Unmanned ship path planning and collision avoidance decision-making method and system based on multi-factor fusion

Abstract

The present invention discloses a method and system for unmanned ship path planning and collision avoidance decision-making based on multi-factor fusion. The method comprises step S1: constructing a multi-sensor fusion perception network for the unmanned ship, collecting environmental data in real time, and capturing mechanical parameters at the moment of collision on the unmanned ship, so that the unmanned ship can perceive and identify the severity of the collision; step S2: defining a path tracking excitation function and adaptively adjusting the dynamic parameters required in the path control process; step S3: setting an incentive mechanism for collision avoidance of static and dynamic obstacles, and evaluating the threat level of static and dynamic obstacles; and step S4: integrating the path tracking response, static and dynamic obstacle collision avoidance incentive mechanism, so that the unmanned ship can balance the relationship between different mission objectives and make decisions on path planning and collision avoidance under multiple tasks. The technical solution provided by the present invention effectively solves the decision-making problem of unmanned ship path planning and obstacle avoidance.

Description

Technical Field

[0001] The present invention relates to the field of autonomous navigation control and path planning of unmanned surface vessels, and in particular to a method and system for unmanned vessel path planning and collision avoidance decision-making based on multi-factor fusion. Background Art

[0002] With the rapid development of science and technology, unmanned vessels are increasingly being used in the maritime sector. In the military, unmanned vessels can perform reconnaissance, surveillance, anti-submarine warfare, and anti-mine warfare missions. They can replace manned vessels in dangerous waters, reducing the risk of casualties and enhancing operational effectiveness and strategic deterrence. In the civilian sector, unmanned vessels demonstrate tremendous potential in marine resource exploration, environmental monitoring, maritime rescue, and shipping assistance. During maritime rescue missions, unmanned vessels can quickly reach the scene of an incident, deliver relief supplies, or provide information support to rescuers, thereby improving rescue efficiency.

[0003] Reinforcement learning is an important technical means to achieve autonomous navigation for unmanned vessels. Unmanned vessels can continuously learn and optimize their behavior strategies based on environmental feedback to achieve specific goals. However, in practical applications, the reinforcement learning process faces many problems. For example, they lack adaptability to complex marine environments, which are highly complex and dynamic, including variable sea conditions (such as waves, currents, wind direction and speed), diverse geographical regions (such as ports, narrow waterways, and open ocean), and various types of obstacles (static islands, reefs, buoys, and dynamic other ships). Traditional reinforcement learning methods often use fixed parameters and simple calculation models, making them difficult to adapt to such complex and changing environments. They also have difficulty coordinating multiple mission objectives. In actual operation, unmanned vessels often need to simultaneously consider multiple mission objectives, such as accurate path tracking, efficient navigation speed, safe obstacle avoidance, and meeting mission-specific requirements (such as rapid response in rescue missions and data accuracy in environmental monitoring missions). Traditional methods struggle to find a reasonable balance between these interrelated and potentially conflicting objectives. For example, the pursuit of rapid path tracking may overlook potential obstacle risks, or over-emphasizing collision avoidance may lead to a significant reduction in navigation efficiency and failure to complete the mission on time. They also lack dynamic adjustment capabilities. The environment and mission requirements faced by unmanned vessels during navigation are constantly changing, and traditional methods are unable to respond to these changes in a timely manner. For example, when encountering sudden severe weather or temporary high-priority tasks, traditional methods cannot automatically adjust the incentive calculation strategy, making it impossible for the unmanned ship to respond flexibly, which may lead to mission failure or threaten navigation safety. Summary of the Invention

[0004] In response to the shortcomings of related technologies, the present invention provides an unmanned ship path planning and collision avoidance decision-making method and system based on multi-factor fusion, which improves the autonomous navigation performance of unmanned ships, enables them to efficiently and safely perform diversified tasks in complex and changeable marine environments, and improves and optimizes the traditional reinforcement learning process to overcome the limitations of existing technologies.

[0005] The technical solution is as follows:

[0006] A method for unmanned vessel path planning and collision avoidance decision-making based on multi-factor fusion includes the following steps:

[0007] Step S1: Build a multi-sensor fusion perception network for the unmanned vessel, integrating GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture the mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision.

[0008] Step S2: Setting an incentive mechanism for the unmanned ship path tracking response, simulating the navigation process of the unmanned ship under various path deviation conditions, defining the path tracking incentive function, and adaptively adjusting the dynamic parameters required in the path control process;

[0009] Step S3: Set up incentive mechanisms for static and dynamic obstacle avoidance. Use regression analysis and machine learning to determine incentive functions for static and dynamic obstacle avoidance. By continuously optimizing the sensor data processing algorithm and incentive function calculation model, evaluate the threat levels of static and dynamic obstacles.

[0010] Step S4: Integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse their respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make path planning and collision avoidance decisions under multiple tasks.

[0011] A multi-factor fusion-based unmanned vessel path planning and collision avoidance decision-making system, which is used to implement the multi-factor fusion-based unmanned vessel path planning and collision avoidance decision-making method, comprising:

[0012] The unmanned vessel's multi-sensor fusion perception network module integrates GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture the mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision.

[0013] The unmanned vessel path tracking response excitation module is used to simulate the navigation process of the unmanned vessel under various path deviation conditions, define the path tracking excitation function, and adaptively adjust the dynamic parameters required in the path control process;

[0014] The unmanned vessel static and dynamic obstacle collision avoidance incentive module uses regression analysis and machine learning to determine the static obstacle avoidance incentive function. It then evaluates the threat level of static and dynamic obstacles by continuously optimizing the sensor data processing algorithm and incentive function calculation model.

[0015] The unmanned ship incentive integration module is used to integrate path tracking response, static obstacle avoidance incentive mechanisms and dynamic obstacle avoidance incentive mechanisms, fusing their respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make path planning and collision avoidance decisions under multiple tasks.

[0016] The present invention has the following beneficial effects:

[0017] The present invention provides an unmanned ship path planning and collision avoidance decision-making method and system based on multi-factor fusion. Taking into account the game situation of executing path planning and obstacle avoidance during the driving process of the unmanned ship, it creatively proposes to integrate the path tracking response, static obstacle and dynamic obstacle collision avoidance incentive mechanism into an organic whole. In this process, the path tracking incentive is deeply studied, and multivariate functions and dynamic adaptation parameters are integrated; static obstacle collision avoidance incentive is optimized, and distance and speed perception and multi-dimensional environmental factors are integrated; dynamic obstacle collision avoidance incentive is comprehensively advanced, and COLREGs rules are deeply integrated and dynamic parameters and situational perception factors are intelligently controlled; total incentive calculation is systematically optimized, multiple factors are integrated and task-oriented adaptive parameter tuning is realized. The innovative synergy of each step significantly improves the decision-making rationality, navigation safety and efficiency, collision avoidance success rate, task adaptability and overall task effectiveness of unmanned ships in multi-task execution in complex environments, and comprehensively enhances their comprehensive performance and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of an unmanned vessel path planning and collision avoidance decision-making method based on multi-factor fusion provided by an embodiment of the present invention;

[0019] Figure 2 This is a flow chart of the unmanned vessel according to an embodiment of the present invention sensing and identifying the severity of a collision;

[0020] Figure 3 This is a flow chart of an incentive mechanism for setting an unmanned vessel path tracking response according to an embodiment of the present invention;

[0021] Figure 4This is a flow chart of the incentive mechanism for an unmanned vessel to avoid static and dynamic obstacles, provided by an embodiment of the present invention;

[0022] Figure 5 This is a structural diagram of an unmanned ship path planning and collision avoidance decision system based on multi-factor fusion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0024] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0025] See Figure 1 The present invention provides a method for unmanned ship path planning and collision avoidance decision-making based on multi-factor fusion, comprising the following steps:

[0026] Step S1: Build a multi-sensor fusion perception network for the unmanned vessel, integrating GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture the mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision.

[0027] Step S2: Setting an incentive mechanism for the unmanned ship path tracking response, simulating the navigation process of the unmanned ship under various path deviation conditions, defining the path tracking incentive function, and adaptively adjusting the dynamic parameters required in the path control process;

[0028] Step S3: Set up incentive mechanisms for static and dynamic obstacle avoidance. Use regression analysis and machine learning to determine incentive functions for static and dynamic obstacle avoidance. By continuously optimizing the sensor data processing algorithm and incentive function calculation model, evaluate the threat levels of static and dynamic obstacles.

[0029] Step S4: Integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse their respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make path planning and collision avoidance decisions under multiple tasks.

[0030] See Figure 2 ,Step S1: Build a multi-sensor fusion perception network for unmanned ships, integrating GPS, lidar, sonar and multispectral vision sensors, collecting environmental data in real time, and deploying high-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments in key collision areas of the unmanned ship's hull to capture the mechanical parameters at the moment of collision, so that the unmanned ship can perceive and identify the severity of the collision. Specifically:

[0031] Step S11: Deploy a high-precision global positioning system, lidar, sonar, and visual camera sensors on the unmanned vessel to build a comprehensive environmental perception system. Use GPS to obtain the vessel's precise geographic location information. Lidar and sonar scan the surrounding environment in real time to detect the location, distance, and shape of obstacles. Visual cameras provide intuitive image data to assist in judgment. By comparing and analyzing these sensor data with pre-stored feature data of different scenes, a pattern recognition algorithm is used to accurately identify the current environmental scene of the unmanned vessel.

[0032] Step S12: Install a collision detection device consisting of a highly sensitive combined collision sensor based on a pressure sensor and an accelerometer at the key parts of the unmanned ship to accurately measure the magnitude, direction and duration of the impact force at the moment of collision, and then calculate the collision angle θ collision and collision relative velocity v collision Based on a large amount of actual collision test data, a weighted function ω(θ collision ), and define the collision excitation function Enable the unmanned vessel to perceive and identify the severity of the collision.

[0033] In one embodiment provided by the present invention, for a head-on collision, a larger weighting value (such as 10) is assigned due to its extremely high risk of damage to the ship structure and equipment; for a side collision, a relatively smaller weighting value (such as 5) is assigned due to its relatively small degree of damage; the calculated collision angle and relative speed are substituted into the collision excitation function to obtain an accurate collision excitation value, providing a strong negative feedback signal to the intelligent agent, prompting it to avoid similar high-risk behaviors in subsequent decision-making.

[0034] See Figure 3 ,Step S2: Set up an incentive mechanism for the unmanned ship path tracking response, simulate the navigation process of the unmanned ship under various path deviation conditions, define the path tracking incentive function, and adaptively adjust the dynamic parameters required in the path control process, specifically:

[0035] Step S21: Obtain the lateral error ε of the unmanned ship in real time through the inertial navigation system, GPS and other sensors on the unmanned ship (t) , speed u (t) and heading information;

[0036] Step S22: Combine the exponential excitation function exp(-γ ε |ε (t) |) and Gaussian activation function The excitation characteristics within different path error ranges are constructed, a simulation environment is constructed, the navigation process of the unmanned ship under various path deviation conditions is simulated, the influence of the two excitation functions on the path adjustment behavior of the unmanned ship is recorded and analyzed, and the weight α is set for optimization to find the α value combination that can make the fusion performance of the two excitation functions optimal within different error ranges. At the same time, an adjustment function κ(ε (t) ), the intensity of the speed and heading excitation terms is dynamically adjusted according to the size of the lateral error, thereby defining the path tracking excitation function as:

[0037]

[0038] where γ ε is the weight coefficient of the lateral error and the path fit, which is used to adjust the control strength; U max is the maximum drivable speed of the unmanned ship, and the dynamic parameters required in the path control process are adaptively adjusted.

[0039] In one embodiment provided by the present invention, the combined effect of the exponential excitation function and Gaussian excitation has an important effect on the continuously changing excitation value based on distance (such as yaw error); it can encourage the unmanned ship to gradually converge to the desired state. Based on this continuously changing characteristic, combined with the speed and heading factors introduced by itself, the excitation function changes more smoothly and reasonably under different path deviations and ship motion states. For example, in the process of the unmanned ship approaching the precise path from a large path deviation, the weight of the excitation term based on speed and heading is better adjusted based on the gradual change characteristics of the exponential excitation function and Gaussian excitation combination, so that the ship can quickly approach the path while maintaining a stable motion state during the adjustment process.

[0040] In one embodiment of the present invention, when the lateral error sensor detects |ε (t) When |≥5, it indicates that the path deviation is large. At this time, the preset parameter adjustment algorithm is activated and the increased γ ε For example, if the initial value is increased from 0.5 to 0.8, the correction of lateral errors will be strengthened, prompting the unmanned ship to quickly adjust its course and speed. (t) When |≤1, to avoid path oscillation caused by over-adjustment, γ is automatically reduced. ε To 0.2, so that the intelligent agent can remain stable when approaching the path; according to the sea area information provided by the electronic chart system or the feedback of the terrain detection sonar, the type of sea area where the unmanned ship is located is judged. In open sea areas, in order to give the unmanned ship a larger exploration space and the ability to respond to environmental changes, σ in the Gaussian excitation is set to 10; in narrow waterways or areas with high-precision path requirements, σ ​​is reduced to 2 to improve the accuracy requirements of path tracking; at the same time, the speed sensor monitors the ship speed in real time. When u (t) <0.5U max When u is set, the ship speed is considered to be low, and the weight coefficient of the speed incentive item is appropriately increased (such as increasing by 0.2) through the preset speed incentive adjustment rule to encourage the unmanned ship to increase its speed; when u (t) >0.9U max When the speed is greater than 0.3, the weight of the speed incentive item is reduced (for example, by 0.3) to prevent speeding from affecting the path tracking accuracy and ensure that the unmanned ship can maintain good path tracking performance under different speed conditions.

[0041] See Figure 4 ,Step S3: Set up the incentive mechanism for static obstacle and dynamic obstacle avoidance, use regression analysis and machine learning to determine the incentive function for static obstacle avoidance and dynamic obstacle avoidance, and evaluate the threat level of static obstacles and dynamic obstacles by continuously optimizing the sensor data processing algorithm and incentive function calculation model. Specifically:

[0042] Step S31: Equip the unmanned vessel with a high-precision laser rangefinder, Doppler radar rangefinder, and speed sensor to accurately measure the rate of change of the distance between the unmanned vessel and the static obstacle in real time. Combined with the analysis of a large amount of actual navigation data, regression analysis and machine learning algorithms are used to determine the weighting coefficient β; at the same time, the angle between the current heading of the unmanned ship and the direction of the obstacle is obtained through the ship heading sensor, steering angle sensor, water flow sensor and electronic compass equipment. Ship steering ability parameter ω turn , water flow velocity v current and direction θ current Information, these parameters together with the obstacle distance x and the angle θ between the unmanned boat and the obstacle are used to construct the static obstacle avoidance incentive function:

[0043]

[0044] where γ θ,stat It is a parameter related to the relative angle between the unmanned boat and the static obstacle. Through this parameter, the weight of the incentive calculation can be adjusted according to the difference in relative angle. When the relative angle is in front, the value of this item will become smaller, thereby reducing the entire incentive value and strengthening the importance of collision avoidance. δ is a coefficient used to weight factors related to the position of the static obstacle. x is the distance weighting index, β is the spacing change rate weighting coefficient, χ is the ship speed weighting coefficient, is the steering ability weighting coefficient.

[0045] In an embodiment provided by the present invention, when the direction of the water flow is consistent with the direction of the ship heading towards the obstacle (the angle with the ship's heading is small) and the water flow speed is large, the incentive item is appropriately increased through a preset water flow impact compensation algorithm to compensate for the impact of the water flow on the speed of the ship approaching the obstacle; when the ship's steering ability is limited (small), the incentive weight related to the steering ability is increased to encourage the unmanned ship to plan a more reasonable collision avoidance path in advance.

[0046] Step S32: Based on AIS data, radar image recognition and machine learning target classification technology, the situation of dynamic obstacles in the unmanned ship's navigation area is grasped. According to the COLREGs rules and the direction and speed of the unmanned ship, the weighted parameters γ of the dynamic obstacles approaching the unmanned ship in the starboard area, port area and stern area are calculated. right , γ left and γ back Perform real-time optimization;

[0047] In an embodiment provided by the present invention, when a dynamic obstacle approaches the unmanned ship from the starboard side, the influence weight of the obstacle in the area on the activation function is adjusted according to factors such as distance; when the starboard side is close to the target ship, for example, less than 80 meters, γright The γ value jumps from 0.5 to 0.95, significantly increasing the decision weight of collision avoidance in the starboard area, prompting the USV to take evasive action on the target ship approaching on the starboard side first, in line with the requirements of the COLREGs rules for starboard avoidance. When in a crossing encounter situation, for example, when the speed of a dynamic obstacle appearing on the port side is greater than 0.5 times the maximum speed and the distance to the USV is between 60 meters and 180 meters, a slight increase of γ is made. left , ensuring appropriate response to dynamic obstacles on the port side in crossing encounter scenarios, achieving a balance between safe collision avoidance and efficient navigation; when dynamic obstacles approach quickly from the stern, increasing γ back The value of is used to increase the weight of obstacles in the stern direction in the incentive function, so that the unmanned ship can promptly detect the danger of the ship coming from behind and take effective collision avoidance measures to prevent accidents such as rear-end collisions.

[0048] Step S33: Define the dynamic collision avoidance excitation factor ξ(v target ,x,θ relative ,ω USV ) Improve the success rate of avoiding collisions with dynamic obstacles, and define where v target is the speed of the dynamic obstacle, x is the relative distance between the unmanned ship and the dynamic obstacle, θ relative is the relative azimuth between the unmanned ship and the dynamic obstacle, ω USV It is the turning ability of the unmanned vessel;

[0049] Step S34: Construct dynamic obstacle avoidance incentive function:

[0050]

[0051] Among them, γ θ,dyn is a parameter related to the relative angle of the dynamic obstacle. It can adjust the weight of this item in the excitation calculation according to the relative angle of the target ship. y is the velocity component of the dynamic obstacle in the direction of the unmanned ship, and ξ v (θ,v y ) is a velocity component weighting parameter based on the angle and velocity of the dynamic obstacle, which is specifically a piecewise function:

[0052]

[0053] η1, η2 and η3 are constant weighting factors, reflecting COLREGs compliance level C rules and The weight of .

[0054] Step S4: Integrate the path tracking response, static obstacle avoidance incentive mechanism, and dynamic obstacle avoidance incentive mechanism, and fuse the respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make multi-task path planning and collision avoidance decisions. Specifically:

[0055] Integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse their respective incentive functions into an organic whole. The overall incentive function is:

[0056]

[0057] where λ (t) is the weight associated with path tracking, which changes dynamically according to different path tracking factors. Its role is to determine the relative importance of path tracking incentives according to the current situation when calculating the total incentive, while 1-λ (t) It reflects the game between the path and the obstacle avoidance incentive; It includes additional incentives related to environmental stability, mission priority, etc. In terms of environmental stability, if the sea conditions are good and the water flow is stable, which is conducive to navigation, The part related to environmental stability will be given a positive incentive; in terms of task priority, if the current task is an emergency rescue task and the behavior of the unmanned boat helps to improve the rescue efficiency, then a positive incentive will be given according to the task priority.

[0058] In an embodiment provided by the present invention, different scene feature data include typical features of ports, narrow waterways, open seas, etc. After the scene type is determined, the weighting coefficients are initialized to corresponding values ​​according to a preset scene and weighting coefficient mapping table. In complex port environments or narrow waterway scenes, due to dense obstacles and limited space, λ is set. (t) It is set to a lower value (such as 0.2) to highlight the importance of collision avoidance incentives. In open and unobstructed sea scenarios, it will be set to a higher value (such as 0.8) to focus on path tracking incentives and provide basic guidance for subsequent decision-making.

[0059] In one embodiment provided by the present invention, when an unmanned vessel is in an emergency rescue mission, the unmanned vessel can adjust its strategy based on the mission characteristics and parameters, giving priority to quickly reaching the target and safely avoiding collisions, effectively improving rescue efficiency. By accurately increasing the speed incentive weight and strengthening the incentive intensity of obstacles in key areas, the unmanned vessel can significantly shorten the rescue time while ensuring safety, increase the probability of successful rescue, and gain valuable time to save lives and reduce accident losses, greatly improving the execution efficiency of emergency rescue missions. For example, in a maritime rescue operation, the unmanned vessel can quickly cross the open sea according to the adjusted incentive function, and when approaching the accident scene, it can quickly reach the rescue site by relying on its high vigilance against obstacles in key areas and effective collision avoidance, thereby increasing the chances of survival for trapped people.

[0060] In one embodiment provided by the present invention, when an unmanned vessel is used in a material transport mission, it ensures the stable operation of the vessel and the safety of the cargo throughout the entire transport process, reduces the risk of cargo damage, and ensures the integrity of the material transport. By reasonably reducing the speed incentive weight, increasing the incentive for smooth navigation, and increasing the intensity of incentives that affect the safe operation of the cargo, the risk of ship shaking and cargo displacement caused by improper operation of the unmanned vessel is effectively suppressed, the possibility of cargo damage during transportation is reduced, and the materials are ensured to be delivered to the destination intact and safely, providing a solid guarantee for the smooth completion of the material transport mission. For example, during long-distance material transport, the unmanned vessel can maintain a stable speed and small course adjustments to avoid collision, damage, or displacement of the cargo due to violent ship movement, ensuring that the quality and quantity of the materials are not affected.

[0061] In one embodiment provided by the present invention, when an unmanned vessel is performing ocean monitoring tasks, the unmanned vessel can efficiently carry out monitoring work in a wide ocean area, reasonably balancing multiple factors such as the monitoring range and navigation safety and data quality; by expanding the scope of incentives for exploration areas, increasing the incentive weight for sensor data quality, and providing incentives for poor navigation conditions, the unmanned vessel is encouraged to actively explore more areas and maintain a good navigation condition to obtain high-quality monitoring data, thereby significantly improving the effect and quality of ocean monitoring, providing richer and more accurate data support for fields such as marine scientific research, environmental protection, and resource exploration, and effectively promoting the development and decision-making of related fields. For example, in large-scale ocean monitoring tasks, the unmanned vessel can, in accordance with the optimized incentive function, expand the monitoring range while ensuring the accuracy of sensor data, obtain more information on the marine environment, ecology, etc., and provide a scientific basis for the rational development of marine resources and the formulation of environmental protection policies.

[0062] See Figure 5 The present invention provides an unmanned vessel path planning and collision avoidance decision-making system 100 based on multi-factor fusion, the system comprising:

[0063] The unmanned vessel's multi-sensor fusion perception network module 101 integrates GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision.

[0064] The unmanned vessel path tracking response excitation module 102 is used to simulate the navigation process of the unmanned vessel under various path deviation conditions, define the path tracking excitation function, and adaptively adjust the dynamic parameters required in the path control process;

[0065] The unmanned vessel static obstacle and dynamic obstacle collision avoidance incentive module 103 is used to determine the static obstacle collision avoidance incentive function using regression analysis and machine learning, and to evaluate the threat level of static and dynamic obstacles by continuously optimizing the sensor data processing algorithm and incentive function calculation model;

[0066] The unmanned ship incentive integration module 104 is used to integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse the respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make path planning and collision avoidance decisions under multiple tasks.

Claims

1. A method for unmanned ship path planning and collision avoidance decision-making based on multi-factor fusion, characterized in that: The following steps are involved: Step S1: Build a multi-sensor fusion perception network for the unmanned vessel, integrating GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture the mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision. Step S2: Setting an incentive mechanism for the unmanned ship path tracking response, simulating the navigation process of the unmanned ship under various path deviation conditions, defining the path tracking incentive function, and adaptively adjusting the dynamic parameters required in the path control process; Step S3: Set up incentive mechanisms for static and dynamic obstacle avoidance. Use regression analysis and machine learning to determine incentive functions for static and dynamic obstacle avoidance. By continuously optimizing the sensor data processing algorithm and incentive function calculation model, evaluate the threat levels of static and dynamic obstacles. Step S4: Integrate the path tracking response, static obstacle avoidance incentive mechanism and dynamic obstacle avoidance incentive mechanism, and fuse the respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make multi-task path planning and collision avoidance decisions; The step S2 specifically includes: Step S21: Obtain the lateral error of the unmanned ship in real time through the inertial navigation system and GPS sensor on the unmanned ship ,speed and heading information; Step S22: Combine exponential activation function and Gaussian activation function The excitation characteristics within different path error ranges are used to build a simulation environment to simulate the navigation process of the unmanned ship under various path deviation conditions, record and analyze the impact of the two excitation functions on the path adjustment behavior of the unmanned ship, and set the weights Optimize and find the best fusion performance of the two excitation functions within different error ranges. value combination, and design an adjustment function related to the lateral error , the intensity of the speed and heading excitation terms is dynamically adjusted according to the size of the lateral error, so that the path tracking excitation function is defined as: ； in is the weight coefficient of the lateral error and the path fit, which is used to adjust the control force; The maximum drivable speed of the unmanned ship is set, and the dynamic parameters required in the path control process are adaptively adjusted; The step S3 specifically includes: Step S31: Equip the unmanned vessel with a high-precision laser rangefinder, Doppler radar rangefinder, and speed sensor to accurately measure the rate of change of the distance between the unmanned vessel and the static obstacle in real time. ; Combined with the analysis of a large amount of actual navigation data, the weighting coefficient is determined using regression analysis and machine learning algorithms At the same time, the angle between the current heading of the unmanned ship and the direction of the obstacle is obtained through the ship heading sensor, steering angle sensor, water flow sensor and electronic compass equipment. , Ship steering capability parameters , water flow velocity and direction Information, these parameters and obstacle distance , the angle between the unmanned boat and the obstacle Information, together with the static obstacle avoidance incentive function: ； in This is a parameter related to the relative angle between the unmanned boat and the static obstacle. Through this parameter, the weight of the incentive calculation can be adjusted according to the relative angle. When the relative angle is in front, the value of this item will become smaller, thereby reducing the entire incentive value and strengthening the importance of collision avoidance. is the coefficient used to weight the factors related to the position of static obstacles; is the distance-weighted exponent, is the weighted coefficient of the spacing change rate, is the ship speed weighting coefficient, is the steering ability weighting coefficient; Step S32: Based on AIS data, radar image recognition and machine learning target classification technology, the situation of dynamic obstacles in the unmanned ship's navigation area is grasped. According to the COLREGs rules and the direction and speed of the unmanned ship, the weighted parameters of dynamic obstacles approaching the unmanned ship in the starboard area, port area and stern area are calculated. 、 and Perform real-time optimization; Step S33: Define the factors of dynamic collision avoidance excitation Improve the success rate of avoiding collisions with dynamic obstacles, and define ,in is the velocity of the dynamic obstacle, is the relative distance between the unmanned ship and the dynamic obstacle, is the relative azimuth angle between the unmanned ship and the dynamic obstacle, It is the turning ability of the unmanned vessel; Step S34: Construct dynamic obstacle avoidance incentive function: ； in, is a parameter related to the relative angle of the dynamic obstacle. It can adjust the weight of this item in the excitation calculation according to the relative angle of the target ship. is the velocity component of the dynamic obstacle in the direction of the unmanned ship, and It is a weighted parameter of the velocity component based on the angle and velocity of the dynamic obstacle, which is specifically a piecewise function: ； and 、 and are constant weighting factors, reflecting , COLREGs compliance and The weight of .

2. The unmanned vessel path planning and collision avoidance decision-making method based on multi-factor fusion according to claim 1, characterized in that: The step S1 specifically includes: Step S11: Deploy a high-precision global positioning system, lidar, sonar, and visual cameras on the unmanned vessel to build a comprehensive environmental perception system. GPS is used to obtain the vessel's precise geographic location information. Lidar and sonar scan the surrounding environment in real time to detect the location, distance, and shape of obstacles. The visual camera provides intuitive image data to assist in judgment. By comparing and analyzing these sensor data with pre-stored feature data of different scenes, a pattern recognition algorithm is used to accurately identify the current environmental scene of the unmanned vessel. Step S12: Install a collision detection device consisting of a highly sensitive combined collision sensor based on a pressure sensor and an accelerometer at the key parts of the unmanned ship to accurately measure the magnitude, direction and duration of the impact force at the moment of collision, and then calculate the collision angle. and collision relative velocity Based on a large amount of actual collision test data, a weighted function related to the collision angle is constructed using data fitting and machine learning algorithms. , and define the collision excitation function , enabling the unmanned ship to perceive and identify the severity of the collision.

3. The unmanned vessel path planning and collision avoidance decision-making method based on multi-factor fusion according to claim 1, characterized in that: The step S4 specifically includes: Integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse their respective incentive functions into an organic whole. The overall incentive function is: ； in is the weight associated with path tracking, which changes dynamically according to different path tracking factors. Its role is to determine the relative importance of path tracking incentives according to the current situation when calculating the total incentive, while It reflects the game between the path and the obstacle avoidance incentive; It includes additional incentives related to environmental stability and mission priority. In terms of environmental stability, if the sea conditions are good and the water flow is stable, environmental factors that are conducive to navigation will appear. The part related to environmental stability will be given a positive incentive; in terms of task priority, if the current task is an emergency rescue task and the behavior of the unmanned boat helps to improve the rescue efficiency, then a positive incentive will be given according to the task priority.

4. An unmanned vessel path planning and collision avoidance decision system 100 based on multi-factor fusion, used to implement the unmanned vessel path planning and collision avoidance decision method based on multi-factor fusion according to any one of claims 1 to 3, characterized in that: The system includes: The unmanned vessel's multi-sensor fusion perception network module 101 integrates GPS, lidar, sonar, and multispectral vision sensors to collect environmental data in real time. High-sensitivity piezoelectric impact sensors and high-precision optical collision angle measuring instruments are deployed in key collision areas of the unmanned vessel's hull to capture mechanical parameters at the moment of collision, enabling the unmanned vessel to perceive and identify the severity of the collision. The unmanned vessel path tracking response excitation module 102 is used to simulate the navigation process of the unmanned vessel under various path deviation conditions, define the path tracking excitation function, and adaptively adjust the dynamic parameters required in the path control process; The unmanned vessel static obstacle and dynamic obstacle collision avoidance incentive module 103 is used to determine the static obstacle collision avoidance incentive function using regression analysis and machine learning, and to evaluate the threat level of static and dynamic obstacles by continuously optimizing the sensor data processing algorithm and incentive function calculation model; The unmanned ship incentive integration module 104 is used to integrate the path tracking response, static obstacle and dynamic obstacle avoidance incentive mechanisms, and fuse the respective incentive functions into an organic whole, so that the unmanned ship can balance the relationship between different mission objectives and make path planning and collision avoidance decisions under multiple tasks.

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