Method and device for constructing unmanned ship configuration motion model based on biological aggregation characteristics, and multi-unmanned ship rapid mission configuration method

Abstract

Method and device for constructing unmanned ship configuration motion model based on biological aggregation characteristics, and multi-unmanned ship fast task configuration method, which relate to the technical field of multi-unmanned ship task planning. The present invention uses the configuration motion model to obtain the execution sequence for multi-unmanned ships to complete tasks, enabling the multi-unmanned ships to complete tasks fastest while consuming the least energy. The configuration method of the present invention is as follows: according to the maximum rated motion speed of each unmanned ship, obtain the number of time slots required for each unmanned ship to complete the task from the initial position, according to the number of time slots, obtain the maximum motion speed of each unmanned ship, then according to the motion direction of each unmanned ship, obtain the aggregation motion model of all unmanned ships, and then according to the distance from the initial position of each unmanned ship to the required passing point, the distance from each unmanned ship to the required passing point after completing the aggregation motion, and the step length of the aggregation motion of each unmanned ship, obtain the configuration motion model of all unmanned ships. The present invention is applicable to multi-unmanned ships to quickly complete tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-unmanned ship mission planning. Background Art

[0002] With the continuous exploration of water resources by humans, the field of unmanned navigation has gradually developed rapidly. Unmanned ships have characteristics such as high flexibility. When performing dangerous tasks in the sea area, they can greatly reduce casualties and cost losses, so they are widely used in performing complex and repetitive sea surface tasks. Due to the complex and changeable water surface environment and the influence of some obstacles, the development of unmanned ships is relatively slow, especially in the field of multi-unmanned ship cooperation. In addition, the navigation ability of a single unmanned ship cannot meet the needs of actual task execution. The collaborative planning of multi-unmanned ships has become an important development trend. In the future, multi-unmanned ships will be more widely used in multi-field sea surface task execution scenarios such as scientific research exploration, search and rescue, underwater mapping, and security patrol.

[0003] With the increasing maturity of unmanned ship technology, the task configurations of multi-unmanned ships have attracted great attention from all sectors of society. Most of the existing task configuration unmanned ships do not consider the issue of energy consumption. Summary of the Invention

[0004] The present invention provides a method for constructing a configuration motion model of an unmanned ship based on biological aggregation characteristics. By using the configuration motion model, an execution sequence for multi-unmanned ships to complete tasks is obtained, enabling the multi-unmanned ships to complete tasks as quickly as possible while minimizing energy consumption.

[0005] To achieve the above object, the present invention provides the following solution:

[0006] The present invention provides a method for constructing a configuration motion model of an unmanned ship based on biological aggregation characteristics. The method is as follows:

[0007] S1. Step of obtaining the maximum motion speed: According to the maximum rated motion speed of each unmanned ship, obtain the number of time slots required for each unmanned ship to complete the task from the initial position. According to the number of time slots, obtain the maximum motion speed of each unmanned ship;

[0008] S2. Step of obtaining the aggregation motion model: According to the maximum motion speed of each unmanned ship and the motion direction of each unmanned ship, obtain the aggregation motion model of all unmanned ships;

[0009] S3. Step of obtaining the configuration motion model: According to the aggregation motion model of all unmanned ships, the distance from the initial position of each unmanned ship to the required passing point, the distance from each unmanned ship to the required passing point after completing the aggregation motion, and the step length of the aggregation motion of each unmanned ship, obtain the configuration motion model of all unmanned ships.

[0010] Further, there is also a preferred embodiment, where the number of time slots of the \(i\)-th unmanned ship in the above step S1 is expressed as:

[0011]

[0012] where \(V\) max is the maximum rated motion speed of the unmanned ship, \(\Delta T\) is the time slot length of the unmanned ship position update, and \(d_i\) is the distance from the initial position of the \(i\)-th unmanned ship to the mandatory point.

[0013] Further, there is also a preferred embodiment, where the maximum motion speed of the \(i\)-th unmanned ship in the above step S1 is expressed as:

[0014]

[0015] Further, there is also a preferred embodiment, where the aggregation motion model of the \(i\)-th unmanned ship in the above step S2 includes an aggregation motion model of three time slots, which are respectively expressed as:

[0016] Aggregation motion model of the first time slot:

[0017]

[0018] Aggregation motion model of the second time slot:

[0019]

[0020] Aggregation motion model of the third time slot:

[0021]

[0022] where \(x\) i (k) and \(y\) i (k) are the position information of the \(i\)-th unmanned ship at the \(k\)-th time slot, and \(\alpha\) i is the motion direction of the \(i\)-th unmanned ship.

[0023] Further, there is also a preferred embodiment, where the above step S3 is specifically:

[0024] When the configuration motion model of the unmanned ship is the aggregation motion model of the first time slot;

[0025] When the configuration motion model of the unmanned ship is the aggregation motion model of the second time slot;

[0026] When the configuration motion model of the unmanned ship is the aggregation motion model of the third time slot;

[0027] where step iThe step length for the collective movement of the i-th unmanned ship, l is The distance from the i-th unmanned ship to the mandatory passing point after completing the collective movement, l i The distance from the i-th unmanned ship to the mandatory passing point.

[0028] The present invention also provides a method for quickly configuring tasks for multiple unmanned ships. The configuration method is implemented using an unmanned ship configuration movement model, which is obtained based on the method for constructing an unmanned ship configuration movement model based on biological aggregation characteristics described in any one of the above. The configuration method is as follows:

[0029] A1. Determine the priority of all unmanned ships to complete the task according to the distance from the initial position of each unmanned ship to the mandatory passing point;

[0030] A2. According to the priority of all unmanned ships to complete the task, use the configuration movement model to sequentially obtain the number of collective movement times and the start time of the configuration movement included in the task configuration of each unmanned ship;

[0031] A3. According to the number of collective movement times and the start time of the configuration movement included in the task configuration of each unmanned ship, obtain the execution sequence of all unmanned ships to complete the task.

[0032] Furthermore, there is a preferred embodiment. Specifically, step A1 is as follows:

[0033] According to the distance from the initial position of unmanned ship i to the mandatory passing point is The distance from the initial position of unmanned ship j to the mandatory passing point is Then the priority of unmanned ship i and unmanned ship j to complete the task is expressed as:

[0034]

[0035] The present invention provides a device for constructing an unmanned ship configuration movement model based on biological aggregation characteristics. The device includes:

[0036] A device for obtaining the maximum movement speed: used to obtain the number of time slots required for each unmanned ship to complete the task from the initial position according to the maximum rated movement speed of each unmanned ship, and according to the number of time slots, obtain a storage device for the maximum movement speed of each unmanned ship;

[0037] A device for obtaining the collective movement model: a storage device for obtaining the collective movement model of all unmanned ships according to the maximum movement speed of each unmanned ship and the movement direction of each unmanned ship;

[0038] Device for obtaining a configuration motion model: A storage device for obtaining the configuration motion model of all unmanned vessels based on the aggregation motion model of all unmanned vessels, the distances from the initial positions of each unmanned vessel to the required passing points, the distances from each unmanned vessel to the required passing points after completing the aggregation motion, and the step lengths of the aggregation motion of each unmanned vessel.

[0039] The present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is run by a processor, the processor executes the method for constructing an unmanned vessel configuration motion model based on biological aggregation characteristics described in any one of the above, or the multi-unmanned vessel fast task configuration method described in any one of the above.

[0040] The present invention provides a computer device that includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method for constructing an unmanned vessel configuration motion model based on biological aggregation characteristics described in any one of the above, or the multi-unmanned vessel fast task configuration method described in any one of the above.

[0041] The beneficial effects of the present invention are as follows: The present invention provides a method for constructing an unmanned vessel configuration motion model based on biological aggregation characteristics. By using the configuration motion model, the execution sequence for multiple unmanned vessels to complete a task is obtained, enabling multiple unmanned vessels to complete the task fastest while consuming the least amount of energy.

[0042] At the same time, the following advantages are generated:

[0043] 1. The present invention provides a method for constructing an unmanned vessel configuration motion model based on biological aggregation characteristics. The aggregation motion model of the unmanned vessel adopted includes an aggregation motion model with three time slots, providing a feasible basis for the aggregation behavior of the unmanned vessel.

[0044] 2. The present invention provides a multi-unmanned vessel fast task configuration method based on biological aggregation characteristics. By using the configuration motion model of the unmanned vessel, the number of aggregation motions and the start time of the configuration motion included in the aggregation behavior of each unmanned vessel can be obtained, and then the execution sequence for all unmanned vessels to complete the task can be obtained, enabling the task to be completed more quickly while consuming the least amount of energy.

[0045] 3. The present invention provides a multi-unmanned vessel fast task configuration method based on biological aggregation characteristics. The unmanned vessel moves along the straight line direction between the unmanned vessel and the required passing point, enabling the unmanned vessel to complete the task with the lowest energy consumption.

[0046] 4. The present invention provides a multi-unmanned vessel fast task configuration method based on biological aggregation characteristics. By introducing an aggregation behavior during the multi-unmanned vessel task process, the multi-unmanned vessel can complete the task configuration with the shortest number of time slots, improving the efficiency of task completion.

[0047] The present invention is applicable to multiple unmanned boats to quickly complete tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the method for constructing a configuration motion model of an unmanned boat based on biological aggregation characteristics described in Embodiment 1;

[0049] Figure 2 is a flowchart of the method for quickly configuring tasks of multiple unmanned boats described in Embodiment 6;

[0050] Figure 3 is a schematic diagram of a scenario for configuring tasks of a multiple unmanned boat cluster described in Embodiment 11;

[0051] Figure 4 is a schematic diagram of the process of the aggregation behavior of multiple unmanned boats described in Embodiment 11. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Embodiment 1. Refer to Figure 1 To describe this embodiment, this embodiment provides a method for constructing a motion model of an unmanned boat based on biological aggregation characteristics. The method is as follows:

[0053] S1. Step of obtaining the maximum motion speed: According to the maximum rated motion speed of each unmanned boat, obtain the number of time slots required for each unmanned boat to move from the initial position to complete the task. According to the number of time slots, obtain the maximum motion speed of each unmanned boat;

[0054] S2. Step of obtaining the aggregation motion model: According to the maximum motion speed of each unmanned boat and the motion direction of each unmanned boat, obtain the aggregation motion model of all unmanned boats;

[0055] S3. Step of obtaining the configuration motion model: According to the aggregation motion model of all unmanned boats, the distance from the initial position of each unmanned boat to the required passing point, the distance from each unmanned boat to the required passing point after completing the aggregation motion, and the step length of the aggregation motion of each unmanned boat, obtain the configuration motion model of all unmanned boats.

[0056] In actual application of this embodiment, in order to enable the unmanned ship to quickly complete the task configuration, a suitable maximum motion speed V needs to be set, so that the unmanned ship can complete the task configuration faster, and ensure that the unmanned ship can just decelerate to 0 when it moves to the required passing point, and then move along a straight line. According to the maximum rated motion speed of each unmanned ship, the maximum motion speed of each unmanned ship is obtained, and then according to the maximum motion speed of each unmanned ship, the aggregation motion model of all unmanned ships is obtained, providing a feasible basis for the aggregation behavior of multiple unmanned ships, so that multiple unmanned ships can complete the task configuration with the shortest number of time slots, improving the efficiency of task completion. In actual application, the motion of the unmanned ship is divided into two processes, namely aggregation motion and configuration motion. The configuration motion refers to the process of the unmanned ship moving again after completing the aggregation process. The motion form of the configuration motion is related to the distance between the unmanned ship and the required passing point and the size relationship of the aggregation motion step length. Therefore, according to the distance from the initial position of each unmanned ship to the required passing point, the distance from each unmanned ship to the required passing point, and the size of the aggregation motion step length, the configuration motion model of all unmanned ships is obtained.

[0057] This embodiment provides a method for constructing an unmanned ship motion model based on biological aggregation characteristics. Using the aggregation motion model and configuration motion model of the unmanned ship, the number of aggregation motions and the start time of the configuration motion included in the aggregation behavior of each unmanned ship can be obtained, and then the execution sequence of all unmanned ships to complete the task can be obtained, so that the task can be completed more quickly while minimizing energy consumption.

[0058] Embodiment 2. This embodiment gives an example of the number of time slots in step S1 of a method for constructing an unmanned ship motion model based on biological aggregation characteristics described in Embodiment 1. The number of time slots of the i-th unmanned ship in step S1 is expressed as:

[0059]

[0060] where V max is the maximum rated motion speed of the unmanned ship, ΔT is the time slot length for updating the position of the unmanned ship, is the distance from the initial position of the i-th unmanned ship to the required passing point.

[0061] In actual application of this embodiment, in order to enable the unmanned ship to quickly complete the task configuration, a suitable maximum motion speed V is set, so that the unmanned ship can complete the task configuration faster, and ensure that the unmanned ship can just decelerate to 0 when it moves to the required passing point, and then move along a straight line. To calculate the maximum motion speed, it is necessary to calculate the number of time slots T max required for the unmanned ship to move from the initial position to complete the task configuration at the rated maximum motion speed V inum0 .

[0062] Embodiment 3. This embodiment gives an example of the maximum motion speed in step S1 of a method for constructing an unmanned ship motion model based on biological aggregation characteristics described in Embodiment 1. The maximum motion speed of the i-th unmanned ship in step S1 is expressed as:

[0063]

[0064] In actual application of this embodiment, the number of time slots T inum0 is used to obtain the maximum motion speed of the unmanned ship, ensuring that the unmanned ship can just decelerate to 0 at the mandatory passing point and then move in a straight line, so that the unmanned ship can quickly complete the task configuration.

[0065] Embodiment 4. This embodiment gives an example of the aggregation motion model of the unmanned ship in step S2 of a method for constructing an unmanned ship motion model based on biological aggregation characteristics described in Embodiment 1. The aggregation motion model of the i-th unmanned ship in step S2 includes the aggregation motion models of three time slots, which are respectively expressed as:

[0066] The aggregation motion model of the first time slot:

[0067]

[0068] The aggregation motion model of the second time slot:

[0069]

[0070] The aggregation motion model of the third time slot:

[0071]

[0072] Among them, x i (k) and y i (k) are the position information of the i-th unmanned ship at the k-th time slot, and α i is the motion direction of the i-th unmanned ship.

[0073] This embodiment proposes an aggregation motion model including three time slots. Among them, the first time slot completes a uniformly accelerated motion from speed 0 to the set maximum speed V; the second time slot then completes a uniform motion at the maximum set speed V; and the last time slot completes a uniformly decelerated motion from the set speed V to 0, providing a feasible basis for the aggregation behavior of multiple unmanned ships, so that multiple unmanned ships can complete the task with the shortest number of time slots and improve the efficiency of task completion.

[0074] Embodiment 5. This embodiment gives an example of step S3 in a method for constructing an unmanned ship motion model based on biological aggregation characteristics described in Embodiment 1 or Embodiment 4. The specific steps of step S3 are as follows:

[0075] When the configuration motion model of the unmanned ship is the aggregation motion model of the first time slot;

[0076] When the configuration motion model of the unmanned ship is the aggregation motion model of the second time slot;

[0077] When the configuration motion model of the unmanned ship is the aggregation motion model of the third time slot;

[0078] where step i is the step length of the aggregation motion of the i-th unmanned ship, l is is the distance from the i-th unmanned ship to the mandatory passing point after completing the aggregation motion, and l i is the distance from the i-th unmanned ship to the mandatory passing point.

[0079] In actual application of this embodiment, the configuration motion model includes three motion forms, which are determined by the relationship between the distance from the initial position of each unmanned ship to the mandatory passing point, the distance from each unmanned ship to the mandatory passing point, and the step length of the aggregation motion.

[0080] Embodiment 6. Refer to Figure 2 This embodiment is described. This embodiment provides a method for quickly configuring tasks for multiple unmanned ships. The configuration method is implemented by using the motion model in a method for constructing an unmanned ship motion model based on biological aggregation characteristics described in any one of Embodiments 1 to 5. The configuration method is as follows:

[0081] A1. Determine the priority of all unmanned ships to complete tasks according to the distance from the initial position of each unmanned ship to the mandatory passing point;

[0082] A2. According to the priority of all unmanned ships to complete tasks, use the configuration motion model to sequentially obtain the number of aggregation motions and the start time of the configuration motion included in the task configuration of each unmanned ship;

[0083] A3. According to the number of aggregation motions and the start time of the configuration motion included in the task configuration of each unmanned ship, obtain the execution sequence of all unmanned ships to complete tasks.

[0084] In practical applications of this embodiment, the number of aggregation movements and the start time of the configuration process required for each unmanned ship's aggregation behavior are calculated by using the number of aggregation movements and the start time of the configuration process of adjacent unmanned ships with higher priorities, so that communication is no longer required during the mission configuration process among unmanned ships.

[0085] In practical applications of this embodiment, the unmanned ship moves along the straight-line direction between the unmanned ship and the necessary passing point, enabling the unmanned ship to complete the mission configuration with the lowest energy consumption.

[0086] Based on the initial position of the unmanned ship being (x, y) and the position of the necessary passing point being (O x ,O y ), the movement direction α of the unmanned ship for mission configuration is expressed as:

[0087]

[0088] This embodiment provides a method for rapid mission configuration of multiple unmanned ships based on biological aggregation characteristics. By using the aggregation movement model and configuration movement model of the unmanned ships, the number of aggregation movements and the start time of the configuration movement included in the aggregation behavior of the unmanned ships can be obtained, and then the execution sequence for all unmanned ships to complete the mission can be obtained, enabling the mission to be completed more quickly while minimizing energy consumption.

[0089] Embodiment Seven. This embodiment is an example of step A1 in the method for rapid mission configuration of multiple unmanned ships described in Embodiment Six. The specific content of step A1 is as follows: [[ID=2l]]

[0090] Based on the distance from the initial position of unmanned ship i to the necessary passing point being and the distance from the initial position of unmanned ship j to the necessary passing point being the priorities of unmanned ship i and unmanned ship j to complete the mission configuration are expressed as:

[0091]

[0092] In practical applications of this embodiment, according to the distances from the initial positions of multiple unmanned ships to the necessary passing point, the priorities for multiple unmanned ships to complete the mission configuration are set. i||j means that the priority of i is higher than that of j or the priority of j is higher than that of i, that is, the unmanned ship closer to the necessary passing point is assigned a higher priority.

[0093] In practical applications of this embodiment, the unmanned ship moves along the straight-line direction between the unmanned ship and the necessary passing point, enabling the unmanned ship to complete the mission configuration with the lowest energy consumption.

[0094] Embodiment Eight. This embodiment provides a device for constructing a movement model of an unmanned ship based on biological aggregation characteristics. The device includes:

[0095] Device for obtaining maximum movement speed: a storage device for obtaining the number of time slots required for each unmanned ship to complete a task from the initial position according to the maximum rated movement speed of each unmanned ship, and obtaining the maximum movement speed of each unmanned ship according to the number of time slots;

[0096] Device for obtaining aggregation movement model: a storage device for obtaining the aggregation movement model of all unmanned ships according to the maximum movement speed of each unmanned ship and the movement direction of each unmanned ship;

[0097] Device for obtaining configuration movement model: a storage device for obtaining the configuration movement model of all unmanned ships according to the distance from the initial position of each unmanned ship to the required passing point, the distance from each unmanned ship to the required passing point after completing the aggregation movement, and the step length of the aggregation movement of each unmanned ship.

[0098] Embodiment Nine. This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes a method for constructing an unmanned ship configuration movement model based on biological aggregation characteristics described in any one of Embodiments One to Five or a method for quickly configuring a multi-unmanned ship task based on biological aggregation characteristics described in any one of Embodiments Six to Seven.

[0099] Embodiment Ten. This embodiment provides a computer device, which includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a method for constructing an unmanned ship movement model based on biological aggregation characteristics described in any one of Embodiments One to Five or a method for quickly configuring a multi-unmanned ship task based on biological aggregation characteristics described in any one of Embodiments Six to Seven.

[0100] Embodiment Eleven. Refer to Figure 2 and Figure 3 This embodiment will be described. This embodiment uses a method for quickly configuring a multi-unmanned ship task based on biological aggregation characteristics described in Embodiment Eight to obtain the number of aggregation movements and the start time of the configuration process included in the aggregation behavior of the unmanned ship. This embodiment calculates the number of aggregation movements and the start time of the configuration process required for its own aggregation behavior by using the number of aggregation movements and the start time of the configuration process of adjacent unmanned ships with higher priority, so that communication is no longer required during the task configuration process among unmanned ships. Since the movement directions of adjacent unmanned ships with higher priority have a great influence on the calculation of the aggregation behavior, it is necessary to calculate the number of aggregation movements and the start time of the configuration process included in the aggregation behavior of the unmanned ship according to different relationships between the two movement directions.

[0101] (1) When the movement direction α of the unmanned ship ii and the movement direction α of the unmanned ship j j When they are the same:

[0102] The distance difference between the unmanned ships i and j from the initial position to the required passing point is expressed as:

[0103] To obtain the number of aggregation movements for the aggregation behavior of the unmanned ships, it is necessary to obtain the distance of the maximum possible aggregation behavior of the unmanned ships. For different speed relationships between the unmanned ships, the maximum possible aggregation distances of the unmanned ships with lower priority are obviously different. When V i ≥ V j , the maximum distance at which the unmanned ship j may perform aggregation includes two parts, namely, the movement distance of the aggregation behavior of the unmanned ship i and the distance difference Juc0;

[0104] When V i < V j , the maximum distance at which the unmanned ship j may perform aggregation behavior includes three parts, namely, the movement distance of the aggregation behavior of the unmanned ship i, the distance difference Juc0, and the movement distance difference between the unmanned ships i and j during the period from the start configuration of the unmanned ship i to the completion of the task configuration. Therefore, the maximum distance Juc at which the unmanned ship j may perform aggregation behavior is expressed as:

[0105]

[0106] where, R inum is the number of aggregation movements included in the aggregation behavior of the unmanned ship i.

[0107] When V i ≥ V j , using the above formula (9), the maximum possible number of aggregation movements Jin max of the unmanned ship j is obtained. Jin max is expressed as:

[0108]

[0109] where the symbol R is the minimum task distance interval required to maintain the task configuration.

[0110] When V i ≥ V j , using the above formula (9) and formula (10), the number of aggregation movements R jnum included in the aggregation behavior of the unmanned ship j is obtained. R jnum is expressed as:

[0111]

[0112] When V i < Vj When, since the aggregation movement step length of the unmanned ship j is greater than that of the unmanned ship i, if the number of aggregation movements of the unmanned ship j is too large, it is very likely that the unmanned ships i and j will collide. The number of aggregation movements required for the aggregation behavior is calculated based on the condition that the unmanned ships do not collide and the task interval requirements between adjacent priority unmanned ships in the task configuration need to be ensured. When the sum of the distance difference between the unmanned ships i and j to the mandatory passing point and the difference in the aggregation movement step lengths of the two unmanned ships is less than or equal to 0, the unmanned ship j will no longer perform aggregation movement, and then it is judged whether the condition for the start of the configuration process is met. As Figure 3 shown, the aggregation movement step length of the unmanned ship 2 is greater than that of the unmanned ship 1. When the unmanned ships 1 and 2 perform the fourth aggregation movement simultaneously, the unmanned ships 1 and 2 will collide. Therefore, the aggregation behavior of the unmanned ship 2 only includes three aggregation movements and then waits for the start of the configuration process. Therefore, in this case, it is necessary to recalculate the maximum possible number of aggregation movements of the unmanned ship j. When V i <V j When, the maximum possible number of aggregation movements of the unmanned ship j is shown as follows:

[0113]

[0114] Using the above formulas (9) and (12), the number of aggregation movements and the configuration start time of the unmanned ship j are respectively:

[0115] Using the above formulas (11) and (13), the number of time slots T of the unmanned ship j from the start of the task configuration moment to the mandatory passing point is jnum expressed as:

[0116]

[0117] The start time slot of the takeoff movement of the unmanned ship j is shown as follows:

[0118]

[0119] (2) When the movement direction α i of the unmanned ship i and the movement direction α j of the unmanned ship j are different:

[0120] When adjacent unmanned boats move in different directions, the aggregation behavior of unmanned boat j does not need to consider the collision problem. It only needs to ensure that the task configuration conditions are met, that is, the two unmanned boats with adjacent priorities should always maintain a distance interval R greater than or equal to the set value during the configuration movement to the task configuration. The distance interval refers to the difference in the distances of the two to the required passing point. Using formula (9) and formula (10), the number of aggregation movements and the configuration start time included in the aggregation behavior of unmanned boat j are obtained as follows:

[0121]

[0122]

[0123] Finally, the multi-unmanned boats are sorted from high to low according to the priority order of completing the task, and various control information required for the multi-unmanned boats to complete the task configuration is obtained in turn using this embodiment.

[0124] This embodiment provides a method for quickly configuring the tasks of multi-unmanned boats based on the biological aggregation characteristics. Using the task configuration method, the number of aggregation movements and the start time of the configuration movement included in the aggregation behavior of the unmanned boats can be obtained, so that the unmanned boats can quickly complete the task configuration.

[0125] The above are only embodiments of the present invention and do not limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for constructing an unmanned ship configuration motion model based on biological aggregation characteristics, characterized in that, The method is as follows: S1. Steps to obtain the maximum movement speed: According to the maximum rated movement speed of each unmanned ship, obtain the number of time slots required for each unmanned ship to complete the task from the initial position. Based on the number of time slots, obtain the maximum movement speed of each unmanned ship; S2. Steps to obtain the aggregation movement model: According to the maximum movement speed of each unmanned ship and the movement direction of each unmanned ship, obtain the aggregation movement model of all unmanned ships; The aggregation movement model of the i-th unmanned ship includes the aggregation movement models of three time slots, which are respectively expressed as: The aggregation movement model of the first time slot: The aggregation movement model of the second time slot: The aggregation movement model of the third time slot: Among them, x i (k) and y i (k) are the position information of the k-th time slot of the i-th unmanned ship, α i is the moving direction of the i-th unmanned ship, ΔT is the time slot length for updating the position of the unmanned ship, V i is the speed of the i-th unmanned ship; S3. Steps to obtain the configuration movement model: According to the aggregation movement models of all unmanned ships, the distances from the initial positions of each unmanned ship to the required passing points, the distances from each unmanned ship to the required passing points after completing the aggregation movement, and the step lengths of the aggregation movement of each unmanned ship, obtain the configuration movement models of all unmanned ships; The specific content of step S3 is as follows: When The configuration motion model of the unmanned ship is the aggregation motion model of the first time slot; When The configuration motion model of the unmanned ship is the aggregation motion model for the second time slot; When The configuration motion model of the unmanned ship is the aggregation motion model for the third time slot; Among them, step i is the step length of the i-th unmanned ship's aggregation movement, l is is the distance from the i-th unmanned ship to the must-pass point after completing the aggregation movement, l i is the distance from the i-th unmanned ship to the must-pass point.

2. The method for constructing an unmanned ship configuration motion model based on biological aggregation characteristics according to claim 1, wherein The number of time slots of the i-th unmanned ship in step S1 is expressed as: Among them, V max is the maximum rated motion speed of the unmanned ship, and ΔT is the length of the unmanned ship position update time slot. is the distance from the initial position of the i-th unmanned ship to the mandatory passing point.

3. The method for constructing an unmanned ship configuration motion model based on biological aggregation characteristics according to claim 2, wherein The maximum movement speed of the i-th unmanned ship in step S1 is expressed as:

4. Method for rapid mission configuration of multiple unmanned boats, characterized in that, The configuration method is implemented by using the configuration movement model of the unmanned ship. The configuration movement model of the unmanned ship is obtained based on the method for constructing the configuration movement model of the unmanned ship based on biological aggregation characteristics described in any one of claims 1-3. The configuration method is as follows: A1. Determine the priorities of all unmanned ships to complete the task according to the distances from the initial positions of each unmanned ship to the required passing points; A2. According to the priorities of all unmanned ships to complete the task, use the configuration movement model to sequentially obtain the number of aggregation movements and the start times of the configuration movements included in the task completion configurations of each unmanned ship; A3. According to the number of aggregation movements and the start times of the configuration movements included in the task completion configurations of each unmanned ship, obtain the execution sequences of all unmanned ships to complete the task.

5. The multi-unmanned ship rapid mission configuration method according to claim 4, wherein The specific content of step A1 is as follows: The distance from the unmanned ship i to the mandatory passing point from the initial position is The distance from the unmanned ship j to the mandatory passing point from the initial position is Then the priority of the unmanned ship i and the unmanned ship j to complete the task is expressed as:

6. An apparatus for constructing a motion model of an unmanned ship configuration based on biological aggregation characteristics, characterized in that The device includes: A device for obtaining the maximum movement speed: A storage device for obtaining the number of time slots required for each unmanned ship to complete the task from the initial position according to the maximum rated movement speed of each unmanned ship, and obtaining the maximum movement speed of each unmanned ship based on the number of time slots; A device for obtaining the aggregation movement model: A storage device for obtaining the aggregation movement model of all unmanned ships according to the maximum movement speed of each unmanned ship and the movement direction of each unmanned ship; The aggregation movement model of the i-th unmanned ship includes the aggregation movement models of three time slots, which are respectively expressed as: The aggregation movement model of the first time slot: The aggregation movement model of the second time slot: The aggregation movement model of the third time slot: Among them, x i (k) and y i (k) are the position information of the k-th time slot of the i-th unmanned ship, α i is the moving direction of the i-th unmanned ship, ΔT is the time slot length of the unmanned ship position update, and V i is the speed of the i-th unmanned ship; A device for obtaining the configuration movement model: A storage device for obtaining the configuration movement models of all unmanned ships according to the aggregation movement models of all unmanned ships, the distances from the initial positions of each unmanned ship to the required passing points, the distances from each unmanned ship to the required passing points after completing the aggregation movement, and the step lengths of the aggregation movement of each unmanned ship; When The configuration motion model of the unmanned ship is the aggregation motion model for the first time slot; When The configuration motion model of the unmanned ship is the aggregation motion model of the second time slot; When The configuration motion model of the unmanned ship is the aggregation motion model for the third time slot; Among them, step i is the step length of the gathering movement of the i-th unmanned ship, l is is the distance from the i-th unmanned ship to the mandatory passing point after completing the gathering movement, l i is the distance from the i-th unmanned ship to the mandatory passing point.

7. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, it executes the method for constructing an unmanned ship configuration motion model based on biological aggregation characteristics described in any one of claims 1-3 or the method for multi-unmanned ship rapid mission configuration described in any one of claims 4-5.

8. A computer device, characterized in that, The device includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method for constructing an unmanned ship configuration motion model based on biological aggregation characteristics described in any one of claims 1-3 or the method for multi-unmanned ship rapid mission configuration described in any one of claims 4-5.

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