Unmanned ship safety cluster control system based on index control barrier function

Abstract

The invention discloses an unmanned ship safety cluster control system based on an index control barrier function, and belongs to the technical field of unmanned ship control. Comprising a leading ship cluster controller carried on a leading unmanned ship and N tracking ship cluster controllers respectively carried on N following unmanned ships, the leader ship cluster controller comprises a path tracking guidance law module, a path tracking trajectory error module, a leader ship secondary optimization module and a leader ship index control barrier function constraint module; the tracking ship cluster controller comprises a virtual reference point module, a cluster guidance law module, a cluster trajectory error module, a potential function calculation module, a tracking ship quadratic optimization module and a tracking ship index control barrier function constraint module. On the basis of the unmanned ship cluster, obstacles are avoided and cluster control is completed under the condition that clear cognition on the boundary of the obstacles is not needed, and the environment adaptability and flexibility of the unmanned ship formation in a complex sea area are improved.

Description

Technical Field

[0001] This invention relates to the field of multi-unmanned vessel control technology, studies the unmanned vessel swarm safety priority swarm control problem, and proposes an unmanned vessel safety swarm control system based on an exponential control barrier function. Background Technology

[0002] In recent years, unmanned surface vessels (USVs) have become crucial for performing maritime missions. However, due to the limitations, low efficiency, and high safety risks associated with individual USVs, multi-USV swarm control methods have attracted widespread attention from researchers both domestically and internationally. Multi-USV cooperative systems can significantly improve the efficiency of completing complex or hazardous tasks. A key research focus in the field of multi-USV systems is the development of distributed control strategies that rely on local interactions to achieve various collective behaviors, such as assembly, formation, containment, and swarming. Specifically, swarm control, through USV cooperation, achieves high robustness, scalability, and efficient resource utilization, adapting to the needs of complex missions.

[0003] Ensuring the safety of unmanned surface vessels (USVs) has become a crucial issue in establishing swarm control behavior. Various collision avoidance methods have been proposed both domestically and internationally, such as artificial potential functions, model predictive control, deep reinforcement learning, and control obstacle functions. Among these, combining control obstacle functions with quadratic programming is a novel approach. It solves the optimization problem online and uses safety as a hard constraint to ensure the safety of USVs. However, existing collision avoidance methods based on control obstacle functions generally require a clear understanding of obstacle boundaries, which is often difficult to accurately obtain in real-world environments. Therefore, enabling USVs to avoid unknown obstacles in complex dynamic environments using only locally detected information, without needing to pre-define obstacle boundaries, thereby enhancing the system's flexibility and environmental adaptability, is of significant research importance. Summary of the Invention

[0004] In view of the shortcomings of existing technologies, this invention provides an unmanned surface vessel (USV) swarm control system based on an exponential obstacle control function. This invention considers that USVs can measure the relative position vector (nearest collision vector) of the nearest point of each obstacle within their sensing range in a global coordinate system based on their onboard radar and shipborne sensors. First, a virtual reference point control input is designed based on an artificial potential function, and nominal guidance laws are designed for the lead USV and follower USVs to follow a given parameterized path and virtual reference point, thereby achieving swarm control behavior. Then, an exponential obstacle control function is designed based on the nearest collision vector to constrain each USV to avoid collisions with obstacles and neighboring USVs. Finally, a constrained quadratic programming problem is constructed to calculate the optimal guidance signal. Therefore, under this control method, the USV swarm can avoid obstacles and complete swarm control without needing a clear understanding of obstacle boundaries, improving the environmental adaptability and flexibility of the USV formation in complex sea areas.

[0005] The technical means employed in this invention are as follows: An unmanned surface vessel (USV) safety swarm control system based on an exponential control obstacle function includes: a leader ship swarm controller mounted on the leader USV and N tracking ship swarm controllers mounted on N follower USVs respectively; the leader ship swarm controller and the tracking ship swarm controllers interact with each other via a communication network. The leader ship swarm controller includes a path tracking guidance law module, a path tracking trajectory error module, a leader ship secondary optimization module, and a leader ship exponential control obstacle function constraint module; the tracker ship swarm controller includes a virtual reference point module, a swarm guidance law module, a swarm trajectory error module, a potential function calculation module, a tracker ship secondary optimization module, and a tracker ship exponential control obstacle function constraint module.

[0006] Furthermore, in the leader ship cluster controller, the path tracking trajectory error module receives the position and parameterized path of the leader ship and outputs the leader ship trajectory error; the leader ship exponential control obstacle function constraint module receives the position, bow angle, and nearest collision point position of the leader ship and outputs the exponential control obstacle function; the path tracking guidance law module receives the leader ship trajectory error and outputs the nominal guidance law; the leader ship quadratic optimization module receives the nominal guidance law and the exponential control obstacle function and outputs the optimal guidance law.

[0007] Furthermore, in the tracking ship cluster controller, the potential function calculation module receives the current tracking ship position and the positions of neighboring tracking ships, and outputs the potential function calculation gradient; the virtual reference point module receives the potential function calculation gradient and outputs the virtual reference point position; the cluster trajectory error module receives the current tracking ship position and the virtual reference point position, and outputs the tracking ship trajectory error; the cluster guidance law module receives the tracking ship trajectory error and outputs the nominal guidance law; the tracking ship exponential control obstacle function constraint module receives the current tracking ship position, bow angle, and nearest collision point position, and outputs the exponential control obstacle function; the tracking ship quadratic optimization module receives the nominal guidance law and the exponential control obstacle function, and outputs the optimal guidance law.

[0008] Furthermore, the leader ship index control obstacle function constraint module outputs the static obstacle index control obstacle function constraint function according to the following formula. and adjacent ship index control barrier function constraint function :

[0009]

[0010] in, Represents the minimum safe distance, variable satisfy and , The leader ship corresponds to the first The x and y coordinates of the nearest collision point of a static obstacle The horizontal and vertical coordinates representing the position of the leader ship;

[0011]

[0012] in, The x and y coordinates represent the nearest collision point between the leading ship and the neighboring ship.

[0013] Furthermore, the path tracking guidance law module outputs the nominal guidance law of the leader unmanned surface vessel according to the following formula:

[0014] in, The tangent angle of the parameterized path. , For the track angle, , The horizontal and vertical coordinates represent the error in the leader ship's trajectory. Positive control gain, , , For reference speed, Sideslip angle, , The total speed of the leading ship is defined as... , It is the tangential velocity of the parameterized path, defined as , As an intermediate variable, it is defined as follows: , The virtual heading angle of the lead ship, Forward distance.

[0015] Furthermore, the leader ship's secondary optimization module obtains the unmanned vessel's safe guidance angular velocity by solving the following secondary optimization problem:

[0016] in, , , and yes Class function, Represents along the vector field Li Daoshu, Represents along the vector field Li Daoshu, , ; The optimal guidance law is expressed as: .

[0017] Furthermore, the following potential function and its gradient are introduced into the potential function calculation module:

[0018]

[0019] in , , , Indicates the location of the unmanned vessel. for and Minimum safe distance between For the maximum communication range, and , Consider a Lyapunov function as follows:

[0020] Its gradient is:

[0021] in It is an adjacency matrix used to describe the connectivity of a graph. If the first... The unmanned ship and the first If the unmanned vessels can communicate with each other, then ,otherwise .

[0022] Furthermore, the virtual reference point module outputs the first value according to the following formula. Virtual reference point of a follower unmanned vessel :

[0023] in The damping coefficient is... The velocity of the virtual reference point, To control the input, It is the first The virtual reference point position of the tracking unmanned vessel.

[0024] Furthermore, the swarm guidance law module outputs the nominal guidance law for tracking the unmanned surface vessel according to the following formula:

[0025] in, , , For the track angle, Positive control gain, , , , For positive integers, For forward distance, For reference speed, Sideslip angle, .

[0026] Furthermore, the tracking ship index control obstacle function constraint module outputs the static obstacle index control obstacle function constraint function according to the following formula. and adjacent ship index control barrier function constraint function :

[0027]

[0028] in It corresponds to the nearest collision point of a static obstacle, parameter Represents the minimum safe distance, variable It should be small enough to meet the requirements. and ;

[0029]

[0030] in, It corresponds to the nearest collision point of the neighboring unmanned vessel, parameters Represents the minimum safe distance, variable It should be small enough to meet the requirements. and .

[0031] Compared with the prior art, the present invention has the following advantages: 1. The control method based on the exponential control obstacle function proposed in this invention can effectively avoid collisions with obstacles and nearby unmanned vessels.

[0032] 2. The unmanned vessel safety priority swarm control method proposed in this invention does not require a clear understanding of obstacle boundaries. It only needs to measure the nearest collision point to achieve collision avoidance, thereby improving the unmanned vessel's adaptability to the environment.

[0033] 3. Compared with existing collision avoidance methods based on obstacle function control, which require simultaneous adjustment of forward velocity and angular velocity, the control method proposed in this invention can avoid collisions by optimizing angular velocity alone, and the forward velocity is flexible and variable. Attached Figure Description

[0034] 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.

[0035] Figure 1 This is the architecture of an unmanned surface vessel safety swarm control system based on an exponentially controlled obstacle function in this embodiment of the invention.

[0036] Figure 2 This is a communication topology diagram between unmanned vessels in an example of the present invention.

[0037] Figure 3 This is a diagram illustrating the effect of safe cluster control of unmanned surface vessels in an example of the present invention.

[0038] Figure 4 This is a numerical diagram of the heading, forward speed, and angular velocity of the unmanned vessel swarm safety swarm control in an example of the present invention.

[0039] Figure 5 This is a numerical diagram of the modulus of the minimum collision vector between an unmanned vessel and a static obstacle in the unmanned vessel swarm safety cluster control of an embodiment of the present invention.

[0040] Figure 6 This is a numerical diagram of the modulus of the minimum collision vector between unmanned vessels in the unmanned vessel swarm safety cluster control of the present invention. Detailed Implementation

[0041] 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.

[0042] like Figure 1 As shown, the present invention provides an unmanned vessel safety swarm control system based on an exponential control obstacle function, comprising: a leader vessel swarm controller mounted on the leader unmanned vessel and N tracking vessel swarm controllers mounted on N follower unmanned vessels respectively; the leader vessel swarm controller and the tracking vessel swarm controllers interact with each other through a communication network.

[0043] The leader ship cluster controller includes a path tracking guidance law module, a path tracking trajectory error module, a leader ship secondary optimization module, and a leader ship exponential control obstacle function constraint module. The path tracking trajectory error module receives the leader ship's position and parameterized path, and outputs the leader ship trajectory error. The parameterized path is the leader ship's path, the specific form of which is determined by mission requirements; the system directly receives this path parameter as input. The leader ship exponential control obstacle function constraint module receives the leader ship's position, bow angle, and nearest collision point position, and outputs an exponential control obstacle function. The path tracking guidance law module receives the leader ship trajectory error and outputs a nominal guidance law. The leader ship secondary optimization module receives the nominal guidance law and the exponential control obstacle function, and outputs an optimal guidance law.

[0044] The tracking ship swarm controller includes a virtual reference point module, a swarm guidance law module, a swarm trajectory error module, a potential function calculation module, a tracking ship quadratic optimization module, and a tracking ship exponential control obstacle function constraint module. The potential function calculation module receives the current tracking ship position and the positions of neighboring tracking ships and outputs the potential function calculation gradient. The virtual reference point module receives the potential function calculation gradient and outputs the virtual reference point position. The swarm trajectory error module receives the current tracking ship position and the virtual reference point position and outputs the tracking ship trajectory error. The swarm guidance law module receives the tracking ship trajectory error and outputs the nominal guidance law. The tracking ship exponential control obstacle function constraint module receives the current tracking ship position, bow angle, and nearest collision point position and outputs the exponential control obstacle function. The tracking ship quadratic optimization module receives the nominal guidance law and the exponential control obstacle function and outputs the optimal guidance law.

[0045] In this application, the kinematic model of the unmanned surface vessel is as follows:

[0046] in This is the location of the unmanned ship in the Earth coordinate system; It is the bow yaw angle; It is the sway velocity of the unmanned vessel in the ship's coordinate system; It is the sway velocity of the unmanned vessel in the ship's coordinate system; It is the bow roll rate.

[0047] Assuming the actual speed and direction of the unmanned vessel can be measured, the kinematic model of the unmanned vessel can be rewritten as follows:

[0048] in This is the actual speed of the unmanned vessel. It is the actual direction angle of movement of the unmanned vessel; It is the sideslip angle; .

[0049] The output of the collision vector module The unmanned ship corresponds to the first The nearest collision point of a static obstacle and collision vector Connected to the input of the exponentially controlled barrier function. The output of the collision vector module is the first... The unmanned ship corresponds to the first The nearest collision point of the unmanned vessel and collision vector It is connected to the input of the exponentially controlled barrier function.

[0050] The leader ship cluster controller consists of the following modules: path tracking guidance law module, path tracking trajectory error module, leader ship quadratic optimization module, and leader ship exponential control obstacle function constraint module. The tracking ship cluster controller consists of the following modules: virtual reference point module, cluster guidance law module, cluster trajectory error module, potential function calculation module, tracking ship secondary optimization module, and leader ship exponential control obstacle function constraint module.

[0051] In the leader ship cluster controller, the leader ship outputs the position of the unmanned vessels. and parameterized path Connected to the input of the path tracking trajectory error module. The output of the leader ship indicates the position of the unmanned surface vessel. Bow roll angle The nearest collision point at the output of the collision vector module , Connected to the input of the leader ship index control obstacle function constraint module. The output of the path tracking trajectory error module is the leader ship trajectory error. It is connected to the input terminal of the path tracking guidance law module. The output terminal of the path tracking guidance law module is nominally labeled with the guidance law. The output of the leader ship index control obstacle function constraint module is the index control obstacle function. It is connected to the input of the secondary optimization module. The output of the leader ship's secondary optimization module is the optimal guidance law. It is connected to the input end of the leader ship.

[0052] In the In the tracking ship cluster controller, the first The output of the tracking ship The location of the tracking ship With the The location of the neighboring ship tracking the ship It is connected to the input terminal of the potential function calculation module. The tracking ship outputs the location of the unmanned vessel. Bow roll angle The nearest collision point at the output of the collision vector module , It is connected to the input of the obstacle function constraint module for the tracking ship's exponential control. The output of the potential function calculation module calculates the gradient of the potential function. Connected to the input of the virtual reference point module. The output of the virtual reference point module is... Virtual reference point position of the tracking ship and the The output of the tracking ship The location of the tracking ship Connected to the input of the cluster trajectory error module. The output of the cluster trajectory error module tracks the ship's trajectory error. It is connected to the input terminal of the swarm guidance law module. The output terminal of the swarm guidance law module is labeled with the guidance law. and the output of the index control barrier function constraint module for tracking ships It is connected to the input of the secondary optimization module. The output of the tracking ship's secondary optimization module contains the optimal guidance law. With the The input terminals of the tracking ships are connected.

[0053] Position of the unmanned vessel at the output end of the leader ship Connected to the input of the communication network, the output of the communication network shows the location of the unmanned vessel's neighbors. Connected to the input end of the leader ship. The position of the unmanned ship at the output end of the follower ship Connected to the input of the communication network, the output of the communication network shows the location of the unmanned vessel's neighbors. With the The input terminal of the following ship is connected.

[0054] A. Leader Ship Collision Vector Module Arrive at the obstacle perpendicularly from the current position of the 0th unmanned vessel. The nearest point on the boundary is the nearest collision point. Collision vector for:

[0055] in .

[0056] Collision point location measured by sensors As input to the collision vector module, the output of the collision vector module corresponds to the first... The nearest collision point of a static obstacle and collision vector The nearest collision point corresponding to the Nth neighboring unmanned vessel. and collision vector .

[0057]

[0058] B. Leader Ship Index Control Obstacle Function Constraint Module Output of unmanned surface vessel (USV) location Bow roll angle and corresponding to the The nearest collision point of a static obstacle and the nearest collision point corresponding to the adjacent ship This serves as the input signal to the leader ship's index control obstacle function constraint module. The output of the leader ship's index control obstacle function constraint module is the static obstacle index control obstacle function constraint function. and adjacent ship index control barrier function constraint function .

[0059] Static obstacle index control obstacle function constraint function for:

[0060] Where the distance function Defined as:

[0061] in It corresponds to the nearest collision point of a static obstacle, parameter Represents the minimum safe distance, variable It should be small enough to meet the requirements. and .

[0062] Adjacent Ship Index Control Barrier Function for:

[0063] Where the distance function Defined as:

[0064] in It corresponds to the nearest collision point of the neighboring unmanned vessel, parameters Represents the minimum safe distance, variable It should be small enough to meet the requirements. and .

[0065] C. Path tracking trajectory error module Position of the unmanned vessel at the output end of the leader ship With the given leader unmanned vessel parameterized path This serves as the input signal to the path tracking trajectory error module. The output of the path tracking trajectory error module is the trajectory error. Lateral trajectory error .

[0066]

[0067] in , is defined as the tangential angle on the path.

[0068] D. Path tracking guidance law module The output of the path tracking trajectory error module is the trajectory error. Lateral trajectory error This serves as the input signal to the path tracking guidance law module. The output of the path tracking guidance law module is the nominal guidance law. .

[0069] The nominal guidance law for the leader unmanned surface vessel is:

[0070] in, The tangent angle of the parameterized path. , For the track angle, , The horizontal and vertical coordinates represent the error in the leader ship's trajectory. Positive control gain, , , For reference speed, Sideslip angle, , The total speed of the leading ship is defined as... , It is the tangential velocity of the parameterized path, defined as , As an intermediate variable, it is defined as follows: , The virtual heading angle of the lead ship, Forward distance. , It is a positive number. E. Leader Ship Secondary Optimization Module The output nominal guidance law of the path tracking guidance law module and adjacent ship index control barrier function This serves as the input signal to the secondary optimization module. The output of the secondary optimization module is the optimal guidance law. .

[0071] The safe guidance angular velocity of the unmanned surface vessel is obtained by solving the quadratic optimization problem:

[0072] in, , , and yes Function-like.

[0073] Therefore, the optimal guidance law is expressed as:

[0074] F. Tracking Ship Collision Vector Module From the The unmanned boat is currently positioned to reach the obstacle vertically. The nearest point on the boundary is the nearest collision point. Collision vector for:

[0075] in .

[0076] Collision point location measured by sensors As input to the collision vector module, the output of the collision vector module corresponds to the first... The nearest collision point of a static obstacle and collision vector The nearest collision point corresponding to the Nth neighboring unmanned vessel. and collision vector .

[0077]

[0078] G. Potential Function Calculation Module No. The output of the tracking ship The location of the tracking ship With the The position of the ship next to the ship As the input signal to the potential function calculation module, the output of the potential function calculation module is the gradient of the potential function. .

[0079] To ensure stable formation behavior, the following potential function and its gradient are introduced:

[0080] in , , , Indicates the location of the unmanned vessel. for and Minimum safe distance between For the maximum communication range, and .

[0081] Consider a Lyapunov function as follows:

[0082] Its gradient is:

[0083] in It is an adjacency matrix used to describe the connectivity of a graph. If the first... The unmanned ship and the first If the unmanned vessels can communicate with each other, then ,otherwise

[0084] H. Virtual Reference Point Module The potential function calculation module outputs the potential function to calculate the gradient. As the input signal to the virtual reference point module, the output of the virtual reference point module is the first... Virtual reference point of a follower unmanned vessel .

[0085]

[0086] in The damping coefficient is... For speed, For controlling input. It is the first The virtual reference point position of the tracking unmanned vessel.

[0087] The control input design for the virtual reference point is as follows:

[0088] in Positive control gain, It is a positive number.

[0089] Based on equations (9) and (10), the virtual reference point can be obtained. .

[0090] I. Cluster Trajectory Error Module The position of the unmanned ship at the output end of the following ship The output of the virtual reference point module Virtual reference point of a follower unmanned vessel This serves as the input signal to the cluster trajectory error module. The output of the cluster trajectory error module is the trajectory error. Lateral trajectory error .

[0091]

[0092] in , is defined as the tangential angle on the path.

[0093] J. Swarm Guidance Law Module The output of the cluster trajectory error module is along the trajectory error. Lateral trajectory error This serves as the input signal to the swarm guidance law module. The output of the swarm guidance law module is the nominal guidance law. .

[0094] The nominal guidance law for follower unmanned surface vessels is as follows:

[0095] in Positive control gain, , , , For positive integers, For forward distance, For reference speed, For the track angle, Sideslip angle, .

[0096] K. Tracking Ship Index Control Barrier Function Constraint Module Output of unmanned surface vessel (USV) location Bow roll angle and corresponding to the The nearest collision point of a static obstacle and the nearest collision point corresponding to the adjacent ship This serves as the input signal to the tracking ship's exponential control obstacle function constraint module. The output of this module is the static obstacle exponential control obstacle function constraint function. and adjacent ship index control barrier function constraint function .

[0097] Static obstacle index control obstacle function constraint function for:

[0098] Where the distance function Defined as:

[0099] in It corresponds to the nearest collision point of a static obstacle, parameter Represents the minimum safe distance, variable It should be small enough to meet the requirements. and .

[0100] Adjacent Ship Index Control Barrier Function for:

[0101] Where the distance function Defined as:

[0102] in It corresponds to the nearest collision point of the neighboring unmanned vessel, parameters Represents the minimum safe distance, variable It should be small enough to meet the requirements. and .

[0103] L. Tracking Ship Secondary Optimization Module The output nominal guidance law of the swarm guidance law module and adjacent ship index control barrier function This serves as the input signal to the secondary optimization module. The output of the secondary optimization module is the optimal guidance law. .

[0104] The safe guidance angular velocity of the unmanned surface vessel is obtained by solving the quadratic optimization problem:

[0105] in, , , and yes Function-like.

[0106] Therefore, the optimal guidance law is expressed as:

[0107] To better explain the present invention, a specific example of seven unmanned surface vessels (USVs) will be used below for further illustration. Each USV in the system satisfies the motion model shown in formula (1). The control objective of these seven USVs is to establish and maintain a swarm control formation from any position. During the swarm behavior, the USVs, based on local detection information, achieve collision avoidance with convex obstacles of arbitrary shapes and neighboring USVs. The communication topology between the leader and the tracking drone is as follows: Figure 2 As shown, the simulation results are as follows: Figure 3-6 As shown. Figure 3 As can be seen, guided by the given parametric path and virtual reference point, the seven unmanned surface vessels (USVs) formed a cluster formation, with the distance between adjacent USVs remaining constant. Furthermore, no collisions occurred between any USV and obstacles or neighboring vessels. Figure 4 The data shows the changes in the unmanned vessel's heading, forward speed, and angular velocity over time. It can be seen that the unmanned vessel's heading has become synchronized, and its forward speed and angular velocity have become consistent. Figure 5 Let represent the magnitude of the minimum collision vector between the unmanned vessel and two static obstacles. It can be seen that no collision occurred between the unmanned vessel and the static obstacles. Figure 6 This represents the magnitude of the minimum collision vector between the unmanned vessels. It can be seen that no collisions occurred between the unmanned vessels.

[0108] 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. An unmanned ship safety swarm control system based on an exponential control barrier function, characterized by, The application relates to a leader-follower swarm control system for unmanned ships. The leader-follower swarm control system comprises a leader swarm controller carried on a leader unmanned ship and N follower swarm controllers carried on N follower unmanned ships. The leader-follower swarm control system comprises a path tracking trajectory error module, a leader ship quadratic optimization module and a leader ship exponential control barrier function constraint module. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error.

2. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 1, characterized in that, The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law.

3. The safety swarm control system for unmanned surface vehicles based on an exponential barrier function of claim 1, wherein, The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error.

4. The safety swarm control system for unmanned surface vehicles based on an exponential barrier function of claim 2, wherein, The leading ship index control barrier function constraint module outputs a static barrier index control barrier function constraint function according to the following formula and the neighboring ship index control barrier function constraint function : wherein, denotes the minimum safety distance, the variable satisfies and , denotes the horizontal and vertical coordinates of the closest collision point of the leading vessel corresponding to the first static obstacle, denotes the horizontal and vertical coordinates of the leading vessel position; wherein, denotes the transverse and longitudinal coordinates of the closest point of approach of the leading ship to the neighboring ship.

5. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 4, characterized in that, The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. wherein is the tangential angle of the parameterized path, , is the track angle, , are the lateral and longitudinal coordinates of the leader vessel trajectory error, is the positive control gain, , , is the reference speed, is the sideslip angle, , is the total speed of the leader vessel, defined as , is the tangential speed of the parameterized path, defined as , is an intermediate variable, defined as , is the virtual heading angle of the leader vessel, is the look-ahead distance.

6. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 5, characterized in that, The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. wherein , , and are class functions, denotes the Lie derivative along the vector field , denotes the Lie derivative along the vector field , , ; The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. 。 7. The exponential control barrier function based swarm control system for safety of unmanned surface vehicles according to claim 3, wherein, The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. wherein , , , represents the position of the unmanned ship, is with the minimum safety distance between, is the maximum communication range, and , The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. wherein is an adjacency matrix describing the connectivity of the graph, if there is a communication between the i-th unmanned ship and the j-th unmanned ship, , otherwise .​​ 8. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 7, characterized in that, The virtual reference point module outputs whether the first virtual reference point of the follower unmanned ship : wherein is a damping coefficient, is the velocity of the virtual reference point, is the control input, is the position of the virtual reference point of the first tracked unmanned ship.

9. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 8, characterized in that, The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs an optimal guidance law. The path tracking trajectory error module receives the position of the leader ship and a parameterized path, and outputs a leader ship trajectory error. The leader ship quadratic optimization module receives the nominal guidance law and the exponential control barrier function, and outputs wherein , , is a track angle, is a positive control gain, , , , is a positive constant, is a look-ahead distance, is a reference velocity, is a sideslip angle, .

10. The safety swarm control system for unmanned ships based on an exponential barrier function according to claim 9, characterized in that, The following formula is used to output the static obstacle index control barrier function constraint function by the tracking ship index control barrier function constraint module and the neighboring ship index control barrier function constraint function : wherein is the closest collision point corresponding to the static obstacle, the parameter represents the minimum safety distance, the variable should be small enough to satisfy and ; wherein, is the closest collision point corresponding to the neighbor unmanned ship, the parameter represents the minimum safety distance, the variable should be small enough to satisfy and .

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