Unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions

Abstract

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the field of unmanned ship multi-agent cooperative obstacle avoidance, in particular to an unmanned ship multi-agent cooperative obstacle avoidance method and system for complex sea conditions. BACKGROUND

[0002] The unmanned ship multi-agent system has become a frontier research direction in the marine field due to its high efficiency, high redundancy and wide coverage range in performing tasks such as regional mapping, ocean monitoring, search and rescue patrol. An ideal cooperative system requires the fleet not only to complete the given formation sailing, but also to make timely, safe and cooperative obstacle avoidance decisions in the dynamic and complex real marine environment, facing static obstacles (such as islands, reefs, anchored ships) and dynamic obstacles (such as passing ships, drifts). At present, unmanned ship cluster cooperative obstacle avoidance mainly realizes environment and obstacle perception through multi-sensor fusion, generates an obstacle avoidance path based on a path planning algorithm, and eliminates inter-ship conflicts by means of a cooperative decision mechanism. Among them, the multi-sensor (inertial measurement unit, laser radar, millimeter wave radar, wave sensor, etc.) is the core of the perception layer, which needs to synchronously collect ship posture, obstacle, sea condition and other data; the path planning needs to balance the safety and efficiency of obstacle avoidance and adapt to the physical characteristics of the ship (minimum turning radius, maximum turning rate); the cooperative decision needs to avoid single-point failure of central control in the limited communication marine environment, and realize distributed consensus. With the expansion of the scale of the unmanned ship cluster and the complexity of the operation scene, the requirements for sea condition adaptability, obstacle avoidance real-time performance and cooperative robustness are significantly improved. The traditional technology needs to be further optimized to adapt to the actual needs in complex sea conditions, and to provide technical support for safe and efficient operation of the unmanned ship cluster.

[0003] The traditional path planning algorithm (such as the standard artificial potential field method) assumes that the ship has ideal maneuverability in still water, completely ignores the key physical facts such as the increase of ship turning inertia, the decline of ship speed maintaining ability and the extension of effective braking distance caused by the combined action of wind, wave and current, which leads to the fact that the planned path may be too aggressive or contain maneuvering actions beyond the dynamic capability of the ship, resulting in collision of the ship in actual execution due to inability to accurately track the trajectory (such as turning not enough or drifting too much). Secondly, the conflict resolution mechanism of the existing multi-agent cooperative algorithm (such as the early CBBA) is usually based on static geometric paths or time windows, while ignoring the fact that in complex sea conditions, the environmental load suffered by unmanned ships at different positions and different headings in the cluster is heterogeneous and time-varying, which means that the dynamic safety boundary and maneuvering capability of each ship are real-time different. Lack of unified quantification and characterization of such dynamic constraints will lead to the fact that the so-called "collision-free" path combination generated by the cooperative decision may force some ships to perform high-risk maneuvers in severe conditions where their maneuvering ability is severely limited, thereby burying safety hazards from the system level and damaging the overall safety and efficiency of the cluster cooperation. SUMMARY

[0004] To solve the above technical problems, a multi-agent collaborative obstacle avoidance method and system for unmanned ships in complex sea conditions are provided. The technical solution solves the problem that the planned path may be too aggressive or contain maneuvering actions beyond the dynamic capability of the ship, causing the ship to collide in actual execution due to the inability to accurately track the trajectory, and the lack of unified quantification and characterization of such dynamic constraints, which may force some ships to perform high-risk maneuvers in severe conditions where their maneuvering capabilities are severely limited.

[0005] To achieve the above purposes, the technical solution adopted by the present application is: A multi-agent collaborative obstacle avoidance method for unmanned ships in complex sea conditions, comprising: Obtaining real-time data of each unmanned ship and the surrounding environment through a multi-sensor fusion system mounted on each unmanned ship; According to the surrounding obstacle point cloud data, based on the density clustering algorithm, screening the candidate obstacle target point cloud cluster, and generating a candidate obstacle target point cloud cluster list; According to the screened candidate obstacle target point cloud cluster, based on the Kalman filter, obtaining the real-time absolute motion state estimation of the candidate obstacle, and outputting an obstacle feature list; According to the real-time data of each unmanned ship and the surrounding environment, determining the safety radius compensation required for each unmanned ship to autonomously avoid obstacles; According to the obstacle feature list, combined with the safety radius required for each unmanned ship to autonomously avoid obstacles, obtaining the safety navigable space of each unmanned ship at the current time; Based on the safety navigable space of each unmanned ship, a global synthetic potential field is constructed, and a set of optimal candidate paths of the unmanned ship from the current position to the target point is generated; According to the candidate path of each unmanned ship from the current position to the target point, based on the consensus binding algorithm, a multi-agent collaborative obstacle avoidance path of the unmanned ship is obtained.

[0006] Preferably, according to the surrounding obstacle point cloud data, based on the density clustering algorithm, screening the candidate obstacle target point cloud cluster, and generating a candidate obstacle target point cloud cluster list specifically comprises: Synchronously obtaining three-dimensional space point cloud data provided by the laser radar and target reflection point cloud data provided by the millimeter wave radar; The three-dimensional space point cloud data and the target reflection point cloud data are converted to the body coordinate system centered on the unmanned ship through coordinate transformation; In the unified coordinate system, data association is performed on the laser radar point cloud and the millimeter wave radar point cloud to generate a preliminary fusion point cloud covering space and reflection characteristics; The raw image collected by the visible light / infrared vision sensor is input into a pre-trained semantic segmentation neural network to assign a semantic label to each pixel and generate a semantic label image; The fused point cloud data is input into a density clustering algorithm. The algorithm identifies a set of point clouds that are closely distributed in space and have a density reaching a preset threshold a1 as an independent point cloud cluster. All spatially isolated points that fail to be classified into any point cloud cluster are determined as sensor noise or transient clutter and filtered out. The preliminary point cloud cluster is projected onto the semantic label image, and the semantic label distribution of the image area covered by each point cloud cluster is counted. If the proportion of the sea wave label is not less than a preset threshold a2, the cluster is determined as a sea wave false target, and is filtered out from the candidate target list. The three-dimensional bounding box size of the remaining point cloud cluster is calculated, and the cluster with a size exceeding a preset physical range [a3, a4] is filtered out, and a list of candidate obstacle target point cloud clusters filtered out is output. The geometric center coordinates in the list of candidate obstacle target point cloud clusters filtered out are converted from the body coordinate system centered on the unmanned ship to the geodetic coordinate system.

[0007] Preferably, the real-time absolute motion state estimation of the candidate obstacle is obtained based on the Kalman filter according to the candidate obstacle target point cloud cluster filtered out, and an obstacle feature list is output, which specifically includes: A Kalman filter is initialized for each newly identified candidate obstacle, wherein the state vector of the Kalman filter includes the geodetic coordinates, velocity, heading angle and turning rate of the candidate obstacle point cloud cluster; Based on the uniform turning motion model, the system state transition equation of the Kalman filter is constructed, and the geodetic coordinates of the current frame candidate obstacle point cloud cluster are taken as the observation vector; Based on the Kalman filter, the updated state vector is output as the real-time and optimal absolute motion state estimation of the corresponding obstacle; According to the velocity in the absolute motion state estimation output by the Kalman filter; The motion determination threshold and the turning determination threshold are set, and the state of the obstacle is judged in combination with the velocity value and the turning rate, wherein the state of the obstacle refers to static or motion; According to the absolute motion state estimation of the obstacle, the relative distance, relative velocity and relative heading of the obstacle relative to the unmanned ship are obtained through vector operation based on the geodetic coordinates and heading angle of the unmanned ship; According to the absolute motion state estimation of the obstacle, the relative distance, relative velocity and relative heading of the obstacle relative to the unmanned ship, the state of the obstacle and the corresponding semantic label, an obstacle feature list is constructed; The judging the state of the obstacle specifically includes: If the absolute speed value is not lower than the motion determination threshold for N1 frames, or the absolute value of the absolute turning rate is not lower than the turning determination threshold for N1 frames, it is determined that the obstacle is moving, otherwise, it is determined that the obstacle is static; When it is determined that the obstacle is static, if the semantic label thereof belongs to a predefined set of categories with kinetic energy, it is marked as a static obstacle with kinetic energy.

[0008] Preferably, the safety radius compensation required for autonomous obstacle avoidance of each unmanned ship is determined according to real-time data of each unmanned ship and the surrounding environment, specifically including: According to the wave direction spectrum and wave height data collected by the unmanned ship, a sea state interference factor representing the severity of the sea state is calculated; Based on the sea state interference factor and the wind flow vector data, the effective maneuverability degradation coefficient of the unmanned ship under the combined action of wind, wave and current is calculated through a ship motion response model; Based on the effective maneuverability degradation coefficient, the safety radius compensation required for autonomous obstacle avoidance of the unmanned ship is calculated.

[0009] Preferably, the safety navigable space of each unmanned ship at the current time is obtained according to the obstacle feature list and the safety radius required for autonomous obstacle avoidance of each unmanned ship, specifically including: According to the length, width and height of the bounding box of all static obstacles in the list, the distance of the outward expansion of the safety radius, a static prohibited area is generated; For each moving obstacle in the obstacle feature list, based on the real-time absolute motion state estimation output by the Kalman filter, the uniform turning motion model is substituted into the forward numerical simulation to predict the position of the obstacle at a series of discrete time points in the future, thereby generating the predicted trajectory of the obstacle; According to the predicted trajectory of the obstacle, combined with the length, width and height of the obstacle bounding box, a dynamic threat corridor varying with time is generated by expanding a safety radius along the center line of the trajectory on both sides; The static prohibited area and the dynamic threat corridor are removed from the global environment to obtain the safety navigable space of each unmanned ship at the current time.

[0010] Preferably, the global synthetic potential field is constructed based on the safety navigable space of each unmanned ship, and a set of optimal candidate paths of the unmanned ship from the current position to the target point is generated, specifically including: Taking the current position of each unmanned ship as the origin, a local plane coordinate system is established based on the geodetic coordinate system, the safety navigable space is converted to the local coordinate system to form a local search space for path search; discretize the continuous state space of the unmanned ship into a four-dimensional space-time state grid in a local plane coordinate system, each node of the space-time state grid representing a specific state, the specific state including: the position of the unmanned ship, the heading angle of the unmanned ship, and the time from the start of the path generation; setting node parameters based on the physical characteristics of the unmanned ship and expert experience to define a search granularity for the specific state in the space-time state grid that matches the physical characteristics of the unmanned ship; constructing a global synthetic potential field in a local plane coordinate system, the synthetic potential field being composed of a target attractive field and an obstacle repulsive field; the target attractive field refers to the potential energy value at any node, which is calculated from the Euclidean distance from the node to the target node; the obstacle repulsive field refers to the potential energy value at any node, which is calculated from the Euclidean distance from the node to the nearest obstacle; the global synthetic potential field is obtained by linearly superimposing the target attractive field and the obstacle repulsive field; starting from the current state node of the unmanned ship, the negative gradient direction of the node position in the synthetic potential field is calculated; centered on the negative gradient direction, candidate heading angles are sampled within a preset offset interval according to the heading angle resolution; based on the candidate heading angles, the position coordinates of the subsequent nodes are calculated through the ship kinematics model combined with the current speed and the time step, to generate the corresponding subsequent state nodes, thereby forming a candidate polyline path node sequence; immediately verify whether the new node and the line segment formed with the previous node have an intersection with the obstacle region, and only keep the nodes without collision; smooth each polyline path in the set of collision-free candidate polyline paths to obtain a set of smoothed candidate paths; calculate the total geometric length of each path in the set of smoothed candidate paths, and select the candidate path with the smallest total geometric length as the optimal candidate path of the unmanned ship from the current position to the target point.

[0011] Preferably, the consensus-based bundling algorithm is used to obtain a consensus-based multi-agent cooperative obstacle avoidance path of the unmanned ship based on the candidate paths of the unmanned ship from the current position to the target point, which specifically includes: each unmanned ship encapsulates the optimal candidate path generated by itself and the total geometric length of the path into a data packet, and broadcasts it to all other unmanned ships in the cluster through wireless ad hoc networking; after receiving the data packets from other ships, each unmanned ship independently constructs a global cooperative decision matrix in its local processing system; The collaborative decision matrix has a row dimension corresponding to the number of each unmanned ship in the cluster and a column dimension corresponding to an optimal candidate path of each ship and a total geometric length of the path. The consensus binding algorithm is used for distributed path combination optimization of the unmanned ship cluster, and a consensus reached unmanned ship multi-agent collaborative obstacle avoidance path is output. Each unmanned ship receives and executes the collaborative path assigned to it in the final path combination, and continues to travel to the final target point according to the preset formation after the obstacle avoidance is completed.

[0012] Further, the scheme proposes a complex sea state oriented unmanned ship multi-agent collaborative obstacle avoidance system for realizing the complex sea state oriented unmanned ship multi-agent collaborative obstacle avoidance method as described above, comprising: The data acquisition module is configured to acquire real-time data of each unmanned ship and the surrounding environment through a multi-sensor fusion system mounted on each unmanned ship. The navigable space module is configured to filter candidate obstacle target point cloud clusters based on a density clustering algorithm and generate a candidate obstacle target point cloud cluster list according to the surrounding obstacle point cloud data, obtain real-time absolute motion state estimation of the candidate obstacle based on a Kalman filter according to the filtered candidate obstacle target point cloud clusters, and output an obstacle feature list, determine a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship according to the real-time data of each unmanned ship and the surrounding environment, and obtain the safety navigable space of each unmanned ship at the current time according to the obstacle feature list in combination with the safety radius required for autonomous obstacle avoidance of each unmanned ship. The collaborative obstacle avoidance module is configured to construct a global synthetic potential field based on the safety navigable space of each unmanned ship, generate a set of optimal candidate paths of the unmanned ship from the current position to the target point, and obtain a consensus reached unmanned ship multi-agent collaborative obstacle avoidance path based on the consensus binding algorithm according to the candidate paths of each unmanned ship from the current position to the target point. The navigable space module specifically comprises: The point cloud cluster unit is configured to filter candidate obstacle target point cloud clusters based on a density clustering algorithm and generate a candidate obstacle target point cloud cluster list according to the surrounding obstacle point cloud data. The obstacle feature unit is configured to obtain real-time absolute motion state estimation of the candidate obstacle based on a Kalman filter according to the filtered candidate obstacle target point cloud clusters, and output an obstacle feature list. The safety radius unit is configured to determine a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship according to the real-time data of each unmanned ship and the surrounding environment. The navigable space unit is used to obtain the safe navigable space of each unmanned ship at the current time according to the obstacle feature list and the safety radius required by each unmanned ship for autonomous obstacle avoidance; The cooperative obstacle avoidance module specifically comprises: The optimal path unit is used for each unmanned ship to construct a global synthetic potential field based on the safe navigable space of the unmanned ship, and generate a set of optimal candidate paths of the unmanned ship from the current position to the target point. The cooperative obstacle avoidance unit is used to obtain a consensus unmanned ship multi-agent cooperative obstacle avoidance path based on the consensus binding algorithm according to the candidate paths of each unmanned ship from the current position to the target point.

[0013] Compared with the prior art, the beneficial effects of the present application are: The present application provides a complex sea state oriented unmanned ship multi-agent cooperative obstacle avoidance method and system. The present application synchronously collects the data such as the position and posture of each unmanned ship, obstacle point cloud, sea wave direction spectrum, wind flow vector and the like through a multi-sensor fusion system, realizes the classification and identification of static, dynamic and kinetic static obstacles and the motion state estimation by combining the density clustering algorithm and the Kalman filtering algorithm, quantifies the influence of the environment on the ship maneuverability based on the sea state interference factor, dynamically adjusts the safety radius compensation to build a precise safe navigable space, and generates an optimal candidate path adapting to the physical characteristics of the ship through the spatiotemporal state grid discretization and the global synthetic potential field. Finally, the distributed path combination optimization and conflict resolution are realized by relying on the consensus binding algorithm, thereby effectively solving the problems such as obstacle misidentification caused by sensor interference under complex sea conditions, mismatch between fixed safety boundary and ship maneuverability degradation, path planning deviating from actual sea condition constraints, and failure of cooperative decision-making relying on central control. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A complex sea state oriented unmanned ship multi-agent cooperative obstacle avoidance method flowchart of the present application; Figure 2 A candidate obstacle target point cloud cluster list flowchart of the present application based on the density clustering algorithm, filtering the candidate obstacle target point cloud cluster, and generating the candidate obstacle target point cloud cluster list; Figure 3 A flowchart of the present application for obtaining the safe navigable space of each unmanned ship at the current time; Figure 4 A flowchart of the present application for constructing a global synthetic potential field and generating a set of optimal candidate paths of the unmanned ship from the current position to the target point. DETAILED DESCRIPTION

[0015] The following description is used to disclose the present application to enable a person skilled in the art to implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be conceived by those skilled in the art.

[0016] Referring to Figure 1 As shown in the figure, a multi-agent collaborative obstacle avoidance method for unmanned ships in complex sea conditions includes: Through the multi-sensor fusion system carried on each unmanned ship, real-time data of each unmanned ship and the surrounding environment is obtained; According to the surrounding obstacle point cloud data, based on the density clustering algorithm, the candidate obstacle target point cloud cluster is screened, and a candidate obstacle target point cloud cluster list is generated; According to the screened candidate obstacle target point cloud cluster, based on the Kalman filter, the real-time absolute motion state estimation of the candidate obstacle is obtained, and an obstacle feature list is output; According to the real-time data of each unmanned ship and the surrounding environment, the safety radius compensation required for autonomous obstacle avoidance of each unmanned ship is determined; According to the obstacle feature list, combined with the safety radius required for autonomous obstacle avoidance of each unmanned ship, the safety navigable space of each unmanned ship at the current time is obtained; Each unmanned ship constructs a global synthetic potential field based on its own safety navigable space, and generates a set of optimal candidate paths of the unmanned ship from the current position to the target point; According to the candidate path of each unmanned ship from the current position to the target point, based on the consensus binding algorithm, a consensus reached multi-agent collaborative obstacle avoidance path of the unmanned ship is obtained.

[0017] It can be explained that this method constructs a progressive and closed-loop decision system from "individual perception" to "group consensus", the core logic of which is to gradually convert complex physical world information into a standardized model that can be calculated and decided, and finally output a safe and collaborative group action plan. Specifically, through the multi-sensor fusion system, the pose, obstacle point cloud, sea wave direction spectrum, wind flow vector and other data of each unmanned ship are synchronously collected, combined with the density clustering algorithm and Kalman filtering algorithm to realize the classification and identification of static, dynamic and kinetic static obstacles and motion state estimation, and based on the sea state interference factor, the influence of the environment on the maneuverability of the ship is quantified. Adjust the safety radius compensation to build an accurate safety navigable space; On this basis, through the spatiotemporal state grid discretization and global synthetic potential field generation, the optimal candidate path suitable for the physical characteristics of the ship is generated, and finally relying on the consensus binding algorithm, the distributed path combination optimization and conflict resolution are realized, so as to realize safe and efficient group obstacle avoidance in complex sea conditions.

[0018] The multi-sensor fusion system carried on each unmanned ship to obtain real-time data of each unmanned ship and the surrounding environment specifically includes: The real-time data includes: the pose state of each unmanned ship, the surrounding obstacle point cloud data, the sea wave direction spectrum and wave height data, and the wind flow vector data; The multi-sensor fusion system includes: an inertial measurement unit, a GPS positioning module, a laser radar, a millimeter wave radar, a visible light / infrared vision sensor, a wave sensor, and a wind speed and direction sensor; The inertial measurement unit and the GPS positioning module are combined to collect the real-time pose state of the ship, including longitude, latitude, heading angle, speed, and roll / pitch / heel angle. The laser radar and the millimeter wave radar cooperatively scan the surrounding environment to obtain the original obstacle point cloud data in the surrounding range, and the visible light / infrared vision sensor assists in collecting obstacle image features. The wave sensor collects sea wave direction spectrum, significant wave height, and wave period data based on spectral analysis technology, and the wind speed and direction sensor collects instantaneous wind speed, wind direction, and sea current speed and direction data. The data collected by all sensors are processed for space-time alignment, with the GPS timestamp as the reference to unify the time dimension of each sensor data. The sensor data after coordinate alignment are smoothed by Kalman filtering algorithm to eliminate outliers, and the filtered data are mapped to the [0, 1] interval through normalization processing. Each unmanned ship shares the preprocessed real-time data to the fleet data center through a wireless communication network.

[0019] It can be explained that data acquisition is an effective data guarantee for unmanned ships to perceive risks and intelligently avoid obstacles in complex sea conditions. Through the multi-sensor fusion system carried on each unmanned ship, real-time data of each unmanned ship and the surrounding environment are obtained, which aims to build a complete "ship-obstacle-environment" coupled dynamic perception system. The pose state obtained by the inertial measurement unit and the GPS positioning module provides a reference for cooperative control, the obstacle point cloud data collected by the laser radar, the millimeter wave radar, and the vision sensor is the direct input for obstacle avoidance perception, the sea wave direction spectrum and wave height data provided by the wave sensor are the key parameters for quantifying the influence of complex sea conditions on ship maneuverability, the wind flow vector data collected by the wind speed and direction sensor is the necessary condition for quantifying the influence of the environment on the ship maneuverability and supporting the dynamic adjustment of the safety radius, and the remaining parameters output by the sensors are used as auxiliary data for system state monitoring and data recording. The systematic integration of these data provides irreplaceable data guarantee for subsequent dynamic threat cloud map construction and intelligent cooperative decision-making, enabling the system not only to perceive obstacles, but also to understand the ship maneuvering boundaries and environmental influences under sea conditions, thereby generating safe and reliable cooperative obstacle avoidance strategies.

[0020] Reference Figure 2As shown, the density-based clustering algorithm filters the candidate obstacle target point cloud clusters and generates a candidate obstacle target point cloud cluster list specifically includes: Synchronously acquire three-dimensional space point cloud data provided by the laser radar and target reflection point cloud data provided by the millimeter wave radar; Convert the three-dimensional space point cloud data and the target reflection point cloud data to the body coordinate system centered on the unmanned ship through coordinate transformation; In the unified coordinate system, data association is performed on the laser radar point cloud and the millimeter wave radar point cloud to generate preliminary fusion point cloud covering space and reflection characteristics; The original image collected by the visible light / infrared vision sensor is input into a pre-trained semantic segmentation neural network to assign a semantic label to each pixel and generate a semantic label image; The fusion point cloud data is input into a density clustering algorithm, and the algorithm identifies a point cloud set that is closely distributed in space and has a density reaching a preset threshold a1 as an independent point cloud cluster; All spatially isolated points that cannot be classified into any point cloud cluster are determined as sensor noise or transient clutter and are filtered out; The preliminary formed point cloud cluster is projected onto the semantic label image, the semantic label distribution of the image area covered by each point cloud cluster is counted, and if the proportion of the sea wave label is not less than a preset threshold a2, the cluster is determined as a sea wave false target and is filtered out from the candidate target list; For the remaining point cloud clusters, calculate the size of the three-dimensional bounding box thereof, filter out the clusters whose size exceeds the preset physical range [a3, a4], and output the filtered candidate obstacle target point cloud cluster list; Convert the geometric center coordinates in the filtered candidate obstacle target point cloud cluster list from the body coordinate system centered on the unmanned ship to the geodetic coordinate system.

[0021] It can be explained that the scheme can effectively avoid the deficiency of single sensor in complex sea state monitoring ability by fusing the laser radar point cloud, millimeter wave radar point cloud and visible light / infrared vision sensor collected data, and improve the robustness of unmanned ship in complex sea state environment perception. Secondly, the scheme preprocesses the fused point cloud data through the density clustering algorithm, selects the candidate obstacle target point cloud cluster through the close distribution and density of the spatial position, and generates a candidate obstacle target point cloud cluster list. After completing the point cloud clustering and screening, the geometric center of the target is converted to the geodetic coordinate system, thereby avoiding the huge calculation overhead brought by coordinate conversion of massive original point cloud, ensuring the real-time performance of the system, and making the clustering process complete in the stable body coordinate system, effectively improving the robustness and accuracy of obstacle identification, and laying a reliable data foundation for subsequent cooperative perception and path planning. Each element in the candidate obstacle target point cloud cluster list represents an identified independent candidate obstacle, and each element (i.e. each candidate obstacle) usually contains the following structured information: Unique identifier: ID for tracking the target; Geometric center coordinates: three-dimensional center position coordinates of the point cloud cluster in the body coordinate system; Three-dimensional size: length, width and height of the bounding box; Point cloud cluster reference: pointer or index to the original point cloud data constituting the target; Preliminary classification / attribute: label type divided according to semantic label diagram, such as unknown, ship-shaped, buoy-shaped and wave, etc. Each unmanned ship independently generates a local candidate obstacle list of its own based on its local perspective, enabling each unmanned ship to independently perceive and understand its surroundings, which is the basis for its autonomous obstacle avoidance function; wherein the semantic segmentation neural network can adopt mature encoder-decoder architectures such as U-Net, DeepLab, etc. The network obtains the ability to perform pixel-level classification on input images by supervised training on image datasets containing annotations of marine scene obstacles (such as ships, buoys, waves, etc.). The semantic label map is a matrix of the same size as the input image, and the value of each element in the matrix represents the semantic category of the corresponding pixel point. The settings of a1, a2 and [a3, a4] are all based on prior knowledge analysis of typical obstacles and marine environment characteristics: the threshold a1 is determined by the physical size and point cloud density of typical small obstacles (such as buoys and small ships), aiming to ensure that such targets can be effectively clustered, while avoiding misclustering large-scale sea wave clutter as a single target; the threshold a2 is based on the spatial distribution characteristics of sea wave spray in visual images, and by analyzing the pixel connectivity of the wave area, a proportion threshold is set to distinguish between continuous waves and independent obstacles; the range [a3, a4] is based on the obstacle size specified in the International Maritime Organization (IMO) regulations and the actual sailing environment, the lower limit a3 is used to filter out irrelevant targets such as bird flocks and small floating objects, and the upper limit a4 is used to exclude super-large virtual images caused by multiple reflections or perception splicing errors, thereby ensuring that the selected targets have physical dimensions that pose a threat to navigation.

[0022] The real-time absolute motion state estimation of the candidate obstacle is obtained based on the Kalman filter according to the selected candidate obstacle point cloud cluster, and the obstacle feature list is output, which specifically includes: A Kalman filter is initialized for each newly identified candidate obstacle, wherein the state vector of the Kalman filter includes the geodetic coordinates, velocity, heading angle and turning rate of the candidate obstacle point cloud cluster; Based on the uniform turning motion model, the system state transition equation of the Kalman filter is constructed, and the geodetic coordinates of the current frame candidate obstacle point cloud cluster are taken as the observation vector; Based on the Kalman filter, the updated state vector is output as the real-time and optimal absolute motion state estimation of the corresponding obstacle; According to the velocity in the absolute motion state estimation output by the Kalman filter; Set a motion determination threshold and a turning determination threshold, and combine the velocity value and the turning rate to determine the state of the obstacle, wherein the state of the obstacle refers to static or motion; According to the absolute motion state estimation of the obstacle, a relative distance, a relative speed and a relative heading of the obstacle relative to the unmanned ship are obtained through vector operation based on the earth coordinates and the heading angle of the unmanned ship; According to the absolute motion state estimation of the obstacle, a relative distance, a relative speed and a relative heading of the obstacle relative to the unmanned ship, an obstacle state and a corresponding semantic label, an obstacle feature list is constructed; The judging of the state of the obstacle specifically includes: If the absolute speed value of the obstacle is not lower than a motion determination threshold for N1 frames or the absolute value of the absolute turning rate of the obstacle is not lower than a turning determination threshold for N1 frames, the obstacle is determined as a moving obstacle. Otherwise, the obstacle is determined as a stationary obstacle. When the obstacle is determined as a stationary obstacle, if the semantic label of the obstacle belongs to a predefined category set with kinetic energy, the obstacle is marked as a stationary obstacle with kinetic energy.

[0023] It can be explained that in the face of complex sea conditions, the unmanned ship not only needs to face fixed obstacles, but also needs to consider moving obstacles. If only the sensor detects the obstacles, there may be a problem of not timely obstacle avoidance. At the same time, since the unmanned ship is a multi-agent cooperative navigation, there may be a chaotic fast obstacle avoidance between each unmanned ship, especially for the obstacles with kinetic energy which are stationary in the early stage. Since there is a risk of sudden start, it leads to the problem of not timely obstacle avoidance between multi-agent, and causes safety accidents. Therefore, according to the selected candidate obstacle target point cloud cluster, the real-time absolute motion state estimation of the candidate obstacle is obtained based on the Kalman filter, and the speed and turning rate in the absolute motion state estimation are used to judge the state of the obstacle. At the same time, the obstacle with kinetic energy is marked and uploaded through the semantic label, so as to help the overall overall arrangement of the subsequent system. The Kalman filter can output the absolute motion state of the obstacle, and the core is that the filter operates in the geodetic coordinate system. Although the unmanned ship itself is moving, the geodetic coordinates observed by the unmanned ship for the same stationary obstacle are also changing. However, the state transition equation (uniform turning motion model) of the Kalman filter describes the motion law of the obstacle itself. In the iterative "prediction-update" process, the filter can spontaneously learn and separate the influence of the unmanned ship motion from the observation data by analyzing the coordinate change sequence in time sequence, so as to finally make the state vector converge to the real absolute motion state of the obstacle relative to the geodetic coordinate system. Therefore, by estimating the absolute motion state of the obstacle through the Kalman filter, the global situation awareness capability beyond the limitation of the coordinate system of the unmanned ship is provided, which not only can early warning and track the dynamic obstacles with clear motion, but also can identify those "static obstacles with kinetic energy" which are currently stationary but have the ability to move. Such targets are a major hidden danger in cooperative navigation, because they may suddenly start and break the safety premise of the original path planning. By uploading the state and semantic information of such targets to the cooperative decision center, the whole ship fleet can be provided with forward-looking risk warning, so as to realize the leap from passive obstacle avoidance to active risk avoidance, and fundamentally avoid the obstacle avoidance confusion and safety accidents caused by multi-agent due to sudden conditions. The pre-defined kinetic energy category set is a semantic label subset preset before system deployment, which includes the labels corresponding to all objects recognized as having autonomous moving ability, such as {ship, motorboat, engineering ship}. The set is constructed based on domain knowledge and can be configured according to task requirements. As a conservative safety strategy, unknown category labels output by the semantic segmentation network which are not in the pre-defined set are treated as obstacles with kinetic energy for processing. In the state vector initialization step, the initial values of the speed, heading angle and turning rate are set to zero. At the same time, the initial state error covariance matrix of the Kalman filter is set to be much larger than the variance term corresponding to the position state.This is a conservative engineering setting, aiming to reflect the high uncertainty of its initial value, so as to ensure that the filter prioritizes subsequent observation data and quickly converges to the true motion state. As a reference, the initial variance of the position state can be calibrated based on the joint measurement accuracy of lidar and millimeter wave radar; while the initial variance of the motion state is determined according to the prior knowledge of the maximum reasonable acceleration of the obstacle. The observation noise covariance matrix is determined according to the measurement error of the point cloud cluster geodetic coordinates. The motion determination threshold is set by the sum of 3 times the standard deviation of the ship navigation system speed measurement error and the detected current flow rate. The turning determination threshold is set according to the minimum observable turning rate of the typical ship tactical maneuver. The N1 frame can reduce the misjudgment rate through multi-frame verification, avoiding single-frame data anomalies caused by sensor interference from sea spray in complex sea conditions. The setting method is set based on the 3σ criterion through testability experiment.

[0024] The safety radius compensation required by each unmanned ship for autonomous obstacle avoidance is determined according to real-time data of each unmanned ship and the surrounding environment, which specifically includes: According to the wave direction spectrum and wave height data collected by the unmanned ship, a sea state disturbance factor representing the severity of the sea state is calculated; Based on the sea state disturbance factor and wind flow vector data, the effective maneuverability degradation coefficient of the unmanned ship under the combined action of wind, wave and current is calculated through the ship motion response model; Based on the effective maneuverability degradation coefficient, the safety radius compensation required by the unmanned ship for autonomous obstacle avoidance is calculated.

[0025] It can be explained that when the unmanned ship is avoiding obstacles, it needs to set a dynamic safety distance for obstacle avoidance according to the actual environment of the sea state, especially when multiple intelligent agents of unmanned ships are cooperating to avoid obstacles. Ensuring the dynamic safety distance of each unmanned ship is an important prerequisite for conflict. Therefore, the present scheme converts the qualitative influence of complex sea conditions into quantitative adjustment of obstacle avoidance safety radius, ensures the consistency of multi-agent cooperation of unmanned ships through unified quantitative logic, provides key environmental and capability quantitative support for subsequent cooperative path planning and conflict resolution, makes the cooperative obstacle avoidance strategy more targeted and safe, and effectively adapts to the navigation needs of unmanned ship cluster in complex sea conditions. The expression of the sea state disturbance factor representing the severity of the sea state is:

[0026] In the formula, is the sea state disturbance factor representing the severity of the sea state, is the significant wave height, is the direction with the maximum energy density in the wave direction spectrum, is the heading angle of the ship, , The weight coefficient can be set by experimental data; By effectively combining wave height (square term amplification extreme sea state influence) and the angle between wave direction and heading (downwind / upwind difference), the interference degree of sea waves on the ship is quantified; The expression of the effective maneuverability degradation coefficient of the unmanned ship under the combined action of wind, wave and current is:

[0027] In the formula, The effective maneuverability degradation coefficient of the unmanned ship under the combined action of wind, wave and current is, The wind speed collected is, The current flow rate is, 、 、 The ship type parameter is, The weight coefficient of the influence of wind speed on maneuverability reflects the sensitivity of the ship to wind load, The weight coefficient of the influence of flow rate on maneuverability reflects the sensitivity of the ship to water flow load, The amplification coefficient of overall environmental interference reflects the sensitivity of the ship type to wind, wave and current, which can be calibrated by real ship test or CFD (computational fluid dynamics) simulation data; The index can quickly reflect the attenuation degree of the ship maneuvering ability, and solves the problem that the ship motion response model output is complex and difficult to directly serve the obstacle avoidance parameter calculation; The expression of the safety radius compensation amount required for the autonomous obstacle avoidance of the unmanned ship is:

[0028] In the formula, The safety radius compensation amount required for the autonomous obstacle avoidance of the unmanned ship is, The safety radius in still water is; The effective maneuverability degradation coefficient is converted into a specific obstacle avoidance safety radius compensation amount, so as to dynamically adjust the safety radius of each unmanned ship through the safety radius compensation amount, wherein the safety radius is equal to the sum of the safety radius in still water and the safety radius compensation amount, and in particular, for the extreme sea state that may cause the safety radius compensation amount to abnormally increase, by setting The lower limit (such as μ≥0.1) is set to avoid abnormal calculation results due to excessive environmental interference.

[0029] Referring to Figure 3 , the acquisition of the safe navigable space of each unmanned ship at the current time specifically includes: According to the length, width, height of the bounding box of all static obstacles in the list, the distance of the outward expansion of the safety radius is generated to generate a static prohibited area; For each moving obstacle in the obstacle feature list, based on its real-time absolute motion state estimation output by its Kalman filter, substitute it into its uniform turning motion model, conduct forward numerical simulation, predict its position at a series of discrete time points in the future, and thus generate the predicted trajectory of the obstacle; According to the predicted trajectory of the obstacle, combined with the length, width and height of the obstacle bounding box, expand a safety radius on both sides of the trajectory center line to generate a dynamic threat corridor that changes over time; From the global environment, eliminate the static prohibited area and the dynamic threat corridor to obtain the safe navigable space of each unmanned ship at the current time.

[0030] It can be explained that the core premise of autonomous obstacle avoidance of unmanned ships is to clearly define the "safe passage boundary". The threat of static obstacles (such as rocks and anchored ships) in complex sea conditions is fixed, and the threat of dynamic obstacles (such as ships in navigation and floating bodies) will continue to change with their motion. If only relying on the current position of the obstacle, it is easy to cause obstacle avoidance failure due to the trajectory deviation of the dynamic target. Therefore, based on the obstacle feature list and the safety radius of each ship, for static obstacles, a static prohibited area is constructed by "bounding box inflation safety radius" to ensure a sufficient safety distance from the fixed obstacle. For dynamic obstacles, combined with its absolute motion state estimation (Kalman filter output) and uniform turning motion model, the trajectory in the future is predicted, and a dynamic threat corridor is expanded to form a safety radius to cover the area that may be reached in advance. Finally, from the global environment, the two types of threat areas are eliminated to obtain the safe navigable space of each unmanned ship. This scheme not only adapts to the "fixed threat" characteristics of static obstacles, but also covers the "motion threat" characteristics of dynamic obstacles. It provides accurate spatial constraints for subsequent multi-agent collaborative path planning, avoids obstacle avoidance conflicts caused by ambiguous obstacle area definition, and fully adapts to the autonomous navigation needs of unmanned ship clusters in complex sea conditions.

[0031] Referring to Figure 4 As shown in the figure, the method for constructing a global synthetic potential field to generate a set of optimal candidate paths for a group of unmanned ships from the current position to the target point specifically includes: Taking the current position of each unmanned ship as the origin, a local plane coordinate system is established based on the geodetic coordinate system, and the safe navigable space is converted to the local coordinate system to form a local search space for path search; In the local plane coordinate system, the continuous state space of the unmanned ship is discretized into a four-dimensional space-time state grid, and each node of the state grid represents a specific state. The specific state includes: the position of the unmanned ship, the heading angle of the unmanned ship, and the time from the start of path generation. Based on the physical characteristics of the unmanned ship and expert experience, node parameters are set to define a search granularity matching the physical characteristics of the unmanned ship for a specific state in the space-time state grid; In the local plane coordinate system, a global synthetic potential field is constructed, which is composed of a target attractive field and an obstacle repulsive field; The target attractive field refers to the potential energy value at any node, which is calculated by the Euclidean distance from the node to the target node; The obstacle repulsive field refers to the potential energy value at any node, which is calculated by the Euclidean distance from the node to the nearest obstacle; The global synthetic potential field is obtained by linear superposition of the target attractive field and the obstacle repulsive field; Starting from the current state node of the unmanned ship, the negative gradient direction of the node position in the synthetic potential field is calculated; Taking the negative gradient direction as the center, the candidate heading angle is sampled within the preset offset interval according to the heading angle resolution; Based on the candidate heading angle, the position coordinates of the subsequent node are calculated through the ship kinematics model combined with the current speed and time step, and the corresponding subsequent state node is generated, thereby forming a candidate polyline path node sequence; Immediately verify whether the space-time trajectory of the new node and the line segment formed with the previous node intersects with the obstacle region, and only keep the nodes without collision; Smooth each polyline path in the set of collision-free candidate polyline paths to obtain a set of smoothed candidate paths; Calculate the total geometric length of each path in the set of smoothed candidate paths, and select the candidate path with the smallest total geometric length as the optimal candidate path for the unmanned ship from the current position to the target point.

[0032] It can be explained that after the unmanned ship identifies the safe navigable space, only the optimal action route needs to be selected in the safe navigable space, in the ideal state without obstacles, only the straight line distance of the unmanned ship to the target position needs to be considered, and when there are obstacles, the geometric distance of the unmanned ship to the target position needs to be known when avoiding the obstacles is the shortest, therefore, the scheme discretizes the continuous state space of the unmanned ship into a four-dimensional space-time state grid under the local plane coordinate system, defines a search granularity matched with the physical characteristics of the unmanned ship, the position of the unmanned ship defines the spatial resolution of the position update in the path planning, limits the minimum displacement considered by the unmanned ship in a single decision cycle, and matches the ship size and inertia; the heading angle of the unmanned ship defines the minimum turning angle considered by the unmanned ship in a single decision cycle, and matches the ship turning rate;The time unit defined by the start time of path generation determines the minimum time unit for generating the polyline path, dictating the time precision with which the algorithm predicts the future motion of the ship and dynamic obstacles, and matching the system response frequency. Smaller search granularity results in more accurate predicted polyline paths, but at the cost of computational complexity and reaction time. This can be calibrated after conducting test experiments based on the physical characteristics of the unmanned vessel and expert experience. Furthermore, after generating the local planar coordinate system and search granularity, it is only necessary to use the UAV's current position as the initial point and generate several sets of polylines extending towards the target point through gridding. The proposed solution uses a polygonal path, but this path contains many redundant elements, such as intersections with obstacle areas or excessively long paths. Therefore, this solution constructs a global synthetic potential field. The target gravitational field generates a potential energy gradient pointing towards the target to ensure the path's purposefulness, while the obstacle repulsive field generates a potential energy gradient away from the obstacles to ensure path safety. The global synthetic potential field, formed by the linear superposition of these two forces, creates a unified virtual environment that simultaneously represents both "target attraction" and "obstacle repulsion." The movement of the unmanned vessel within this environment can be intuitively understood. To glide along the steepest downhill slope in the "terrain," a path that can safely avoid obstacles and efficiently approach the target is naturally found. Based on this, the spatiotemporal trajectory of new nodes and the line segments formed by them and previous nodes is checked to see if they intersect with obstacle areas (which have already been identified in previous sections). This eliminates intersecting polyline paths, significantly reducing redundant paths. Finally, by calculating the total geometric length of all paths in the smoothed candidate path set, the candidate path with the smallest total geometric length is selected as the optimal candidate route for the unmanned vessel from its current position to the target point. The path selection method does not sacrifice the real-time obstacle avoidance performance of the unmanned vessel and improves its response rate. The preset offset interval is centered on the negative gradient direction. The lower bound of the interval is the negative gradient direction minus a maximum allowable left deflection angle, and the upper bound is the negative gradient direction plus a maximum allowable right deflection angle. The maximum allowable left deflection angle and the maximum allowable right deflection angle are equal in value. The value is based on the maximum heading change that the unmanned vessel can achieve based on its maximum turning rate within a single time step. This maximum heading change is measured experimentally. The expression for the target gravitational field is as follows:

[0033] In the formula, For in position The gravitational potential energy at the target location. The gravitational gain coefficient is a positive constant. For position The Euclidean distance from the target node; The expression for the repulsive force field of the obstacle is:

[0034] In the formula, For in position The repulsive potential energy of the obstacle at the location, The repulsive gain coefficient is a positive constant, and its value is much larger than that of the gravitational gain coefficient. The threshold for the impact of obstacles; The threshold value for the obstacle impact is determined by the dynamic safety radius of the unmanned vessel and a safety margin, and its calculation formula is as follows: ,in, For the safety radius, As a safety margin, the value of the safety margin is determined through test experiments. The experiments need to be carried out separately for different types of obstacles: for static obstacles (such as reefs and fixed buoys), the unmanned surface vessel is tested under different sea conditions, starting from a distance of [missing information]. The test assesses the maximum turning rate and the ability of the unmanned surface vessel (USV) to change course and avoid obstacles within a single time step, recording the minimum safety margin. For dynamic obstacles (such as sailing vessels), the test utilizes a uniform turning motion model to determine the additional safety margin required in scenarios with relative motion to the obstacle, ensuring the trajectory of the USV is covered by the obstacle. For stationary obstacles with kinetic energy (such as anchored vessels or motorboats), the test simulates extreme scenarios of sudden acceleration, testing the required additional safety margin to address the uncertainty of the unknown starting direction. Based on the test results for these three types of obstacles, the test determines the appropriate safety margin values ​​for different scenarios, ensuring the USV maintains a safe distance from obstacles. At that time, the unmanned surface vessel (USV) is capable of successfully avoiding the obstacle within a single time step by applying a change in heading caused by its maximum turning rate. The repulsive gain coefficient is calibrated as follows: a critical obstacle avoidance scenario is set in the simulation, and the value of the repulsive gain coefficient is gradually increased until the USV can maintain a minimum distance greater than or equal to a safety threshold from the obstacle. This value is then determined as the minimum effective value of the repulsive gain coefficient. The gravitational gain coefficient is calibrated as follows: it is first set to be equal to the minimum effective value of the repulsive gain coefficient. , and then in the unobstructed scene, fine-tune the value in the range of ±20% of the value, and select the value that can make the total length of the navigation path of the unmanned ship from the starting point to the target point shortest, wherein, in the obstacle avoidance process, the target point is dynamically set as a sub-target point located behind the obstacle on the planned route, that is, in the local plane coordinate system, a straight line parallel to the current heading of the unmanned ship and tangent to the obstacle contour is drawn, and the position behind the obstacle in the direction of the planned route after the tangent point is offset by a safe backtracking distance is taken as the sub-target point, and the safe backtracking distance is determined by a test experiment, and the value is the minimum look-ahead amount required to ensure that the unmanned ship can smoothly and stably cut back to the planned route with the minimum turning radius after avoiding the obstacle.

[0035] The consensus binding algorithm-based alternative path of each unmanned ship from the current position to the target point specifically comprises: Each unmanned ship encapsulates the optimal alternative path generated by itself and the total geometric length of the path into a data packet and broadcasts it to all other unmanned ships in the cluster through wireless ad hoc networking. After receiving the data packet from other ships, each unmanned ship independently builds a global collaborative decision matrix in its local processing system. The column dimension of the collaborative decision matrix corresponds to the optimal alternative path of each unmanned ship and the total geometric length of the path. The consensus binding algorithm is used to perform distributed path combination optimization on the unmanned ship cluster, and a consensus-based multi-agent collaborative obstacle avoidance path of the unmanned ship is output. Each unmanned ship receives and executes the collaborative path assigned to it in the final path combination, and continues to travel to the final target point according to the preset formation after the obstacle avoidance is completed.

[0036] It can be explained that, as the unmanned ship multi-agent collaborative driving, after each unmanned ship generates a candidate path from the current position to the target point, it cannot be directly executed, and the individual candidate path needs to be considered whether it conflicts with the whole, therefore, the whole needs to reach a consensus to execute uniformly, therefore, the scheme broadcasts the candidate path generated by each unmanned ship and the total geometric length data packet of the path to all other unmanned ships in the cluster through wireless ad hoc network, and independently builds a global collaborative decision matrix in the local processing system of each unmanned ship, this decentralized design avoids the single point failure risk of traditional master-slave architecture, even if part of the nodes in the cluster fail, the remaining nodes can still make collaborative decisions based on the local matrix, which ensures the survival ability of the system, and through the global collaborative decision matrix, the subsequent "consensus binding optimization" step is consensus to all ships and executed in parallel, which greatly shortens the time of seeking global optimal solution, meets the stringent requirements of real-time in complex environment, finally, the consensus binding algorithm is used to optimize the distributed path combination of the unmanned ship cluster, and a consensus unmanned ship multi-agent collaborative obstacle avoidance path is output, and after the obstacle avoidance is completed, the array continues to move to the final target point according to the preset array, which ensures the consistency of the array with the initial state, so as to realize the multi-agent collaborative obstacle avoidance of unmanned ship in complex sea conditions.

[0037] The consensus binding algorithm for distributed path combination optimization of the unmanned ship cluster specifically includes: Each unmanned ship selects its own optimal candidate path to form an initial path combination; Based on the initial path combination, the closest point distance between all unmanned ships is calculated, and the unmanned ship pair with the closest point distance less than the safety distance is marked as a conflict pair and bound as a conflict cluster; Each unmanned ship in the conflict cluster acts as a bidder and selects a replacement path from its candidate path set, which has a closest point distance greater than the safety distance and the shortest total geometric length; Calculate the reduction of the total geometric length of the whole cluster after using the replacement path, that is, the cost improvement value, and broadcast the improvement value as a tender; All unmanned ships receive the tender and independently select the tender with the largest cost improvement value as the winning tender; Update the path combination according to the winning tender and update the conflict cluster, repeat the process until all conflict pairs are eliminated or the maximum number of iterations is reached, at which time the output path combination is the collaborative obstacle avoidance path combination.

[0038] It can be explained that in the cooperative obstacle avoidance scene of the unmanned ship cluster, the local path generated by each ship independently can avoid environmental obstacles, but it is easy to cause sailing conflicts between ships (such as meeting and crossing meeting), therefore, the global consensus at the cluster level is indispensable, instead of static formation division of the ship formation, the scheme dynamically divides each ship with a suitable obstacle avoidance path through the consensus binding algorithm, thereby logically forming a cooperative and orderly traffic sequence or a shunt group, and the process strictly follows the principle of the shortest total geometric length of the cluster path (complementary to the local optimal target of single ship path selection), aiming to ensure the optimal global sailing efficiency, and through distributed bidding and local consensus, the complex global conflict resolution problem is decomposed into a series of local conflict resolution proposals that can be processed in parallel by each ship, so that the system can quickly converge to a consensus agreed by all ships, a conflict-free cooperative obstacle avoidance path combination, thereby eliminating the inherent single-point failure risk of the central controller, improving the robustness and survivability of the system, at the same time, through the information broadcasting and local decision-making mode, the distributed communication characteristics of the wireless ad hoc network (Ad-hoc) are matched, the dependence on high-bandwidth and low-latency backbone communication networks is effectively avoided, and then the complex sea condition operation scene with limited communication conditions and variable environment is adapted.

[0039] Further, based on the same inventive concept as the above unmanned ship multi-agent cooperative obstacle avoidance method for complex sea conditions, the scheme proposes an unmanned ship multi-agent cooperative obstacle avoidance system for complex sea conditions, comprising: A data acquisition module, the data acquisition module is used to acquire real-time data of each unmanned ship and the surrounding environment through a multi-sensor fusion system carried on each unmanned ship; A navigable space module, the navigable space module is used to filter candidate obstacle target point cloud clusters based on a density clustering algorithm according to the surrounding obstacle point cloud data, and generate a candidate obstacle target point cloud cluster list; based on a Kalman filter, the real-time absolute motion state estimation of the candidate obstacle is obtained according to the filtered candidate obstacle target point cloud cluster, and an obstacle feature list is output; according to the real-time data of each unmanned ship and the surrounding environment, the safety radius compensation required for autonomous obstacle avoidance of each unmanned ship is determined; according to the obstacle feature list, in combination with the safety radius required for autonomous obstacle avoidance of each unmanned ship, the safety navigable space of each unmanned ship at the current time is obtained; A cooperative obstacle avoidance module, the cooperative obstacle avoidance module is used for each unmanned ship to construct a global synthetic potential field based on its safety navigable space, and generate a set of optimal candidate paths of the unmanned ship from the current position to the target point; based on the consensus binding algorithm, a consensus reached unmanned ship multi-agent cooperative obstacle avoidance path is obtained according to the candidate path of each unmanned ship from the current position to the target point; The navigable space module specifically comprises: The point cloud cluster unit is configured to filter candidate obstacle target point cloud clusters based on a density clustering algorithm according to the surrounding obstacle point cloud data, and generate a candidate obstacle target point cloud cluster list. The obstacle feature unit is configured to obtain real-time absolute motion state estimation of the candidate obstacle based on a Kalman filter according to the filtered candidate obstacle target point cloud cluster, and output an obstacle feature list. The safety radius unit is configured to determine a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship according to real-time data of each unmanned ship and the surrounding environment. The navigable space unit is configured to obtain the safety navigable space of each unmanned ship at the current time according to the obstacle feature list and the safety radius required for autonomous obstacle avoidance of each unmanned ship. The cooperative obstacle avoidance module specifically comprises: The optimal path unit is configured to construct a global synthetic potential field based on the safety navigable space of each unmanned ship, and generate a set of optimal candidate paths of the unmanned ship from the current position to the target point. The cooperative obstacle avoidance unit is configured to obtain a consensus unmanned ship multi-agent cooperative obstacle avoidance path based on a consensus binding algorithm according to the candidate paths of each unmanned ship from the current position to the target point.

[0040] In summary, the present application has the advantages of realizing safe, efficient and cooperative intelligent obstacle avoidance of multi-unmanned ship clusters in real complex marine environments.

[0041] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A multi-agent collaborative obstacle avoidance method for unmanned ships in complex sea conditions, characterized in that, include: Real-time data of each unmanned vessel and its surrounding environment are acquired through a multi-sensor fusion system mounted on each unmanned vessel. Based on the surrounding obstacle point cloud data, a density clustering algorithm is used to filter candidate obstacle target point cloud clusters and generate a list of candidate obstacle target point cloud clusters. Based on the selected candidate obstacle target point cloud clusters, the real-time absolute motion state estimation of the candidate obstacles is obtained using a Kalman filter, and a list of obstacle features is output. Based on real-time data of each unmanned vessel and its surrounding environment, determine the safety radius compensation required for each unmanned vessel to autonomously avoid obstacles. Based on the list of obstacles and the safe radius required for each unmanned vessel to autonomously avoid obstacles, the safe navigation space of each unmanned vessel at the current moment is obtained. Each unmanned surface vessel (USV) constructs a global synthetic potential field based on its own safe navigation space, generating a set of optimal alternative paths from its current position to the target point. Based on the alternative paths for each unmanned surface vessel (USV) from its current location to the target point, a consensus-based binding algorithm is used to obtain a multi-agent cooperative obstacle avoidance path for USVs.

2. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 1, characterized in that, The acquisition of real-time data on each unmanned vessel and its surrounding environment through a multi-sensor fusion system mounted on each unmanned vessel specifically includes: The real-time data includes: the position and attitude status of each unmanned vessel, point cloud data of surrounding obstacles, wave direction spectrum and wave height data, and wind vector data; The multi-sensor fusion system includes: an inertial measurement unit, a GPS positioning module, a lidar, a millimeter-wave radar, a visible light / infrared vision sensor, a wave sensor, and a wind speed and direction sensor; The inertial measurement unit and GPS positioning module are combined to collect the ship's real-time position and attitude status, including longitude, latitude, heading angle, speed and roll / pitch / heave angles; LiDAR and millimeter-wave radar work together to scan the surrounding environment and acquire raw point cloud data of obstacles in the surrounding area, while visible light / infrared vision sensors assist in acquiring image features of obstacles; Wave sensors collect wave direction spectrum, significant wave height and wave period data based on spectral analysis technology, while wind speed and direction sensors collect instantaneous wind speed, wind direction and ocean current speed and direction data; All data collected by the sensors are spatiotemporally aligned, and the time dimension of the data from each sensor is unified based on the GPS timestamp. The Kalman filter algorithm is used to smooth the coordinate-aligned sensor data, remove outliers, and normalize the filtered data to the [0,1] interval. Each unmanned vessel shares its pre-processed real-time data with the fleet data center via a wireless communication network.

3. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 2, characterized in that, The step of filtering candidate obstacle target point cloud clusters based on surrounding obstacle point cloud data using a density clustering algorithm and generating a list of candidate obstacle target point cloud clusters specifically includes: Simultaneously acquire 3D spatial point cloud data provided by lidar and target reflection point cloud data provided by millimeter-wave radar; The three-dimensional spatial point cloud data and the target reflection point cloud data are uniformly transformed into the body coordinate system centered on the unmanned vessel through coordinate transformation. Under a unified coordinate system, the point clouds of lidar and millimeter-wave radar are correlated to generate a preliminary fused point cloud covering spatial and reflection characteristics. The raw image collected by the visible light / infrared vision sensor is input into a pre-trained semantic segmentation neural network to assign a semantic label to each pixel and generate a semantic label image; The fused point cloud data is input into a density clustering algorithm, and the algorithm identifies a set of point clouds that are closely distributed in space and have a density reaching a preset threshold a1 as an independent point cloud cluster; All spatially isolated points that fail to be classified into any point cloud cluster are determined as sensor noise or transient clutter and filtered out; The initially formed point cloud cluster is projected onto the semantic label image, the semantic label distribution of the image area covered by each point cloud cluster is counted, and if the proportion of the sea wave label is not less than a preset threshold a2, the cluster is determined as a sea wave false target and filtered out from the candidate target list; The three-dimensional bounding box size of the remaining point cloud cluster is calculated, and the cluster whose size exceeds the preset physical range [a3, a4] is filtered out, and a list of candidate obstacle target point cloud clusters filtered out is output; The geometric center coordinates in the list of candidate obstacle target point cloud clusters filtered out are converted from the body coordinate system centered on the unmanned ship to the geodetic coordinate system.

4. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 3, characterized in that, The real-time absolute motion state estimation of the candidate obstacle is obtained based on the Kalman filter according to the filtered candidate obstacle target point cloud cluster, and an obstacle feature list is output, which specifically includes: A Kalman filter is initialized for each newly identified candidate obstacle, wherein the state vector of the Kalman filter includes the geodetic coordinates, velocity, heading angle, and turning rate of the candidate obstacle point cloud cluster; Based on the uniform speed turning motion model, the system state transition equation of the Kalman filter is constructed, and the geodetic coordinates of the current frame candidate obstacle point cloud cluster are taken as the observation vector; Based on the Kalman filter, the updated state vector is output as the real-time and optimal absolute motion state estimation of the corresponding obstacle; According to the velocity in the absolute motion state estimation output by the Kalman filter; Motion judgment threshold and turning judgment threshold are set, and the state of the obstacle is judged based on the velocity value and the turning rate, wherein the state of the obstacle refers to static or motion; According to the absolute motion state estimation of the obstacle, the relative distance, relative speed, and relative heading of the obstacle relative to the unmanned ship are obtained through vector operation based on the geodetic coordinates and heading angle of the unmanned ship; According to the absolute motion state estimation of the obstacle, the relative distance, relative speed, and relative heading of the obstacle relative to the unmanned ship, and the state of the obstacle and its corresponding semantic label, an obstacle feature list is constructed; The state of the obstacle specifically includes: If the absolute velocity value is continuously not less than the motion judgment threshold for N1 frames, or the absolute value of the absolute turning rate is continuously not less than the turning judgment threshold for N1 frames, the obstacle is determined as a moving obstacle, otherwise, the obstacle is determined as a static obstacle; When the obstacle is determined as a static obstacle, if its semantic label belongs to a predefined set of categories with kinetic energy, it is marked as a static obstacle with kinetic energy.

5. The multi-agent collaborative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 4, characterized in that, The safety radius compensation required for autonomous obstacle avoidance of each unmanned ship is determined according to the real-time data of each unmanned ship and the surrounding environment, which specifically includes: According to the sea wave direction spectrum and wave height data collected by the unmanned ship, a sea state disturbance factor representing the severity of the sea state is calculated; Based on the sea state disturbance factor and the wind flow vector data, the effective maneuverability degradation coefficient of the unmanned ship under the combined action of wind, wave and current is calculated through a ship motion response model; Based on the effective maneuverability degradation coefficient, the safety radius compensation required for the autonomous obstacle avoidance of the unmanned ship is calculated.

6. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 5, characterized in that, The safety navigable space of each unmanned ship at the current time is obtained according to the obstacle feature list and the safety radius required for the autonomous obstacle avoidance of each unmanned ship, and specifically includes: According to the length, width and height of the bounding box of all static obstacles in the list, the safety radius is expanded outward by a certain distance to generate a static prohibited area; For each moving obstacle in the obstacle feature list, based on the real-time absolute motion state estimation output by the Kalman filter, the uniform speed turning motion model is substituted to perform forward numerical simulation to predict the positions of the obstacle at a series of discrete time points in the future, thereby generating a predicted trajectory of the obstacle; According to the predicted trajectory of the obstacle, combined with the length, width and height of the obstacle bounding box, a dynamic threat corridor that changes over time is generated by expanding a safety radius on both sides of the trajectory center line; The static prohibited area and the dynamic threat corridor are removed from the global environment to obtain the safety navigable space of each unmanned ship at the current time.

7. The multi-agent collaborative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 6, characterized in that, Based on the safety navigable space of each unmanned ship, a global synthetic potential field is constructed to generate a set of optimal candidate paths of the unmanned ship from the current position to the target point, and specifically includes: A local plane coordinate system is established based on the geodetic coordinate system with the current position of each unmanned ship as the origin, the safety navigable space is converted to the local coordinate system to form a local search space for path search; In the local plane coordinate system, the continuous state space of the unmanned ship is discretized into a four-dimensional space-time state grid, and each node of the state grid represents a specific state, including the position of the unmanned ship, the heading angle of the unmanned ship, and the time from the start of path generation; Based on the physical characteristics of the unmanned ship and expert experience, node parameters are set to define a search granularity for the specific state in the space-time state grid that matches the physical characteristics of the unmanned ship; In the local plane coordinate system, a global synthetic potential field is constructed, which is composed of a target attractive field and an obstacle repulsive field; The target attractive field refers to the potential energy value at any node, which is calculated by the Euclidean distance from the node to the target node; The obstacle repulsive field refers to the potential energy value at any node, which is calculated by the Euclidean distance from the node to the nearest obstacle; The global synthetic potential field is obtained by linearly superimposing the target attractive field and the obstacle repulsive field; Starting from the current state node of the unmanned ship, the negative gradient direction of the node position in the synthetic potential field is calculated; Taking the negative gradient direction as the center, a candidate heading angle is generated by sampling within a preset offset interval with a heading angle resolution; Based on the candidate heading angle, combined with the current speed and time step, the position coordinates of the subsequent nodes are calculated through the ship kinematics model to generate the corresponding subsequent state nodes, thereby forming a candidate polyline path node sequence; Immediately verify whether the spatiotemporal trajectory of the new node and the line segment formed by the previous node intersects with the obstacle area, and only retain nodes that do not collide; Smooth each polyline path in the set of collision-free candidate polyline paths to obtain a smoothed set of candidate paths. Calculate the total geometric length of each path in the smoothed candidate path set, and select the candidate path with the smallest total geometric length as the optimal candidate path for the unmanned vessel to travel from its current position to the target point.

8. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 7, characterized in that, The step of obtaining a consensus-based multi-agent cooperative obstacle avoidance path for each unmanned surface vessel (USV) based on its alternative paths from its current location to the target point, using a consensus-based binding algorithm, specifically includes: Each unmanned surface vessel encapsulates its generated optimal alternative path and the total geometric length of that path into a data packet, and broadcasts it to all other unmanned surface vessels in the cluster via a wireless ad hoc network. After receiving data packets from other vessels, each unmanned vessel independently constructs a global collaborative decision matrix in its local processing system. Wherein, the row dimension of the collaborative decision matrix corresponds to the number of each unmanned vessel in the cluster, and the column dimension corresponds to the optimal alternative path and the total geometric length of the path for each vessel. A consensus-based binding algorithm is used to optimize the distributed path combination of unmanned surface vessels (USVs) swarms, and outputs a consensus-based multi-agent cooperative obstacle avoidance path for USVs. Each unmanned vessel receives and executes the cooperative path assigned to it in the final path combination, and continues to travel towards the final target point in a preset formation after obstacle avoidance.

9. The multi-agent cooperative obstacle avoidance method for unmanned ships in complex sea conditions according to claim 8, characterized in that, The aforementioned distributed path combination optimization of the unmanned vessel swarm using a consensus-based binding algorithm specifically includes: Each unmanned vessel selects its own optimal alternative path to form an initial path combination; Based on the initial path combination, calculate the closest point distance between all pairs of unmanned vessels, and mark the pairs of unmanned vessels whose closest point distance is less than the safe distance as conflict pairs and bundle them into a conflict cluster; Each unmanned vessel within the conflict cluster acts as a bidder, selecting from its own set of alternative paths the closest point with a distance greater than the safe distance and the shortest total geometric length. Calculate the reduction in the total geometric length of the entire cluster path after adopting this alternative path, i.e., the cost improvement value, and broadcast this improvement value as a tender. All unmanned surface vessels (USVs) receive bids and independently select the bid with the highest cost improvement value as the winning bid. Update the path combination based on the winning bid and update the conflict cluster. Repeat this process until all conflict pairs are eliminated or the maximum number of iterations is reached. The output path combination at this point is the cooperative obstacle avoidance path combination.

10. A multi-agent collaborative obstacle avoidance system for unmanned ships in complex sea conditions, characterized in that, To implement the multi-agent cooperative obstacle avoidance method for unmanned vessels in complex sea conditions as described in any one of claims 1-9, the method includes: The data acquisition module is used to acquire real-time data of each unmanned vessel and its surrounding environment through a multi-sensor fusion system mounted on each unmanned vessel. The navigable space module is used for screening candidate obstacle target point cloud clusters based on a density clustering algorithm according to peripheral obstacle point cloud data, and generating a candidate obstacle target point cloud cluster list; obtaining real-time absolute motion state estimation of a candidate obstacle based on a Kalman filter according to the screened candidate obstacle target point cloud clusters, and outputting an obstacle feature list; determining a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship according to real-time data of each unmanned ship and a surrounding environment; and obtaining safety navigable spaces of each unmanned ship at a current time according to the obstacle feature list and in combination with the safety radius required for autonomous obstacle avoidance of each unmanned ship; The cooperative obstacle avoidance module is used for each unmanned ship to construct a global synthetic potential field based on the safety navigable space of the unmanned ship, to generate a group of optimal candidate paths of the unmanned ship from a current position to a target point; and to obtain a consensus reached unmanned ship multi-agent cooperative obstacle avoidance path based on a consensus binding algorithm according to the candidate paths of each unmanned ship from the current position to the target point. The navigable space module specifically comprises: A point cloud cluster unit is used for screening candidate obstacle target point cloud clusters based on a density clustering algorithm according to peripheral obstacle point cloud data, and generating a candidate obstacle target point cloud cluster list; An obstacle feature unit is used for obtaining real-time absolute motion state estimation of a candidate obstacle based on a Kalman filter according to the screened candidate obstacle target point cloud clusters, and outputting an obstacle feature list; A safety radius unit is used for determining a safety radius compensation required for autonomous obstacle avoidance of each unmanned ship according to real-time data of each unmanned ship and a surrounding environment; A navigable space unit is used for obtaining safety navigable spaces of each unmanned ship at a current time according to the obstacle feature list and in combination with the safety radius required for autonomous obstacle avoidance of each unmanned ship; The cooperative obstacle avoidance module specifically comprises: An optimal path unit is used for each unmanned ship to construct a global synthetic potential field based on the safety navigable space of the unmanned ship, to generate a group of optimal candidate paths of the unmanned ship from a current position to a target point; A cooperative obstacle avoidance unit is used for obtaining a consensus reached unmanned ship multi-agent cooperative obstacle avoidance path based on a consensus binding algorithm according to the candidate paths of each unmanned ship from the current position to the target point.

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