution module and the path planning module respectively. It is used to monitor the navigation status of the tugboat in real time through sensors and provide feedback data to the path planning module and the decision and control module. The feedback data is used to dynamically adjust the path planning and control strategies.
2. The intelligent safety management system for tugboats according to claim 1, characterized in that: The data acquisition module includes: AIS sensor, used to collect real-time information on the position, speed and heading of other vessels around the tugboat; Radar and LiDAR sensors are used to detect the relative position and distance of obstacles and other vessels around the tugboat and provide precise environmental awareness; Cameras are used to capture visual images around the tugboat, assist in identifying invisible obstacles and provide remote visual perception.
3. The intelligent safety management system for tugboats according to claim 1, characterized in that: The path planning module includes: The optimal collision avoidance path calculation unit uses optimal control theory and dynamic programming methods, combined with real-time environmental data and the tugboat's kinematic model to calculate the optimal collision avoidance path; Dynamic path adjustment unit, which dynamically adjusts the path planning based on real-time collected environmental data and the actual status of the tugboat; The path assessment unit conducts safety assessment on the planned path and eliminates dangerous areas.
4. The intelligent safety management system for tugboats according to claim 1, characterized in that: The decision and control module includes: The control strategy generation unit generates speed, heading and steering control instructions in real time based on the collision avoidance path provided by the path planning module and the tugboat's current position, speed, heading and steering conditions; Real-time adjustment unit, dynamically adjusts the control strategy according to the actual navigation status of the tugboat and changes in the surrounding environment; The navigation strategy optimization unit uses artificial intelligence algorithms for self-learning and optimization to avoid tugboat collisions.
5. The intelligent safety management system for tugboats according to claim 1, characterized in that: The execution module includes: The speed control unit receives speed instructions from the decision and control module and adjusts the tugboat's speed in real time; The heading control unit receives the heading instructions from the decision and control module and adjusts the tugboat's heading in real time; The steering control unit receives steering instructions from the decision and control module and accurately adjusts the steering rate of the tugboat.
6. The intelligent safety management system for tugboats according to claim 1, characterized in that: The feedback module includes: The status monitoring unit uses sensors to obtain the tugboat's current position, speed, heading, and deviation data in real time; Feedback data processing unit processes the tugboat status data monitored in real time and feeds the processed data back to the path planning module and decision and control module; The dynamic adjustment unit dynamically adjusts the path planning and control strategy according to the feedback data, so that the tugboat is always on the optimal collision avoidance path during navigation.
7. The intelligent safety management system for tugboats according to claim 3, characterized in that: The optimal collision avoidance path calculation unit includes: The environment modeling unit builds a navigation environment model of the tugboat based on the obstacle information and target ship information provided by the data acquisition module, providing a scenario basis for path calculation; The path generation unit generates the initial collision avoidance path based on the environmental modeling results using dynamic programming, AI algorithm and Dijkstra algorithm; The path optimization unit optimizes the generated initial path, taking into account factors such as path smoothness, energy consumption minimization, and steering constraints to ensure path feasibility and safety; The path replanning unit replans the collision avoidance path in real time when environmental information changes significantly and there is a potential collision risk.
8. The intelligent safety management system for tugboats according to claim 4, characterized in that: The navigation strategy optimization unit includes: Intelligent learning unit, based on machine learning and deep learning technologies, analyzes and models historical navigation data and continuously optimizes control strategies; Behavior prediction unit, used to predict the movement trends of surrounding target ships; The risk assessment unit conducts real-time assessment of the risk level caused by the current navigation strategy and provides risk information to the decision-making and control modules; The strategy adjustment unit dynamically adjusts the existing navigation strategy based on intelligent learning and risk assessment results.
9. The intelligent safety management system for tugboats according to claim 5, characterized in that: The steering control unit comprises: The rudder angle control unit is used to accurately control the rudder angle of the tugboat steering gear according to the steering control instructions, so as to achieve fine-tuning and direction change of the tugboat; Steering rate adjustment unit, used to adjust the speed of rudder angle change during steering; The course deviation correction unit detects the deviation between the tugboat's actual course and the target course in real time and dynamically corrects the steering control instructions; The stability feedback unit monitors the dynamic stability parameters during the tugboat's steering process and helps optimize the steering control strategy.
10. The intelligent safety management system for tugboats according to claim 6, characterized in that: The feedback data processing unit includes: Data filtering unit, used to pre-process the raw sensor data and remove outliers and noise; The state analysis unit performs real-time analysis of the tugboat's position, speed, and heading parameters to extract key operational characteristics; The anomaly detection unit identifies abnormal behaviors or states during the tugboat's operation by setting thresholds or introducing anomaly detection models; The feedback optimization unit transmits the processed key feedback data to the path planning module and the decision and control module for adjusting the collision avoidance path and navigation strategy.