Autonomous collaborative search method for multiple unmanned ships facing island reef sea area

Abstract

The invention relates to the technical field of unmanned ship control and multi-agent collaboration, in particular to a multi-unmanned ship collaborative search method for island reef sea areas, which comprises the following steps of: constructing an island reef environment perception map containing obstacles and a search area grid, and dividing a monitoring area into a plurality of sub-areas according to a fixed grid scale; defining a sub-region state variable and initializing a task allocation table; constructing a region distribution mechanism based on distance and state judgment; a local obstacle avoidance enhanced propulsion control algorithm is established, the propulsion speed of the unmanned ship is synthesized by the target guiding speed and the obstacle avoidance speed, and the synthesized speed meets the power constraint of the unmanned ship; setting a search completion judgment mechanism and a region state updating rule; and establishing a search task degradation scheduling mechanism. The method disclosed by the invention has the advantages of high flexibility, clear division of labor and high autonomy, can effectively improve the searching efficiency of complex sea areas, and has wide application prospects in unmanned system formation searching, disaster emergency inspection and offshore information acquisition.

Description

Technical Field

[0001] This invention relates to the field of unmanned surface vessel (USV) control and multi-agent cooperative technology, specifically to a multi-USV cooperative search method for island and reef waters. Background Technology

[0002] In recent years, multi-agent unmanned surface vessel (USV) systems have shown broad application prospects in marine engineering and autonomous system control research, particularly in complex marine environments such as islands and reefs. Relying on multiple USV systems operating collaboratively to perform search missions has become an important means of improving mission efficiency and system coverage. However, due to the dense distribution of obstacles in island and reef waters, the complex hydrodynamic environment, and the high time sensitivity of missions, achieving an efficient and stable search mechanism within a multi-USV system remains a core challenge in current research.

[0003] Existing research has proposed various methods for multi-robot cooperative search, region partitioning algorithms, and task allocation strategies. For example, methods based on Voronoi partitioning, grid maps, and information gain-driven approaches are used for environment modeling and subtask partitioning; centralized and distributed scheduling mechanisms are used for multi-robot task allocation and conflict avoidance; and some literature improves the robustness of the system in dynamic environments through reinforcement learning and adaptive cooperative mechanisms. However, most of these methods are based on the idealized open water assumption and lack the ability to model and adapt scheduling to the actual obstacle structures, path constraints, and task resource conflicts in island and reef environments.

[0004] Furthermore, during the operation of multi-vessel systems, problems such as search stagnation and resource waste can easily occur due to asynchronous search area states, frequent task conflicts, or path advancement failures. Although some scheduling algorithms have introduced task locking, path obstacle avoidance, or search priority strategies, there is still a lack of a comprehensive search mechanism framework that is oriented towards complex area structures and has dynamic fault tolerance and degradation capabilities. This largely restricts the actual deployment effectiveness of multi-unmanned surface vessel systems in complex sea area missions.

[0005] Therefore, constructing a multi-unmanned surface vessel search mechanism suitable for island and reef waters, with regional modeling capabilities, scheduling degradation mechanisms, and obstacle avoidance propulsion control functions, is of great theoretical and engineering significance for improving the stability, coverage efficiency, and response capability of the system in actual marine missions. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention proposes a collaborative search method for multiple unmanned surface vessels (USVs) in island and reef waters. This method takes into account environmental modeling, task allocation, obstacle avoidance propulsion, and state fault tolerance. In actual deployment, it has high efficiency, collaboration, and stability, and is applicable to various scenarios such as marine inspection, target search, and disaster survey.

[0007] This invention provides a collaborative search method for multiple unmanned surface vessels (USVs) in island and reef waters, comprising the following steps: Construct an island and reef environment perception map and search area grid containing obstacles. The obstacles are generated into polygonal structures by perturbation circles and constructed using the polyshape function. The overall monitoring area is divided into several sub-regions according to a fixed grid scale. Each sub-region is identified by its geometric center coordinates. The obstacle status of the sub-region is recorded. If the sub-region contains obstacles, it is recorded as 1. If the sub-region does not contain obstacles, it is recorded as 0. Define sub-region state variables and initialize the task allocation table. The sub-region state variables include search status and lock flag. The task allocation table includes sub-region ID, center coordinates, state variables and allocation timestamp. During initialization, the search status and lock flag of all non-obstacle sub-regions are set to 0 and stored in the distributed storage module for access by multiple unmanned surface vessels. A region allocation mechanism based on distance and status judgment is constructed. The unmanned surface vessel periodically triggers the allocation process, filters out sub-regions with search status, lock flag and obstacle status of 0, selects the sub-region closest to its current position as the target through Euclidean distance calculation, and immediately sets the lock flag of the sub-region to 1 after allocation and synchronizes it to the global task allocation table. A propulsion control algorithm with enhanced local obstacle avoidance is established. The propulsion speed of the unmanned surface vessel is synthesized from the target propulsion speed and the obstacle avoidance repulsion speed. The synthesized speed must meet the dynamic constraints of the unmanned surface vessel. Set up a search completion judgment mechanism and area status update rules. When the unmanned surface vessel enters the target sub-area, its dwell time is continuously monitored through positioning data. If the dwell time reaches the preset threshold, the search is judged to be completed, the search status of the sub-area is set to 1, and the lock flag is reset to 0. If the search is not completed after more than twice the dwell time threshold, the search status and lock flag of the sub-area are forcibly reset to 0 and the area is released. Establish a degraded scheduling mechanism for search tasks. When the unmanned surface vessel (USV) cannot find a sub-region that meets the requirements of search status, lock-on flag, and obstacle status of 0 in the current allocation round, the degraded scheduling strategy is triggered. Only sub-regions with search status and obstacle status of 0 are selected and the nearest region is allocated. If there is duplicate allocation, the USV will automatically switch to the next nearest region. The degraded scheduling strategy will stop when a sub-region that meets the requirements of search status, lock-on flag, and obstacle status of 0 is found.

[0008] Furthermore, the method for constructing the search region grid is as follows:

[0009] in, The entire unmanned surface vessel (USV) search area is represented using a two-dimensional grid. Indicates the first Each sub-region is the basic unit of search. Represents the geometric center coordinates of the sub-region, defined in the inertial coordinate system. Indicates the search status of the current area. This indicates whether the sub-region is an obstacle region.

[0010] Furthermore, the sub-region state variables include the search state and the locking flag, where the search state is:

[0011] in, Indicates the first The current search status of each sub-region, and the specific meanings of the values ​​include: This indicates that the area has not yet been searched by any unmanned surface vessels. This indicates that the area is currently being searched by an unmanned surface vessel. This indicates that the area has been successfully searched and the completion criteria have been met; The locking flag for the sub-region state variable is:

[0012] in This indicates that the area has been selected as a search target by a certain unmanned surface vessel (USV) and is in the task assignment state; other USVs cannot assign it again. This indicates that the area can be assigned to any unmanned surface vessel that meets the conditions for searching.

[0013] Furthermore, based on its current position, sub-region state variables, and obstacle states, the optimal search target area is dynamically allocated to each unmanned surface vessel participating in the search mission. The specific allocation strategy is as follows:

[0014] in, This indicates the target sub-region number assigned to the current unmanned surface vessel. Indicates the current position coordinates of the unmanned surface vessel. and the The center point coordinates of each sub-region The Euclidean distance between them This indicates that the area has not yet been searched. This indicates that the area is passable. This indicates that the area has not yet been locked by other unmanned surface vessels.

[0015] Furthermore, the propulsion control algorithm is composed of the kinematic equations of the unmanned surface vessel and the obstacle avoidance control strategy, including: Establish a kinematic model for the unmanned surface vessel:

[0016] in This represents the x-coordinate of the unmanned surface vessel in the inertial coordinate system. This represents the ordinate of the unmanned surface vessel in the inertial coordinate system. Indicates the heading angle. This represents the longitudinal velocity in the hull coordinate system. This represents the lateral velocity in the hull coordinate system. and Indicates angular velocity; Establish an unmanned surface vessel control dynamics model:

[0017] in Represents the mass and inertia matrix. Represents the acceleration vector. Represents the longitudinal velocity in the hull coordinate system rate of change, Represents the lateral velocity in the hull coordinate system rate of change, Represents angular velocity rate of change, Represents the hydrodynamic damping matrix. Represents the velocity state vector. This represents the longitudinal velocity in the hull coordinate system. This represents the lateral velocity in the hull coordinate system. Indicates angular velocity. Represents the control input vector. Indicates propulsion. Indicates steering torque. Indicates external interference; The obstacle avoidance control strategy uses a repulsive potential field model to construct the obstacle avoidance velocity, adds an obstacle avoidance term to the propulsion direction, and synthesizes the final velocity. :

[0018] in, This indicates the expected speed of advancement towards the target search sub-region. The obstacle avoidance velocity generated by the repulsive field of the obstacle is calculated as follows:

[0019] in, Indicates the current position of the unmanned surface vessel. Indicates the first The location of the obstacle Indicates the relationship with the first The distance to each obstacle For the first The repulsive force coefficient of each obstacle can be set as a constant or related to the importance of the obstacle. The obstacle avoidance velocity vector is oriented away from the obstacle, and its total strength is determined by each obstacle.

[0020] Furthermore, when setting up the search completion judgment mechanism, a combination of dwell time and path coverage judgment is used as the criterion for judging search completion, and the judgment conditions are as follows: Determination of time spent in the area: Unmanned surface vessel enters a search area Subsequently, if the continuous stay time in this area reaches the threshold... If the condition is met, the stationing mission in that area is considered complete. The determination formula is as follows:

[0021] in, This indicates that the unmanned surface vessel (USV) remains continuously in the sub-region. Within the time, The minimum dwell time threshold set for the task; if this condition is met, the search is considered complete. State update mechanism: If the above search completion criteria are met, the state variables for that region will be updated as follows:

[0022] If within the preset timeout period If the unmanned surface vessel fails to complete the search or abnormally exits the area, a reset operation will be performed: .

[0023] Furthermore, when the unmanned surface vessel (USV) cannot find a sub-region in the current allocation round that satisfies the search status, lock-on flag, and obstacle status of 0, a degraded scheduling strategy is triggered, as follows: Demotion allocation mechanism:

[0024] in This indicates that the area has not yet been searched. This indicates that the area is passable. This indicates that the area has not yet been locked by other unmanned surface vessels (USVs). If the current USV cannot find a sub-area that simultaneously meets the following three conditions during the mission allocation process, a downgraded mission allocation will be performed, ignoring the locking conditions, and selecting the nearest target area from the traversable areas that have not yet been searched. The formula is as follows:

[0025] in Indicates the current position coordinates of the unmanned surface vessel. and the The center point coordinates of each sub-region The Euclidean distance between them ensures that even if the selected area has been locked by other unmanned surface vessels (USVs), the current USV is allowed to proceed to perform the search mission, thus ensuring the mission can continue to advance in the event of resource conflicts.

[0026] Compared with the prior art, the present invention has the following beneficial effects: By constructing a gridded environmental perception map and a state management mechanism, structured modeling of densely obstructed sea areas with islands and reefs was achieved. Combined with a dynamic task allocation mechanism based on distance and state (search status, lock markers, obstacle markers), search targets can be intelligently and evenly allocated to multiple unmanned surface vessels, significantly reducing task overlap and resource idleness, and ensuring efficient progress and full coverage of search tasks under complex geographical conditions.

[0027] The system innovatively introduces a degraded scheduling mechanism and a forced release rule. When normal task allocation conditions cannot be met, the system can automatically relax constraints to maintain task continuity, effectively avoiding system "deadlock" or overall shutdown caused by local conflicts or single-vessel failures. At the same time, through dwell time monitoring and timeout forced reset, the system can automatically handle task execution anomalies, ensuring the long-term stability and self-recovery capability of the system.

[0028] An enhanced propulsion control algorithm integrating target guidance and local obstacle avoidance was established. This algorithm vector-synthesizes the navigation velocity pointing towards the target with the obstacle avoidance velocity based on the repulsive potential field, enabling the unmanned surface vessel to smoothly and in real time avoid static obstacles while advancing towards the target area. This significantly improves the safety and path reliability of navigation in obstacle-dense sea areas such as islands and reefs.

[0029] By employing a distributed storage and synchronized global task allocation table, combined with sub-region "lock-release" state management, distributed collaboration and information sharing among multiple unmanned surface vessels (USVs) are achieved. This design avoids the communication bottlenecks and single-point-of-failure risks that centralized control may bring, and effectively prevents task conflicts through lightweight coordination mechanisms (such as locking flags), thus optimizing the overall resource utilization of the multi-USV system.

[0030] This invention provides a complete technical closed loop from environmental modeling, task allocation, motion control to state updates. This method can be widely applied to various practical mission scenarios such as marine patrol, target search, disaster emergency reconnaissance, and nearshore information collection. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart of a multi-unmanned surface vessel autonomous cooperative search method for island and reef sea areas according to the present invention.

[0033] Figure 2 This is a schematic diagram of the region division and state initialization structure in the search task of this invention.

[0034] Figures 3(a) and 3(b) are schematic diagrams of path advancement and target coverage in the multi-unmanned surface vessel cooperative search simulation task of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] like Figure 1 As shown, this invention discloses an autonomous cooperative search method for multiple unmanned surface vessels (USVs) in island and reef waters, which specifically includes the following steps: S1: Construct an environmental perception map of the island and reef containing obstacles and a search area grid. The obstacles are generated into polygonal structures by perturbation circles and constructed using the polyshape function. The overall monitoring area is divided into several sub-regions according to a fixed grid scale. Each sub-region is identified by its geometric center coordinates. The obstacle status of the sub-region is recorded. If the sub-region contains obstacles, it is recorded as 1; if the sub-region does not contain obstacles, it is recorded as 0.

[0038] As a preferred embodiment of this application, such as Figure 2 As shown, the system performs spatial modeling of the task area and divides the entire sea area into several sub-regions. The method for constructing the search area grid is as follows:

[0039] in, The entire unmanned surface vessel (USV) search area is represented using a two-dimensional grid. Indicates the first Each sub-region is the basic unit of search. Represents the geometric center coordinates of the sub-region, defined in the inertial coordinate system. Indicates the search status of the current area. This indicates whether the sub-region is an obstacle region.

[0040] S2: Define sub-region state variables and initialize the task allocation table. The sub-region state variables include search status and lock flag. The task allocation table includes sub-region ID, center coordinates, state variables and allocation timestamp. During initialization, the search status and lock flag of all non-obstacle sub-regions are set to 0 and stored in the distributed storage module for access by multiple unmanned surface vessels.

[0041] In a preferred embodiment of this application, the sub-region state variables include a search state and a locking flag, wherein the search state is:

[0042] in, Indicates the first The current search status of each sub-region, and the specific meanings of the values ​​include: This indicates that the area has not yet been searched by any unmanned surface vessels. This indicates that the area is currently being searched by an unmanned surface vessel. This indicates that the area has been successfully searched and the completion criteria have been met.

[0043] The locking flag for the sub-region state variable is:

[0044] in This indicates that the area has been selected as a search target by a certain unmanned surface vessel (USV) and is in the task assignment state; other USVs cannot assign it again. This indicates that the area can be assigned to any unmanned surface vessel that meets the conditions for searching.

[0045] S3: Construct a region allocation mechanism based on distance and status judgment. The unmanned surface vessel periodically triggers the allocation process, filters out sub-regions with search status, lock flag and obstacle status of 0, selects the sub-region closest to its current position as the target through Euclidean distance calculation, immediately sets the lock flag of the sub-region to 1 after allocation, and synchronizes it to the global task allocation table.

[0046] As a preferred implementation of this application, based on its current position, sub-region state variables, and obstacle state, an optimal search target area is dynamically allocated to each unmanned surface vessel participating in the search mission. The specific allocation strategy is as follows:

[0047] in, This indicates the target sub-region number assigned to the current unmanned surface vessel. Indicates the current position coordinates of the unmanned surface vessel. and the The center point coordinates of each sub-region The Euclidean distance between them This indicates that the area has not yet been searched. This indicates that the area is passable. This indicates that the area has not yet been locked by other unmanned surface vessels.

[0048] S4: Establish a propulsion control algorithm with enhanced local obstacle avoidance. The propulsion speed of the unmanned surface vessel is synthesized from the target propulsion speed and the obstacle avoidance repulsion speed. The synthesized speed must meet the dynamic constraints of the unmanned surface vessel.

[0049] As a preferred embodiment of this application, the propulsion control algorithm is composed of the kinematic equations of the unmanned surface vessel and an obstacle avoidance control strategy, including: Establish a kinematic model for the unmanned surface vessel:

[0050] in This represents the x-coordinate of the unmanned surface vessel in the inertial coordinate system. This represents the ordinate of the unmanned surface vessel in the inertial coordinate system. Indicates the heading angle. This represents the longitudinal velocity in the hull coordinate system. This represents the lateral velocity in the hull coordinate system. and It represents angular velocity.

[0051] Establish an unmanned surface vessel control dynamics model:

[0052] in Represents the mass and inertia matrix. Represents the acceleration vector. Represents the longitudinal velocity in the hull coordinate system rate of change, Represents the lateral velocity in the hull coordinate system rate of change, Represents angular velocity rate of change, Represents the hydrodynamic damping matrix. Represents the velocity state vector. This represents the longitudinal velocity in the hull coordinate system. This represents the lateral velocity in the hull coordinate system. Indicates angular velocity, Represents the control input vector. Indicates propulsion. Indicates steering torque. It indicates external interference.

[0053] The obstacle avoidance control strategy uses a repulsive potential field model to construct the obstacle avoidance velocity, adds an obstacle avoidance term to the propulsion direction, and synthesizes the final velocity. :

[0054] in, This indicates the expected speed of advancement towards the target search sub-region. The obstacle avoidance velocity generated by the repulsive field of the obstacle is calculated as follows:

[0055]

[0056] in, Indicates the current position of the unmanned surface vessel. Indicates the coordinates of the center of the assigned region. The weighting coefficient representing the propulsion speed. Indicates the first The location of the obstacle Indicates the relationship with the first The distance to each obstacle For the first The repulsive force coefficient of each obstacle can be set as a constant or related to the importance of the obstacle. The obstacle avoidance velocity vector is oriented away from the obstacle, and its total strength is determined by each obstacle.

[0057] S5: Set the search completion judgment mechanism and area status update rules. When the unmanned surface vessel enters the target sub-area, its dwell time is continuously monitored through positioning data. If the dwell time reaches the preset threshold, the search is judged to be completed, the search status of the sub-area is set to 1, and the lock flag is reset to 0. If the search is not completed after more than twice the dwell time threshold, the search status and lock flag of the sub-area are forcibly reset to 0 and the area is released.

[0058] In a preferred embodiment of this application, when setting the search completion determination mechanism, a combination of dwell time and path coverage determination is used as the criterion for determining search completion, and the determination conditions are as follows: Determination of time spent in the area: Unmanned surface vessel enters a search area Subsequently, if the continuous stay time in this area reaches the threshold... If the condition is met, the stationing mission in that area is considered complete. The determination formula is as follows:

[0059] in, This indicates that the unmanned surface vessel (USV) remains continuously in the sub-region. Within the time, The minimum dwell time threshold set for the task; if this condition is met, the search is considered complete.

[0060] State update mechanism: If the above search completion criteria are met, the state variables for that region will be updated as follows:

[0061] If within the preset timeout period If the unmanned surface vessel fails to complete the search or abnormally exits the area, a reset operation will be performed: .

[0062] S6: Establish a degraded scheduling mechanism for search tasks. When the unmanned surface vessel cannot find a sub-region that meets the requirements of search status, lock-on flag, and obstacle status of 0 in the current round of allocation, the degraded scheduling strategy is triggered. Only sub-regions with search status and obstacle status of 0 are selected and the nearest region is allocated. If there is duplicate allocation, the next allocation vessel will automatically switch to the next nearest region. The degraded scheduling strategy will stop when a sub-region that meets the requirements of search status, lock-on flag, and obstacle status of 0 is found.

[0063] As a preferred implementation of this application, when the unmanned surface vessel cannot find a sub-region in the current allocation round that satisfies the search state, lock flag, and obstacle state of 0, a degraded scheduling strategy is triggered, the specific strategy of which is as follows: Demotion allocation mechanism:

[0064] in This indicates that the area has not yet been searched. This indicates that the area is passable. This indicates that the area has not yet been locked by other unmanned surface vessels (USVs). If the current USV cannot find a sub-area that simultaneously meets the following three conditions during the mission allocation process, a downgraded mission allocation will be performed, ignoring the locking conditions, and selecting the nearest target area from the traversable areas that have not yet been searched. The formula is as follows:

[0065] in Indicates the current position coordinates of the unmanned surface vessel. and the The center point coordinates of each sub-region The Euclidean distance between them ensures that even if the selected area has been locked by other unmanned surface vessels (USVs), the current USV is allowed to proceed to perform the search mission, thus ensuring the mission can continue to advance in the event of resource conflicts.

[0066] This invention proposes a collaborative search method for multiple unmanned surface vessels (USVs) in island and reef waters, which can significantly improve the collaborative search efficiency, environmental adaptability and operational stability of multiple USVs in complex island and reef waters, and is applicable to scenarios such as marine inspection and target search.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for autonomous cooperative search of multiple unmanned surface vehicles in an atoll sea area, characterized in that, The method comprises the following steps: An island environment perception map and a search area grid containing obstacles are constructed, wherein the obstacles are generated into a polygon structure by a disturbance circle and constructed by a polyshape function, the whole monitoring area is divided into a plurality of sub-areas according to a fixed grid scale, the geometric center coordinates of each sub-area are taken as an identifier, and the obstacle state of the sub-area is recorded, wherein if the sub-area contains an obstacle, the sub-area is recorded as 1, and if the sub-area does not contain an obstacle, the sub-area is recorded as 0; Sub-area state variables are defined and a task allocation table is initialized, wherein the sub-area state variables include a search state and a lock flag, the task allocation table includes a sub-area ID, a center coordinate, a state variable and an allocation time stamp, and when the initialization is performed, the search state and the lock flag of all non-obstacle sub-areas are set to 0 and stored in a distributed storage module for access by a plurality of unmanned ships; A region allocation mechanism based on distance and state judgment is constructed, the unmanned ship triggers the allocation process periodically, the sub-areas with the search state, the lock flag and the obstacle state of 0 are screened out, the sub-area closest to the current position of the unmanned ship is selected as a target through Euclidean distance calculation, and after the allocation is completed, the lock flag of the sub-area is immediately set to 1 and is synchronized to the global task allocation table; A local obstacle avoidance enhanced propulsion control algorithm is established, the propulsion speed of the unmanned ship is composed of a target propulsion speed and an obstacle avoidance repulsive force speed, and the synthesized speed needs to meet the power constraint of the unmanned ship; A search completion judgment mechanism and a region state updating rule are set, when the unmanned ship enters the target sub-area, the residence time thereof is continuously monitored through positioning data, if the residence time reaches a preset threshold, it is determined that the search is completed, the search state of the sub-area is set to 1, and the lock flag is reset to 0, if the search is not completed after the residence time threshold is doubled, the search state and the lock flag of the sub-area are forcibly reset to 0 and the region is released; A search task degradation scheduling mechanism is established, when the unmanned ship cannot find a sub-area meeting the search state, the lock flag and the obstacle state of 0 in the current round of allocation, the degradation scheduling strategy is triggered, only the sub-areas with the search state and the obstacle state of 0 are screened out and the nearest region is selected for allocation, if there is repeated allocation, the unmanned ship automatically switches to the second nearest region, and the degradation scheduling strategy is stopped when a sub-area meeting the search state, the lock flag and the obstacle state of 0 is found.

2. The method of claim 1, wherein, The method for constructing the search area grid comprises the following steps: wherein, represents the whole unmanned ship search task area, adopts two-dimensional grid representation, represents the first sub-area, is the basic unit of search, represents the geometric center coordinates of the sub-area, defined in the inertial coordinate system, represents the search state of the current area, represents whether the sub-area is an obstacle area.

3. The method of claim 1, wherein, The sub-area state variable includes a search state and a lock flag, wherein the search state is: wherein, represents the current search state of the th sub-region, and the specific value meanings include represents that the region has not been searched by any unmanned ship, represents that the region is being searched by a certain unmanned ship, represents that the region has been successfully searched and meets the completion condition; The lock flag of the sub-area state variable is: wherein indicates that the area has been selected by a certain unmanned ship as a search target at present, is in a task allocation state, and other unmanned ships cannot be allocated repeatedly; indicates that the area can be allocated to any unmanned ship that meets the conditions for searching.

4. The method of claim 1, wherein, Based on the current position, the sub-area state variable and the obstacle state, each unmanned ship participating in the search task is dynamically allocated an optimal search target region, and the specific allocation strategy is: wherein, denotes the target sub-region number assigned to the current USV, denotes the position coordinate of the current USV and the center point coordinate of the first sub-region, the Euclidean distance between them, denotes that the region has not been searched, denotes that the region is a passable region, denotes that the region has not been locked by other USVs.

5. The method of claim 1, wherein, The propulsion control algorithm is composed of a kinematics equation of the unmanned ship and an obstacle avoidance control strategy, and comprises the following steps: A kinematics model of the unmanned ship is established: wherein denotes the lateral coordinate of the USV in the inertial coordinate system, denotes the longitudinal coordinate of the USV in the inertial coordinate system, denotes the heading angle, denotes the longitudinal velocity in the body coordinate system, denotes the lateral velocity in the body coordinate system, and denotes the angular velocity; A control dynamics model of the unmanned ship is established: in Represents the mass and inertia matrix. Represents the acceleration vector. Represents the longitudinal velocity in the hull coordinate system rate of change, Represents the lateral velocity in the hull coordinate system rate of change, Represents angular velocity rate of change, Represents the hydrodynamic damping matrix. Represents the velocity state vector. This represents the longitudinal velocity in the hull coordinate system. This represents the lateral velocity in the hull coordinate system. Indicates angular velocity, Represents the control input vector. Indicates propulsion. Indicates steering torque. Indicates external interference; The obstacle avoidance control strategy adopts a repulsive potential field model to construct an obstacle avoidance speed, and adds an obstacle avoidance term in the propulsion direction to synthesize a final speed : wherein, represents the desired pushing velocity towards the target search sub-region, represents the obstacle avoidance velocity generated by the obstacle repulsion field, the calculation method is: wherein, represents the current position of the unmanned boat, represents the position of the first obstacle, represents the distance to the first obstacle, represents the distance to the first obstacle, represents the distance to the first obstacle, represents the distance to the first obstacle, represents the distance to the first obstacle, represents the distance to the first obstacle, 6. The method of claim 1, wherein, When the search completion judgment mechanism is set, a residence time and path coverage determination combination is used as a search completion determination criterion, and the determination condition is as follows: The region residence time determination: An unmanned vehicle enters a search area After, if the continuous residence time in the area reaches a threshold The residence task in the area is considered to be completed, and the judgment formula is as follows: wherein, denotes the time the unmanned vessel continuously stays within the sub-area, is a minimum dwell time threshold set for the mission, if this condition is met, the search is determined to be complete;​ The state updating mechanism is: If the search completion determination condition is met, the region state variable is updated as follows: If the USV fails to complete the search or exits the area abnormally within the preset timeout time a reset operation is performed: 。 7. The method of claim 1, wherein, When the unmanned ship cannot find a sub-area that meets the search state, lock flag, and obstacle state of 0 in the current round assignment, the degradation scheduling strategy is triggered, and the specific strategy is as follows: Degradation assignment mechanism: wherein represents that the area has not been searched, represents that the area is a passable area, represents that the area has not been locked by other unmanned boats, if the current unmanned boat cannot find a sub-area that meets the following three conditions at the same time in the task allocation process, a degraded task allocation is performed, the locking condition is ignored, and only the closest target area in the unsearched passable area is selected , the formula is as follows: wherein represents the current position coordinate of the unmanned vehicle and the center point coordinate of the first sub-region , the Euclidean distance between them. The selected region allows the current unmanned vehicle to go to perform the search task even if it has been locked by other unmanned vehicles, so as to ensure the continuous advancement of the task in the case of resource conflict.

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