Collaborative unmanned ship formation control method

Abstract

The invention discloses a cooperative unmanned surface vehicle formation control method, and relates to the technical field of unmanned surface vehicle formation control, and the method comprises the steps: carrying out the preprocessing of a marine environment map, and setting a reference formation when the unmanned surface vehicle formation advances; screening a global path for the weak communication marine environment by using a genetic algorithm; according to the characteristics of the unmanned ship formation, reference to the formation and the global path, when an obstacle is encountered in the operation process, an improved artificial potential field method and a multi-event triggering mechanism are used for local obstacle avoidance; a leader-following algorithm and a multi-event triggering mechanism are used to carry out local adjustment on the formation after formation obstacle avoidance, and the position and state information of the unmanned ship formation is updated; the unmanned surface vehicle continuously judges whether the formation arrives at the target point or not, and if yes, the process is ended; and if not, circularly executing environment updating, path planning and the like, and continuously running until the target point is reached. According to the invention, the formation system can keep smooth communication and stable formation in a dynamic marine environment with weak communication, and can safely and successfully arrive at a target.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat formation control, and in particular to a cooperative unmanned boat formation control method. Background Art

[0002] At present, with the rapid development of marine technology, unmanned boats, as intelligent marine vehicles, have shown their prowess in the fields of marine resource exploration, coastal security defense, etc. due to their characteristic of operating without the need for personnel to follow the boat, and have become a powerful assistant for humans to explore and utilize the ocean. However, the progress of technology has pushed maritime tasks to become more refined and complex. Tasks such as multi-region synchronous monitoring and large-scale sea area patrols far exceed the capabilities of a single unmanned boat. A single boat is limited by problems such as a limited power system, sensor configuration, and computing resources, resulting in low efficiency when performing tasks and difficulty in coping with complex and changing environments. To break through this development bottleneck, researching unmanned boat formation control technology has become an urgent task in the field of marine vehicles. In particular, path planning and obstacle avoidance for leader-follower cooperative formations have become one of the inevitable choices to conform to the development trend of marine technology and meet the growing needs of marine operations.

[0003] In the field of path planning and obstacle avoidance control, some achievements have been made in existing research. Early solutions mostly adopted some traditional algorithms, such as particle swarm algorithm, bee colony algorithm, A-Star algorithm, Dijkstra algorithm, etc. Algorithms such as particle swarm and bee colony are very likely to produce locally optimal results; while the A-Star algorithm and Dijkstra algorithm have poor applicability to dynamic environments and have problems such as high complexity in complex environments. Most importantly, these traditional algorithms have many drawbacks in terms of communication. In a complex marine environment, communication signals are easily interfered by factors such as seawater medium and sea conditions, resulting in problems such as communication signal attenuation and high transmission delay, presenting weak communication characteristics. Continuous communication during the operation of traditional algorithms will consume limited marine communication resources excessively, easily cause communication congestion, and hinder the transmission of key instructions; continuous communication will also accelerate the power consumption of unmanned boats, reduce efficiency and increase costs. Moreover, the exchange of a large amount of data will cause processing delays, affect their timely response to environmental changes, and increase the collision risk. In addition, communication delay will lead to poor real-time performance. Facing a dynamic marine environment, it is impossible to ensure that unmanned boats can avoid obstacles in time, threatening navigation safety.

[0004] To optimize algorithm performance and communication issues, researchers have also tried to improve traditional algorithms, but there are still many limitations in actual marine application scenarios. On the one hand, from the perspective of the characteristics of the algorithms themselves, whether performing path planning tasks or obstacle avoidance operations, most existing algorithms only solve some of the problems among obstacle avoidance, preventing collisions between formation members, optimizing computational complexity, reducing the number of communications, and lowering communication costs, rather than considering all aspects comprehensively. Moreover, existing algorithms do not consider the problem that the algorithm is prone to falling into local optima and the resulting path oscillation. On the other hand, in a weak communication background, traditional algorithms are difficult to ensure real-time and accurate information interaction, and the cooperation efficiency between unmanned boats is greatly reduced. In addition, various other problems of event triggering are not considered, such as sudden large changes in speed and heading change rate, etc., which will all have a negative impact on the overall working efficiency. Summary of the Invention

[0005] In order to overcome the above problems existing in the prior art, the present invention proposes a cooperative unmanned boat formation control method.

[0006] The technical solution adopted by the present invention to solve its technical problems is: a cooperative unmanned boat formation control method, including the following steps: Step 1, Initialization: Preprocess the marine environment map, initialize the parameters, and set the reference formation when the unmanned boat formation is moving forward. Step 2, Global path planning: Use the genetic algorithm to screen out the global path for the weak communication marine environment. Step 3, Path construction and obstacle avoidance: According to the characteristics of the unmanned boat formation, the reference formation set in Step 1, and the global path obtained in Step 2, when the follower starts, it quickly adjusts and actively forms a reference formation with the leader. When encountering obstacles during the operation, use the improved artificial potential field method and multi-event triggering mechanism for local obstacle avoidance. Step 4, Information update and formation adjustment: Use the leader-follower algorithm and multi-event triggering mechanism to locally adjust the formation of the unmanned boat formation after obstacle avoidance, and update the position and state information of the unmanned boat formation. Step 5, Process judgment and loop: The formation system continuously judges whether the formation has reached the target point. If it has reached, it ends; if it has not reached, it repeats Steps 3-4 until the formation reaches the target point. The multi-event triggering mechanism in Step 3 and Step 4 includes a relative distance triggering mechanism, a change rate triggering mechanism, and a communication delay triggering mechanism.

[0007] The above-mentioned cooperative unmanned boat formation control method, the specific content of step 1 is as follows: rasterize the marine environment map, and convert static obstacles into grid cells with regular shapes; set dynamic obstacles, set their speeds and accelerations, define the grid occupancy status, and construct a standardized environment model; set the parameters of the unmanned boat formation, the gravitational coefficient and the repulsive coefficient of the artificial potential field method; set the threshold of the multi-event trigger mechanism; obtain the initial kinematic parameters of the unmanned boat.

[0008] The above-mentioned cooperative unmanned boat formation control method, the specific content of step 2 is as follows: Step 2.1, set the initial parameters of the genetic algorithm and generate an initial population; Step 2.2, calculate the fitness of the individual population through the optimized fitness function, select the optimal individual through the optimized selection method probability formula, and generate a new generation of population through adaptive crossover and mutation; Step 2.3, judge whether the satisfaction condition for iteration stop is met. If it is met, output the optimal path. If it is not met, repeat step 2.2; The specific form of the optimized fitness function is as follows: ; Among them, is the fitness value, representing the quality of the current path; , , are weight coefficients, respectively representing the importance degrees of the path length, obstacle avoidance cost, and sea condition influence cost in the fitness function, and satisfy ; L represents the path length; represents the obstacle avoidance cost, which is used to measure the degree of the path passing through the obstacle area; represents the sea condition influence cost, which reflects the degree of the path affected by adverse sea conditions.

[0009] The specific form of the optimized selection method probability formula in step 2.2 of the above-mentioned cooperative unmanned boat formation control method is as follows: ; Among them, represents the probability that the -th individual is selected for reproduction; represents the fitness value of the -th individual; represents the number of individuals in the population; The specific formulas for adaptive crossover and mutation in step 2.2 are as follows: ; ; Among them, represents the crossover probability; represents the maximum value of the crossover probability; represents the minimum value of the crossover probability; represents the current iteration number; represents the maximum number of iterations; mutation probability; the maximum value of the mutation probability; the minimum value of the mutation probability.

[0010] The above-mentioned cooperative unmanned boat formation control method, in step 3, the improved artificial potential field method is specifically as follows: Calculate the resultant force received by the unmanned boat: ; where represents the resultant force received by the unmanned boat; represents the gravitational force received by the unmanned boat; represents the repulsive force of the static obstacle received by the unmanned boat; represents the repulsive force of the dynamic obstacle received by the unmanned boat; ; Among them, is the gravitational coefficient, used to adjust the magnitude of the gravitational force; q is the current position of the unmanned boat; is the th key point on the global path; is the number of key points; ; ; where, is the static repulsive force function; is the static obstacle repulsive force coefficient; q is the current position of the unmanned boat; is the position of the static obstacle; is the radius of the influence range of the static obstacle; ; Among them, represents the unit vector, the direction is from the dynamic obstacle to the unmanned boat, determining the direction of the repulsive force; , is the dynamic obstacle repulsive force coefficient; q is the current position of the unmanned boat; is the position of the dynamic obstacle at moment; is the speed of the dynamic obstacle; is the acceleration of the dynamic obstacle; is the radius of the influence range of the dynamic obstacle; is the speed influence coefficient; is the acceleration influence coefficient.

[0011] The above-mentioned cooperative unmanned boat formation control method, the relative distance triggering mechanism specifically includes heterogeneous relative distance triggering and homogeneous relative distance triggering. The heterogeneous relative distance triggering is specifically as follows: using sensors to continuously perceive obstacle information in the surrounding environment, the distance between the unmanned boat and the obstacle is , the dangerous distance threshold is , and the triggering condition is ; The homogeneous relative distance triggering is specifically as follows: the expected distance between unmanned boats is , the actual distance between unmanned boats a and g in the environment is , the triggering threshold is , and the triggering condition is .

[0012] The above-mentioned cooperative unmanned boat formation control method, the change rate triggering mechanism includes a heading change rate triggering mechanism and a speed change rate triggering mechanism. The heading change rate triggering mechanism is specifically as follows: the heading of unmanned boat a is , the heading change rate is , the triggering threshold is , and the triggering condition is: ; The speed change rate triggering mechanism is specifically as follows: the speed of unmanned boat a is , the speed change rate is , its triggering threshold is , and the triggering condition is: .

[0013] The above-mentioned cooperative unmanned boat formation control method, the communication delay triggering mechanism is specifically as follows: the communication delay is , the triggering threshold is , and the triggering condition is .

[0014] The beneficial effect of the present invention is that in terms of global path planning, an optimized genetic algorithm is adopted. By optimizing the fitness function, factors such as path length, obstacle avoidance cost, and sea condition impact cost are comprehensively considered to guide the algorithm to obtain a better fitness. Furthermore, the probability formula of the optimization selection method is optimized, so that individuals with higher fitness have a greater chance of being selected, allowing high-quality individuals to pass on more genes. Immediately afterwards, the crossover and mutation formulas are optimized, enabling the crossover probability and mutation probability to be adaptively adjusted according to the current iteration number and the maximum iteration number, improving the ability of the algorithm to find the global optimal solution.

[0015] In terms of obstacle avoidance, by improving the potential field function of the artificial potential field method, the gravitational force at key points is increased. Some key points are set in the global path to improve the resultant force direction and make it fit the reference path of the global planning, solving the problem that the traditional algorithm is prone to falling into local optimum. At the same time, the problem of path oscillation caused by this is solved, making local obstacle avoidance more reliable. Particular emphasis is placed on improving obstacle avoidance in the face of dynamic obstacles, further considering the influence degree of the speed and acceleration of dynamic obstacles on the repulsive force, so that the unmanned boat can more accurately avoid dynamic obstacles and sail more safely in a complex marine environment.

[0016] In terms of communication, through the multi-event trigger mechanism, various situations encountered in a weak communication marine environment are comprehensively considered. For example, obstacle detection, rate of change of speed, rate of change of heading, and possible member collisions and communication delays caused by the distance between formation members. The environmental adaptability is enhanced to ensure the safe operation of the unmanned boat formation in a weak communication marine environment. Brief Description of the Drawings

[0017] Figure 1 is a schematic diagram of the process of the present invention; Figure 2 is a flow chart of the genetic algorithm of the present invention; Figure 3 is a schematic diagram of the principle of applying key points of the present invention; Figure 4 is a flow chart of the relative distance trigger mechanism of the present invention; Figure 5 is a flow chart of the rate of change trigger mechanism of the present invention; Figure 6 is a flow chart of the communication delay trigger mechanism of the present invention. Detailed Embodiment

[0018] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below in conjunction with the drawings and specific embodiments.

[0019] As Figure 1 shown, this embodiment discloses a cooperative unmanned boat formation control method, and the path planning and formation control in a weak communication environment are realized through the following specific methods, and the specific process is as follows.

[0020] I. System Initialization and Basic Settings 1. Perform map rasterization processing on the marine environment, convert static obstacles into regular-shaped raster units; set dynamic obstacles, set their speeds and accelerations, define the raster occupancy state, and construct a standardized environment model.

[0021] 2. Parameter Configuration Formation parameters: Set parameters such as the number of unmanned boats and the reference formation.

[0022] Algorithm parameters: Set the gravitational coefficient and repulsive coefficient of the artificial potential field method; set the threshold of the multi-event trigger mechanism; at the same time, obtain the kinematic parameters such as the initial position and velocity of the unmanned boat.

[0023] II. Global path planning of the leader (based on genetic algorithm) The traditional genetic algorithm is simply optimized to be more capable of selecting a relatively excellent global path in a complex marine environment. The optimization is as follows: 1. Fitness function ; Among them is the fitness value, representing the quality of the current path; , , are weight coefficients, each representing different meanings. These three respectively represent the importance of path length, obstacle avoidance cost, and sea condition impact cost in the fitness function, and satisfy , and the respective weight ratios can be flexibly adjusted according to the actual marine environment and mission requirements; L represents the path length; represents the obstacle avoidance cost, which is used to measure the degree of the path passing through the obstacle area; represents the sea condition impact cost, reflecting the degree of influence of the path by adverse sea conditions (such as storms, strong ocean currents, etc.).

[0024] 2. Probability formula of the optimized selection method ; Among them, represents the probability that the th individual is selected for reproduction; represents the fitness value of the th individual; represents the number of individuals in the population.

[0025] 3. Adaptive crossover rate and mutation probability ; ; Among them, represents the crossover probability, that is, the probability of two individuals performing a crossover operation; represents the maximum value of the crossover probability; represents the minimum value of the crossover probability; represents the current iteration number; represents the maximum iteration number; Mutation probability, that is, the probability of an individual performing a mutation operation; Maximum value of the mutation probability; The minimum value of the mutation probability.

[0026] By comprehensively considering factors such as path length, obstacle avoidance cost, and sea condition influence cost through an optimized fitness function, a fitness value is given to each path individual to distinguish the quality of different paths and guide the algorithm to search in the direction of a better path. Furthermore, the probability formula of the optimized selection method is optimized, and the ratio of the fitness value of each individual to the sum of the fitness values of all individuals in the population is used as the probability of its being selected, so that individuals with high fitness have a greater chance of being selected, allowing high-quality individuals to pass on genes more and promoting the evolution of the population. Immediately afterwards, the crossover and mutation formulas are optimized so that the crossover probability and mutation probability can be adaptively adjusted according to the current iteration number and the maximum iteration number to enhance the exploratory ability in the initial stage of the algorithm and focus on local fine search in the later stage, so as to improve the ability of the algorithm to find the global optimal solution.

[0027] By optimizing these three aspects, a relatively excellent global planning path can be obtained, providing a good start for safe operation in the future. The flowchart of the global planning of the optimized genetic algorithm is as Figure 2 shown.

[0028] III. Improving the artificial potential field method for obstacle avoidance To effectively solve the problems of weak obstacle avoidance for dynamic obstacles and local optimality in the traditional artificial potential field method, the present invention improves it in combination with global path planning.

[0029] According to the improved artificial potential field method, calculate the gravitational force, repulsive force, and resultant force suffered by the unmanned boat . During the calculation process, the position of the obstacle, the speed and acceleration information of the dynamic obstacle are updated in real time to ensure the accuracy of the calculation of the gravitational force and repulsive force, thereby providing an accurate basis for the motion control of the unmanned boat. The following are the improvement aspects: 1. Construction of the gravitational field According to the task requirements and environmental information, use the global path planning algorithm to plan the global path of the unmanned boat formation, and set some key points on the global path to determine the key points on the path . By introducing the gravitational forces of multiple key points, guide the unmanned boat to move along the direction of the global path to avoid falling into local optimality. Its schematic diagram is as Figure 3 shown.

[0030] Considering the guiding effect of the key points on the global path on the unmanned boat, the gravitational potential field function is defined as: ; where is the gravitational potential field function; is the gravitational coefficient used to adjust the magnitude of the gravitational force; q is the current position of the unmanned boat; is the Key points; is the number of key points.

[0031] The gradient of the gravitational field is the gravity, and the expression is: ; where represents the gravity; this gravity expression is used to calculate the magnitude and direction of the gravity exerted on the unmanned boat at the current position, and to guide the unmanned boat to approach the key points of the global path.

[0032] 2. Construction of the repulsive force field For static obstacles, the repulsive force field function is set to be similar to the traditional method without adding too many elements to complicate it, and its definition is: ; where, is the static repulsive force function; is the static obstacle repulsive force coefficient; q is the current position of the unmanned boat; is the position of the static obstacle; is the radius of the influence range of the static obstacle.

[0033] When the unmanned boat enters the influence range of the static obstacle, it will be affected by the repulsive force, thus avoiding collision with the static obstacle.

[0034] The repulsive force formula of the static obstacle is: ; For dynamic obstacles, considering its speed and acceleration information, the dynamic obstacle repulsive force field function is defined as: ; where, represents the dynamic obstacle repulsive force potential field function; is the dynamic obstacle repulsive force coefficient; q is the current position of the unmanned boat; is the position of the dynamic obstacle at time; is the speed of the dynamic obstacle; is the acceleration of the dynamic obstacle; is the radius of the influence range of the dynamic obstacle; is the speed influence coefficient; is the acceleration influence coefficient.

[0035] This formula further considers the influence degree of the speed and acceleration of the dynamic obstacle on the repulsive force, so that the unmanned boat can avoid the dynamic obstacle more accurately.

[0036] When it is found during the operation that the dynamic obstacle is accelerating towards the unmanned boat, with the acceleration influence coefficient In this case, the repulsive force received by the unmanned boat will increase, prompting the unmanned boat to change its sailing direction faster; furthermore, adding the number 1 in the formula is also to prevent the situation where when the speed and acceleration of the dynamic obstacle are both very small, it will affect the value in the formula. In this way, even when the speed or acceleration of the dynamic obstacle is very small, it will not affect the obstacle avoidance of the unmanned boat.

[0037] The repulsive force of the dynamic obstacle, and its expression is: ; Among them, represents the repulsive force of the dynamic obstacle on the unmanned boat; represents the unit vector, and the direction is from the dynamic obstacle to the unmanned boat, which determines the direction of the repulsive force; , which has the same meaning as the corresponding part in the repulsive force potential field function.

[0038] The resultant force received by the unmanned boat is the vector sum of the gravitational force and the repulsive force, that is: ; Among them represents the resultant force received by the unmanned boat; represents the gravitational force received by the unmanned boat; represents the repulsive force of the static obstacle received by the unmanned boat; represents the repulsive force of the dynamic obstacle received by the unmanned boat.

[0039] This resultant force determines the movement direction and acceleration of the unmanned boat, enabling the unmanned boat to tend to the global path under the action of the gravitational force, and at the same time avoiding obstacles under the action of the repulsive force to achieve safe and efficient navigation.

[0040] IV. Setting of multi-event trigger mechanism To improve the efficiency and reliability of the unmanned boat formation control and balance the communication burden and computing resources, the present invention designs a variety of event trigger mechanisms.

[0041] 1. Relative distance trigger threshold: To enable the unmanned boat to maintain a safe distance during operation, avoid collisions with obstacles, and at the same time balance the distance between formations, avoid the increased communication burden caused by too large a spacing or the collision risk easily caused by too small a spacing, so as to maintain the stability of the formation. For this reason, the relative distance trigger thresholds for the same and different types are set; the priority of this is the highest, and safety is the top priority. Its simple flow chart is as Figure 4 shown.

[0042] Relative distance trigger for different types: Use sensors to continuously sense the obstacle information in the surrounding environment. When it is detected that an obstacle enters a certain dangerous area, an obstacle avoidance event is triggered.

[0043] Let the distance between the unmanned boat and the obstacle be , and the danger distance threshold be . The triggering condition is: .

[0044] The range of this danger area is determined by a preset threshold . When the distance between the obstacle and the unmanned boat is less than the threshold , the system considers that there is a collision risk, triggers an event and starts the corresponding obstacle avoidance algorithm to guide the unmanned boat to avoid the obstacle and ensure navigation safety.

[0045] The triggering of the same kind of relative distance is specifically as follows: The expected distance between unmanned boats is , the actual distance between unmanned boats a and g in the environment is , the triggering threshold is , and the triggering condition is . Among them, can be dynamically adjusted according to the size, speed of the unmanned boat and the actual application scenario.

[0046] When the absolute value of the difference between the actual distance and the expected distance exceeds the threshold, an event is triggered to adjust the movement of the unmanned boat, such as adjusting the speed or heading, to maintain an appropriate spacing and avoid communication delay caused by too far distance in a weak communication environment, or collision that may occur to formation members due to too close distance.

[0047] 2. Rate of change triggering: To respond in a timely manner to the dynamic changes of individual unmanned boats (such as accelerating, decelerating, turning), ensure formation coordination, and avoid affecting the overall stability due to individual mutations. When the rate of change of the unmanned boat exceeds a certain threshold, an event is triggered. The rates of change of speed and heading are designed. Its simple flowchart is as Figure 5 shown.

[0048] Heading rate of change triggering: Pay attention to the change of the heading of the unmanned boat. When the heading rate of change of unmanned boat a exceeds the set range, an event is triggered.

[0049] Let the heading of unmanned boat a be , the heading rate of change be , the triggering threshold be , and the triggering condition be: .

[0050] When the formation is sailing along a predetermined route, if the heading of a certain unmanned boat suddenly changes by a large margin and its change rate exceeds the threshold, it may be an emergency turn to avoid a suddenly emerging obstacle. After the event is triggered, the heading consistency of the formation can be adjusted in a timely manner to ensure the overall navigation safety of the formation and the accuracy of mission execution. Of course, the priority of event triggering is designed, and safety is the top priority. When both obstacle avoidance and heading change are triggered, obstacle avoidance takes precedence.

[0051] Trigger by speed change rate: Pay attention to the speed change of the unmanned boat. When the speed change rate of unmanned boat a exceeds the set range, the event is triggered.

[0052] Let the speed of unmanned boat a be , and the speed change rate be , and its trigger threshold is: , and the trigger condition is: .

[0053] Among them, It is set according to the power performance of the unmanned boat and the formation control requirements.

[0054] When the speed change rate of a certain unmanned boat exceeds the threshold, it indicates that the unmanned boat may be affected by external disturbances (such as large waves or water flow tides, etc.) and other events. At this time, the event is triggered so that the formation can make corresponding adjustments. For example, other following unmanned boats need to adjust their speeds to keep consistent with the leading unmanned boat to make the formation maintain coordination consistency.

[0055] 3. Trigger by communication delay: Continuous communication will consume limited communication resources, easily cause communication congestion, hinder the transmission of key instructions, and result in communication delay. Excessive communication delay will affect the real-time performance and accuracy of formation control, leading to problems in coordination. Its simple flow chart is as Figure 6 shown.

[0056] Set the communication delay trigger condition. When the communication delay exceeds a certain threshold, the event is triggered.

[0057] Let the communication delay be , and the trigger threshold be , and the trigger condition is: .

[0058] Among them, It can be determined according to the performance of the communication system and the real-time requirements of formation control.

[0059] When the communication delay exceeds the threshold, an event is triggered to promptly detect and resolve problems in the communication system, such as switching communication frequency bands or enabling backup communication links to ensure the normal operation of the formation. The trigger threshold for this communication delay is generally set relatively high, and an event will not be triggered just because of time delay. When an event is triggered, it proves that the communication delay has seriously affected the mission efficiency.

[0060] V. Process Loop and Task End Collect the formation status in real time. Each unmanned boat collects its own position, speed and other status information, as well as the positions of surrounding obstacles and other information through the sensors it carries. At the same time, monitor relevant information such as whether the communication is delayed.

[0061] Detect whether the formation has reached the target point: if it has reached, the task ends; if not, loop to perform environment update and path planning, and continue to run until the end point is reached.

[0062] The present invention combines the use of an optimized genetic algorithm for global planning, and then uses an improved artificial potential field method combined with multi-event triggering to solve the problems that traditional algorithms are prone to falling into local path optimality and the resulting path oscillation; by setting a multi-event triggering mechanism, it effectively avoids the problems of real-time and accurate information interaction that are difficult to guarantee by traditional path planning and obstacle avoidance algorithms, as well as the excessive consumption of communication resources caused by continuous communication, its resulting communication delay and communication cost problems. Furthermore, the combination of event triggering such as setting a relative distance threshold and the improved artificial potential field method avoids problems such as collisions with obstacles or in-team collisions, and uses the rate of change of speed, the rate of change of heading, whether the communication is delayed, etc. to judge the formation status and adjust the formation status in a timely manner; the present invention also uses a leader-follower algorithm for cooperative formation control to ensure the efficient operation of the entire formation. The present invention can enable the formation system to maintain smooth communication and stable formation in a dynamic marine environment with weak communication, and achieve safe and successful arrival at the target.

[0063] The above embodiments are only exemplary embodiments of the present invention and are not used to limit the present invention. Those skilled in the art can make various modifications or equivalent replacements to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present invention.

Claims

1. A cooperative unmanned boat formation control method, characterized in that, It includes the following steps: Step 1, Initialization: Preprocess the marine environment map, initialize the parameters, and set the reference formation when the unmanned boat formation moves forward; Step 2, Global path planning: Use the genetic algorithm to screen out the global path for the weak communication marine environment; Step 3, Path construction and obstacle avoidance: According to the characteristics of the unmanned boat formation, the reference formation set in Step 1, and the global path obtained in Step 2, when the follower starts, it quickly adjusts and actively forms a reference formation with the leader. When encountering obstacles during the operation, use the improved artificial potential field method and multi-event triggering mechanism for local obstacle avoidance; Step 4, Information update and formation adjustment: Use the leader-follower algorithm and multi-event triggering mechanism to locally adjust the formation of the formation after obstacle avoidance, and update the position and state information of the unmanned boat formation; Step 5, Process judgment and loop: The formation system continuously judges whether the formation reaches the target point. If it reaches, it ends; if it does not reach, it repeats Steps 3-4 until the formation reaches the target point; The multi-event triggering mechanism in Step 3 and Step 4 includes a relative distance triggering mechanism, a change rate triggering mechanism, and a communication delay triggering mechanism.

2. The collaborative unmanned boat formation control method according to claim 1, wherein Specifically, Step 1 is as follows: Perform rasterization processing on the marine environment map, convert static obstacles into raster units with regular shapes; set dynamic obstacles, set their speeds and accelerations, define the raster occupancy state, and construct a standardized environment model; set the parameters of the unmanned boat formation, the gravitational coefficient and repulsion coefficient of the artificial potential field method; set the thresholds of the multi-event triggering mechanism; obtain the initial kinematic parameters of the unmanned boat.

3. A cooperative unmanned boat formation control method according to claim 1, characterized in that Specifically, Step 2 is as follows: Step 2.1, Set the initial parameters of the genetic algorithm and generate an initial population; Step 2.2, Calculate the fitness of the individual population through the optimized fitness function, select the optimal individual through the optimized selection method probability formula, and generate a new generation of population through adaptive crossover and mutation; Step 2.3, Judge whether the satisfaction condition for iterative stop is met. If it is met, output the optimal path. If it is not met, repeat Step 2.2; Specifically, the optimized fitness function is as follows: ; Among them, is the fitness value, representing the quality of the current path; , , are weight coefficients, respectively representing the importance degrees of path length, obstacle avoidance cost, and sea condition influence cost in the fitness function, and satisfying ; L represents the path length; represents the obstacle avoidance cost, used to measure the degree of the path passing through the obstacle area; represents the sea condition influence cost, reflecting the degree of the path affected by adverse sea conditions.

4. A cooperative unmanned boat formation control method according to claim 3, characterized in that, Specifically, the optimized selection method probability formula in Step 2.2 is as follows: ; Among them, represents the probability that the th individual is selected for reproduction; represents the fitness value of the th individual; represents the number of individuals in the population; Specifically, the specific formulas for adaptive crossover and mutation in Step 2.2 are as follows: ; ; Among them, represents the crossover probability; represents the maximum value of the crossover probability; represents the minimum value of the crossover probability; represents the current iteration number; represents the maximum iteration number; Mutation probability; represents the maximum value of the mutation probability; represents the minimum value of the mutation probability.

5. A cooperative unmanned boat formation control method according to claim 1, characterized in that Specifically, the improved artificial potential field method in Step 3 is as follows: Calculate the resultant force received by the unmanned boat: ; Among them represents the resultant force on the unmanned boat; represents the gravitational force on the unmanned boat; represents the repulsive force of the static obstacle on the unmanned boat; represents the repulsive force of the dynamic obstacle on the unmanned boat; ; Among them, is the gravitational coefficient, which is used to adjust the magnitude of gravity; q is the current position of the unmanned boat; is the th key point on the global path; is the number of key points; ; ; Among them, is the static repulsive force function; is the static obstacle repulsive force coefficient; q is the current position of the unmanned boat; is the position of the static obstacle; is the radius of the influence range of the static obstacle; ; Among them, represents a unit vector, with the direction pointing from the dynamic obstacle to the unmanned boat, determining the direction of the repulsive force; , is the repulsive force coefficient of the dynamic obstacle; q is the current position of the unmanned boat; is at the dynamic obstacle in the position at time; is the velocity of the dynamic obstacle; is the acceleration of the dynamic obstacle; is the radius of the influence range of the dynamic obstacle; is the velocity influence coefficient; is the acceleration influence coefficient.

6. A cooperative unmanned boat formation control method according to claim 1, characterized in that The relative distance trigger mechanism specifically includes heterogeneous relative distance trigger and homogeneous relative distance trigger. The heterogeneous relative distance trigger is specifically as follows: The sensor is used to continuously sense the obstacle information in the surrounding environment. The distance between the unmanned boat and the obstacle is , and the dangerous distance threshold is . The trigger condition is ; The above-mentioned same-kind relative distance triggering specifically means: the expected distance between unmanned boats is , the actual distance between unmanned boats a and g in the environment is , the triggering threshold is , and the triggering condition is .

7. A cooperative unmanned boat formation control method according to claim 1, characterized in that The The rate-of-change trigger mechanism includes a heading rate-of-change trigger mechanism and a speed rate-of-change trigger mechanism. The specific content of the heading rate-of-change trigger mechanism is as follows: The heading of unmanned boat a is , the rate of change of heading is , the trigger threshold is , and the trigger condition is: ; The specific speed change rate triggering mechanism is as follows: The speed of the unmanned boat a is , and the speed change rate is . Its triggering threshold is , and the triggering condition is: .

8. A cooperative unmanned boat formation control method according to claim 1, characterized in that, The specific communication delay triggering mechanism is as follows: The communication delay is , the triggering threshold is , and the triggering condition is .

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