Ship-uav bridge area cooperative collision avoidance control method based on geometric speed barrier
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DALIAN MARITIME UNIVERSITY
- Filing Date
- 2026-05-14
- Publication Date
- 2026-06-12
Smart Images
Figure CN122195098A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ship-UAV motion control technology, and in particular to a ship-UAV bridge area cooperative collision avoidance control method based on geometric velocity obstacles. Background Technology
[0002] With the rapid development of the global shipping industry and the continuous growth of inland waterway transport volume, the frequency of ships passing through bridge areas has increased significantly. As a key node in waterways, the safety of bridges directly affects regional logistics efficiency and public safety.
[0003] In existing research, although 3D mapping guidance has constructed a guidance framework for ship-UAV cooperative bridge systems, the ship-UAV path tracking control algorithm based on 3D mapping guidance has the following obvious shortcomings in the cooperative control tasks: (1) For obstacles such as large-sized bridges, the collision avoidance path must consider both the nearest collision avoidance route and the three-dimensional structure of the obstacle. Traditional LVS-LVA guidance algorithms typically generate guidance reference signals by analyzing the time parameters of straight and turning segments. However, in the scenario of UAVs cooperating to cross bridges, collision avoidance operations must be performed in three-dimensional space. In this case, relying solely on UAV-cooperative yaw angle planning mapped onto a two-dimensional plane cannot effectively handle the dynamic changes in the altitude direction, i.e., the OZ direction. Therefore, traditional guidance methods limited to the mapping plane are difficult to meet the refined guidance requirements of such ship-UAV cooperative bridge crossing systems for three-dimensional collision avoidance. (2) Since the control commands generated by the control system need to be transmitted to the actuators in real time to eliminate ship-UAV coordination errors, continuous control signals may cause the actuators to operate frequently. This will not only cause unnecessary wear and tear on the actuators, but also increase the burden on the communication channel. Traditional event triggering mechanisms only use error as the triggering criterion. However, in collaborative bridge crossing tasks, the system needs to respond to dynamic changes in speed, so the speed change mechanism must also be included in the triggering condition. To solve this problem, a dual event triggering mechanism that comprehensively considers error state and speed change is urgently needed. This mechanism needs to optimize resource utilization while ensuring control accuracy, and achieve an effective balance between accuracy and resource consumption. Therefore, a collision avoidance guidance and high-precision variable speed coordinated control strategy with a bridge collision avoidance mechanism is extremely urgent to ensure the safety of ship-UAV cooperative bridge crossing. Summary of the Invention
[0004] This invention provides a ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles in bridge areas to overcome the above-mentioned technical problems.
[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A ship-UAV cooperative collision avoidance control method based on geometric velocity barriers in bridge areas includes the following steps: S1. Establish a nonlinear model of ship-UAV cooperative bridge system based on ship-UAV cooperative bridge system; S2. The geometric velocity obstacle method is used to plan the initial collision avoidance reference path for the ship-UAV cooperative bridge system; S3. The initial collision avoidance reference path is smoothed by using DVS-DVA guidance technology to obtain an optimized collision avoidance path; a hyperbolic tangent speed change mechanism is designed so that ships and UAVs can adjust their speed in real time based on the hyperbolic tangent speed change mechanism. S4. Design a multi-port segmented event triggering mechanism for the ship-UAV collaborative bridge system to realize on-demand triggering transmission of multi-port status data during ship-UAV collaborative bridge operations; S5. Design a ship-UAV cooperative bridge controller based on the aforementioned ship-UAV cooperative bridge nonlinear model; implement ship-UAV collision avoidance cooperative speed change navigation control based on the aforementioned optimized collision avoidance path, hyperbolic tangent speed change mechanism, multi-port segmented event triggering mechanism and ship-UAV cooperative bridge controller, so that the ship and UAV converge to the desired path and maintain cooperative stability.
[0006] Furthermore, in S1, the nonlinear model of the ship-UAV cooperative bridge system is a ship four-degree-of-freedom model and a UAV six-degree-of-freedom nonlinear mixed-order Euler-Lagrange mathematical model, expressed as shown in formulas (1) and (2): (1) (2) in, (3) in, These represent the ship's forward displacement, yaw displacement, bow angle, and roll angle in the nautical coordinate system, respectively. These represent the displacements of the UAV along the horizontal, vertical, and yaw axes in the nautical coordinate system, as well as the UAV's pitch, roll, and yaw angles, respectively. These represent the ship's forward speed, drift speed, roll rate, and bow rate in the attached coordinate system, respectively. These represent the forward velocities of the UAV along the horizontal, vertical, and lateral axes in the attached coordinate system, respectively. These represent the angular velocities of the UAV rotating around the horizontal, vertical, and angular axes in the attached coordinate system, respectively. Represents the form of the first derivative; These represent the nonlinear dynamics terms of the ship and the drone, respectively. and These represent the hydrodynamic added mass coefficients for the ship's forward, lateral, and bow degrees of freedom, respectively. This indicates the total mass of the drone, including inertial mass and added mass; This represents the hydrodynamic mass factor of a ship in its roll degree of freedom; It is gravitational acceleration; It is the distance between the rotor axis of the drone and the center of mass of the drone; and This represents the moment of inertia of the drone about its main axis; It represents the amount of external disturbances acting on the ship's degrees of freedom of forward, drift, yaw, and roll; This represents the amount of external disturbance acting on the translational and rotational degrees of freedom of the UAV; and These represent the forward thrust torque and yaw control torque of the ship, respectively. This indicates the total lift generated by the drone; and These represent the roll moment, pitch moment, and yaw moment of the UAV, respectively. , and This represents the elements of the rotation matrix of the drone along the x, y, and z directions.
[0007] Furthermore, in S2, the specific steps for planning the initial collision avoidance reference path for the ship-UAV cooperative bridge system using the geometric velocity obstacle method include: Setting ship target points based on navigational experience Coordinated target points with corresponding drones Set the initial position of the drone. and the initial position of the unmanned vessel The length of the bridge is set as follows: Width is The height is Set bridge safety zone thresholds This creates a bridge safety zone. The length, width, and height of the bridge safety zone are respectively , and ; When the USV and UAV are synchronized and coordinated, the desired heading angle of the ship is set. Desired yaw angle of the UAV And the expected pitch angle of the drone As shown in equation (4): (4) in, and These represent the desired position signals of the drone and the ship in the x and y directions, respectively. ; This represents the desired position signal of the UAV in the z-direction; These represent the displacements of the UAV along the horizontal, vertical, and lateral axes in the nautical coordinate system, respectively. These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. Based on three typical scenarios of UAVs crossing bridges, the number of intersections between the straight line formed by the initial position point and the cooperative target point and the surface of the bridge's safe zone is obtained. As shown in equation (5): (5) in, Represents the bridge safety zone. This represents the boundary surface formed by the union of the six planes of the bridge safety zone; It is a line segment The set of points on the map represents the safe path planned by the drone; The center point is P , radius is The open spherical surface; The edges of the visible bridge safety zone are defined as visible edges, and the mathematical expression for a visible edge is: ; in, Visible edge, The set of edges representing the bridge safety zone and These are the two endpoints of the visible edge corresponding to the drone's starting point; Indicates the distance between the drone's starting point and the visible edge. The two endpoints A unique plane jointly determined; Based on three typical scenarios and combined with visible edges, a set of surfaces tangent to the bridge safety zone is established, as shown in equation (6): (6) in, and These are the two endpoints of the visible edge corresponding to the drone's target point; This is the set of faces obtained by connecting the initial position of the UAV to the endpoints of the nine edges; This is the set of faces obtained by connecting the UAV target point to the endpoints of the nine edges; and These are the sets of surfaces tangent to the bridge safety zone from the initial position point and the target point, respectively. Eand F Let each represent the set of intersection surfaces of the common tangents from the starting point to the target point; Based on the set of faces, we obtain the publicly visible edges, and then obtain the nearest safe meeting point. At the same time, combined with the number of intersection points The initial collision avoidance reference path of the UAV is obtained, as shown in equation (7): (7) in, , and Representing points respectively P Position coordinates in the x, y, and z directions; , and These represent the position coordinates of the UAV target point in the x, y, and z directions, respectively. This indicates the position coordinates of the drone's starting point in the x-direction; and These represent the yaw angle and pitch angle of the UAV at the current moment, respectively. The roll angle of the UAV is obtained based on the anti-decoupling technology and the UAV collision avoidance reference path, as shown in equation (8): (8) in, and These represent the control inputs of the UAV in the x, y, and z directions, respectively. When the USV and UAV coordinate asynchronously, the reference heading angle of the ship is derived based on the relative attitude between the DVS and USV, as shown in equation (9): (9) in, and These represent the position tracking errors of the ship in the x and y directions, respectively. , , These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. and This indicates the desired position signal of the ship in the x and y directions.
[0008] Furthermore, in S3, the specific steps for designing the hyperbolic tangential transmission mechanism include: Based on the initial point Recent security meeting encountered some issues and target point The circular arc path is determined, and the turning rate of the UAV turning along the circular arc path is derived. , represented as: ; in, This indicates the turning radius of the current drone path; This represents the expected speed of the drone traveling along a circular path; Design a hyperbolic tangential speed change mechanism for a ship-UAV cooperative bridge system to address... Configure it as follows: (10) in, For time variables, Peak time, For the maximum speed of the ship-drone collaborative bridge system, The acceleration coefficient of the ship-UAV cooperative bridge system directly reflects the speed response characteristics of the system. , Indicates the ship's expected forward speed; and These represent the maximum speeds in the gear shifting mechanisms of ships and drones, respectively. and This represents the acceleration coefficient of ships and drones.
[0009] Furthermore, in S4, the multi-port segmented event triggering mechanism is designed as shown in equations (12) and (13): (12) in, (13) in, These are design parameters. It is the threshold parameter, where Indicates the dynamic threshold parameter; Indicates the static threshold parameter; It is a speed-related factor; They represent Signals following the multi-port segmentation event triggering rules These represent the position tracking error of the UAV, the position tracking error of the ship, the attitude tracking error of the UAV, and the heading tracking error of the ship, respectively. This represents the difference between the desired signal and the current signal. and This indicates the set error threshold. Indicates the lower limit of the dynamic threshold; and Threshold adjustment parameters designed to address different errors; This indicates the ship's forward speed in the appendage coordinate system. This represents the resultant velocity of the UAV in the attached coordinate system.
[0010] Furthermore, in S5, the specific steps for designing a ship-UAV cooperative bridge controller include: S51. Define the position loop and attitude loop tracking errors of the ship-UAV cooperative bridge system as follows: The corresponding expression is as shown in equation (11): (11) in, Indicates the expected heading angle of a ship or drone; Indicates the desired pitch angle of the drone; Indicates the expected roll angle of the drone; To stabilize the tracking error in equation (11), a virtual control law is designed based on the Backsteeping method, expressed as: (14) in, A virtual control law representing the position tracking error of a ship-UAV cooperative bridge system; The virtual control law representing the ship's heading error. A virtual control law representing the attitude tracking error of a UAV; express The first derivative with respect to time This indicates the desired location signal of the drone; This represents the desired attitude angle signal of the UAV; All are design parameters; It is a design value used to ensure that the ship is always behind the virtual ship; ; S52. Define the dynamic errors of USV and UAV, quantify the deviation between the actual state and the expected state of the ship and UAV, and the specific construction form is shown in Equation (15): (15) in, and These represent the position dynamics errors of the USV and UAV, respectively. and These represent the attitude dynamics errors of the USV and UAV, respectively. Indicates the current attitude angle of the drone; This indicates the ship's current bow turning angular velocity; This indicates the current speed of the drone. The dynamic surface control technique is introduced, and its specific expression is shown in equation (16): (16) in, This represents the first-order filter corresponding to the position and attitude tracking errors in a ship-UAV cooperative bridge system. The time derivative of the virtual control law representing the system's position and attitude tracking error; Indicate design parameters; This represents the initial value of the first-order filter; Represents the initial value of the virtual control law; This represents the error between the output of the first-order filter and the virtual control law. Represents continuous real-valued functions defined in the respective domains of system position and attitude; S53. Based on the dynamic errors of USV and UAV, and combined with the nonlinear model of ship-UAV cooperative bridge (Equation (1), virtual control law (Equation (14)) and dynamic surface control technology, the control law of the ship-UAV cooperative bridge controller is designed as shown in Equation (17): (17) in, , These represent the position and attitude control inputs for the UAV, respectively. All are design parameters; It is the total lift generated by the drone; Gain for drones; For the control inputs of the UAV in the x, y, and z directions; It is a dynamic threshold parameter for the multi-port event triggering mechanism; , and Gaussian activation function for multilayer neural networks; and These represent the virtual control laws for the position loop and attitude loop of the ship after being filtered by a first-order filter, respectively. and These represent the virtual control laws for the position loop and attitude loop of the UAV after being filtered by a first-order filter, respectively. Designed for estimating adaptive parameters , , , The adaptive law is shown in equation (18): (18) in, This represents the estimated values of the adaptive parameters of the ship-UAV cooperative bridge system. All of these represent adaptive design parameters.
[0011] Beneficial Effects: This invention employs the geometric velocity obstacle method to plan an initial collision avoidance reference path for a ship-UAV cooperative bridge system. DVS-DVA guidance technology is used to smooth the initial collision avoidance reference path with circular arcs, resulting in an optimized collision avoidance path. A hyperbolic tangent speed-changing mechanism is designed, enabling the ship and UAV to adjust their speeds in real time based on this mechanism. To meet the high-precision requirements of the cooperative bridge collision avoidance path tracking and control task, a multi-port segmented event triggering mechanism is designed to achieve on-demand transmission of multi-port state data, effectively balancing system resource consumption and tracking accuracy. Based on the nonlinear model of the ship-UAV cooperative bridge, a ship-UAV cooperative bridge controller is designed, enabling the ship and UAV to converge to the desired path and maintain cooperative stability. This effectively solves the dynamic collision avoidance problem in the ship-UAV cooperative bridge task, ensuring the safety and stability of the entire bridge crossing process. Attached Figure Description
[0012] 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.
[0013] Figure 1 This is a flowchart of the ship-UAV cooperative collision avoidance control method in the bridge area based on geometric velocity obstacles in this invention; Figure 2 This is a schematic diagram of the obstacle avoidance guidance principle based on the geometric velocity obstacle method in an embodiment of the present invention; Figure 3 This is a schematic diagram of the collision avoidance path of DVS-DVA in an embodiment of the present invention; Figure 4 This is a trajectory diagram of obstacle avoidance guidance and path tracking control for the ship-UAV cooperative bridge crossing system in an embodiment of the present invention; Figure 5 This is a diagram showing the changes in the control inputs of a ship in an embodiment of the present invention; Figure 6 This is a diagram showing the changes in the control input of the UAV in an embodiment of the present invention; Figure 7 This is a comparison chart of the UAV tracking error results under the two algorithms proposed in this embodiment of the invention; Figure 8 This is a comparison diagram of the triggering results of the yaw angle event of the UAV under the two algorithms proposed in the embodiments of the present invention. Detailed Implementation
[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0015] This embodiment provides a ship-UAV cooperative collision avoidance control method based on geometric velocity barriers in bridge areas, such as... Figure 1 As shown, the specific steps include: S1. Establish a nonlinear model of ship-UAV cooperative bridge system based on ship-UAV cooperative bridge system; In a specific embodiment, in S1, the nonlinear model of the ship-UAV cooperative bridge system is a ship four-degree-of-freedom model and a UAV six-degree-of-freedom nonlinear mixed-order Euler-Lagrange mathematical model, expressed as shown in formulas (1) and (2): (1) (2) in, (3) in, These represent the ship's forward displacement, yaw displacement, bow angle, and roll angle in the nautical coordinate system, respectively. These represent the displacements of the UAV along the horizontal, vertical, and yaw axes in the nautical coordinate system, as well as the UAV's pitch, roll, and yaw angles, respectively. These represent the ship's forward speed, drift speed, roll rate, and bow rate in the attached coordinate system, respectively. These represent the forward velocities of the UAV along the horizontal, vertical, and lateral axes in the attached coordinate system, respectively. These represent the angular velocities of the UAV rotating around the horizontal, vertical, and angular axes in the attached coordinate system, respectively. Represents the form of the first derivative; These represent the nonlinear dynamics terms of the ship and the drone, respectively. and These represent the hydrodynamic added mass coefficients for the ship's forward, lateral, and bow degrees of freedom, respectively. This indicates the total mass of the drone, including inertial mass and added mass; This represents the hydrodynamic mass factor of a ship in its roll degree of freedom; It is gravitational acceleration; It is the distance between the rotor axis of the drone and the center of mass of the drone; and This represents the moment of inertia of the drone about its main axis; It represents the amount of external disturbances acting on the ship's degrees of freedom of forward, drift, yaw, and roll; This represents the amount of external disturbance acting on the translational and rotational degrees of freedom of the UAV; and These represent the forward thrust torque and yaw control torque of the ship, respectively. This indicates the total lift generated by the drone; and These represent the roll moment, pitch moment, and yaw moment of the UAV, respectively. , and This represents the elements of the rotation matrix of the drone along the x, y, and z directions.
[0016] S2. The geometric velocity obstacle method is used to plan the initial collision avoidance reference path for the ship-UAV cooperative bridge system; In a specific embodiment, S2, the specific steps for planning the initial collision avoidance reference path for the ship-UAV cooperative bridge system using the geometric velocity obstacle method include: like Figure 2 As shown, the heading angles of the drones and unmanned vessels during the bridge obstacle avoidance mission planning process are... And the pitch angle of the drone Achieving bridge collision avoidance specifically includes: To enable vessels to quickly leave the bridge area, target points for vessels were set based on navigation experience. Coordinated target points with corresponding drones Set the initial position of the drone. and the initial position of the unmanned vessel The length of the bridge is set as follows: Width is The height is Set bridge safety zone thresholds This creates a bridge safety zone. The length, width, and height of the bridge safety zone are respectively , and ; When the USV and UAV are synchronized and coordinated, the ship's heading angle is set. Yaw angle of the drone And the pitch angle of the drone As shown in equation (4): (4) in, and These represent the desired position signals of the drone and the ship in the x and y directions, respectively. ; This represents the desired position signal of the UAV in the z-direction; These represent the displacements of the UAV along the horizontal, vertical, and lateral axes in the nautical coordinate system, respectively. These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. Based on the analysis, regardless of whether the drone starts from its initial position or the collaborative target point, it will encounter three typical scenarios when crossing a bridge: The first type is Figure 2 As shown in Figure a, three sides of the bridge safety zone can be seen from the oblique side of the bridge, and a maximum of three sides can also be seen in space. The second type is Figure 2 As shown in Figure b, two sides of the bridge safety zone can be seen; The third type is Figure 2 As shown in c, only one side of the bridge safety zone is visible; For the three typical scenarios mentioned above, the number of intersections between the straight line formed by the initial position point and the cooperative target point and the surface of the bridge safety zone is obtained. As shown in equation (5): (5) in, Represents the bridge safety zone. This represents the boundary surface formed by the union of the six planes of the bridge safety zone; It is a line segment The set of points on the map represents the safe path planned by the drone; The center point is P , radius is The open spherical surface; According to formula (5), a collision risk analysis is performed: when there is tangency or no intersection, there is no collision risk, and the UAV maintains its original yaw and pitch angles; when there are two intersections, there is a collision risk, and new yaw and pitch angles need to be planned for the UAV, such as... Figure 2 As shown; when there are three visible surfaces from both the drone's perspective and the target point's perspective, i.e., scenario a, it is the most complex case; when there are three visible surfaces, a maximum of 9 edges of the bridge's safety zone can be seen; The edges of the visible bridge safety zone are defined as visible edges, and the mathematical expression for a visible edge is: ; in, Visible edge, The set of edges representing the bridge safety zone and Let the two endpoints of the visible edge corresponding to the drone's starting point be defined. Then, connect the drone's initial position to the endpoints of the nine edges, forming nine faces. At most six of these faces are tangent to the bridge's safety zone. These six faces are denoted as set. , Indicates the distance between the drone's starting point and the visible edge. The two endpoints The plane is determined by common factors; similarly, the drone also has 6 faces tangent to the bridge safety zone when it starts from the target point. These 6 faces are denoted as set. In scenarios b and c, there are at most four faces tangent to the bridge safety zone. These four faces tangent to the bridge from the initial position and the target point are denoted as sets. and Based on three typical scenarios and combined with visible edges, a set of surfaces tangent to the bridge safety zone is established, as shown in equation (6): (6) in, and These are the two endpoints of the visible edge corresponding to the drone's target point; E and F Let each represent a set of intersection surfaces of common tangents from the starting point to the target point; in scenario a, set _____ With sets There are 3 publicly visible edges , and Connect the initial position and target point of the UAV to the endpoints of three common visible edges, respectively, to form three dihedral angles. , and Corresponding scenarios and ,Will Rotate to This makes in the scene middle, and publicly visible edges The two endpoints are in the same plane, in the scene middle, and publicly visible edges The two endpoints are in the same plane, in the scene middle, and publicly visible edges The two endpoints are in the same plane. The intersection point with the publicly visible edge is the nearest safe meeting point for the corresponding scenario. Connect the points in sequence , and The initial collision avoidance reference path of the UAV is obtained, as shown in equation (7): (7) in, , and Representing points respectively P Position coordinates in the x, y, and z directions; , and These represent the position coordinates of the UAV target point in the x, y, and z directions, respectively. This indicates the position coordinates of the drone's starting point in the x-direction; and These represent the yaw angle and pitch angle of the UAV at the current moment, respectively. The roll angle of the UAV is obtained based on the anti-decoupling technology and the UAV collision avoidance reference path, as shown in equation (8): (8) in, and These represent the control inputs of the UAV in the x, y, and z directions, respectively. like Figure 3 As shown, when the USV and UAV coordinate asynchronously, the reference heading angle of the ship is derived based on the relative attitude between the DVS (Dynamic Virtual Ship) and the USV (Unmanned Ship), as shown in Equation (9): (9) in, and These represent the position tracking errors of the ship in the x and y directions, respectively. , , These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. and Signals indicating the ship's desired position in the x and y directions; In scenario a, there are 3 collision avoidance reference paths. You can choose the shortest path or select the final collision avoidance reference path according to the actual engineering requirements. There are two collision avoidance reference paths in scenario b, while there is only one collision avoidance reference path in scenario c. Specifically, this embodiment constructs a cooperative obstacle avoidance guidance algorithm, namely the geometric velocity obstacle method, suitable for cubic bridge obstacles. First, a safe distance threshold is set to define the safe zone, and the nearest safe meeting point is calculated. Based on the position information of this point, a cooperative heading reference signal can be calculated. Simultaneously, by connecting the starting point, the nearest safe meeting point, and the target point, a nearest safe collision avoidance path is planned. This effectively solves the dynamic collision avoidance problem in cooperative bridge-crossing missions between ships and UAVs. S3. Dynamic Virtual Ship-Dynamic Virtual Aircraft (DVS-DVA) guidance technology is used to smooth the initial collision avoidance reference path with circular arcs, eliminating path bends and generating a continuous and trackable optimized collision avoidance path, providing a smooth guidance reference signal for subsequent coordinated speed change and tracking control; a hyperbolic tangent speed change mechanism is designed so that the ship and UAV can adjust their navigation speed in real time based on the hyperbolic tangent speed change mechanism, ensuring the safety and stability of the coordinated bridge crossing operation; In a specific embodiment, since the collision avoidance path of the UAV planned using the geometric velocity obstacle method is a polygonal line, DVA navigation is required to guide the UAV to fly along a smooth curve. In actual navigation scenarios, unmanned vessels typically adopt a straight-line navigation mode, and their path planning relies on traditional navigation methods. Obstacles such as bridge piers can be avoided in advance during the path planning stage, so straight-line navigation is more in line with engineering practice. If the method proposed in this embodiment is used to achieve dynamic obstacle avoidance of bridge piers by the UAV, it is also feasible, and the implementation process is simpler because the problem dimension is simplified from three dimensions to two dimensions. Regardless of whether the UAV's preset path is a straight line or a curve, the DVS method proposed in this embodiment can provide smooth guidance: when the planned path is a straight line, it naturally has smooth characteristics and can directly achieve stable navigation; when the planned path is a curve, DVS can also ensure smooth tracking of the UAV. Since this embodiment focuses on the UAV needing to navigate along a curved path, the path form of the UAV is not described in detail. In fact, even if the UAV's path is a polygonal line, DVS guidance can still achieve the same cooperative navigation effect as the UAV.
[0017] In a specific embodiment, S3 includes the following steps for designing the hyperbolic tangent speed change mechanism: Due to the initial point Recent security meeting encountered some issues and target point Always lying on the same plane, these three points determine a circular arc path, the radius of which is determined by the points. , and The location determines this; specifically, this arc path ensures that the drone remains above the bridge's safe zone, thereby improving the safety factor in engineering practice. The turning rate for turning along the circular path of the UAV is derived. , represented as: ; in, Indicates the turning radius of the drone's current path; This represents the expected speed of the UAV traveling along a circular path; meanwhile, the ship's path is a straight line. To improve the safety of collaborative bridge-crossing missions, a hyperbolic tangential speed-changing mechanism for a ship-UAV collaborative bridge-crossing system is designed. Configure it as follows: (10) in, For time variables, Peak time, For maximum speed, The acceleration coefficient directly reflects the speed response characteristics of the ship-UAV cooperative bridge system. , Indicates the ship's expected forward speed; and These represent the maximum speeds in the gear shifting mechanisms of ships and drones, respectively. and This represents the acceleration coefficient of ships and drones.
[0018] Specifically, the velocity variation law of the hyperbolic tangent function is adapted to the kinematics and dynamics of UAVs and unmanned vessels. The trend is deceleration followed by acceleration, meaning that the velocity decreases as it approaches the bridge, reaching a very low velocity near point P, closest to the bridge. This design aims to minimize damage even in the event of sudden disturbances, preventing high-speed collisions with the bridge. The coordinated speed adjustment mechanism, based on the coordinated approach and departure of the vessel and UAV from the nearest safe encounter point, effectively ensures the safety of the entire bridge crossing process.
[0019] Specifically, this embodiment introduces DVS-DVA guidance technology to enhance the maneuverability of the USV-UAV cooperative bridge-crossing system, enabling it to adapt to sudden changes in the reference path. Simultaneously, the hyperbolic tangent speed-changing mechanism ensures that the UAV decelerates when approaching the bridge and accelerates when moving away, while maintaining the same initial and target velocity values.
[0020] S4. Design a multi-port segmented event triggering mechanism for the ship-UAV collaborative bridge system. This mechanism enables on-demand transmission of multi-port status data during ship-UAV collaborative bridge operations, reducing communication bandwidth usage and improving system communication efficiency and operational reliability while ensuring control accuracy. In a specific embodiment, this embodiment addresses the communication bandwidth limitation problem of the ship-UAV collaborative bridge system by designing a multi-port segmented event triggering mechanism. This mechanism triggers multi-source state data transmission through dynamic threshold segmentation. In S4, the designed multi-port segmented event triggering mechanism is shown in equations (12) and (13): (12) in, (13) in, These are design parameters. It is the threshold parameter, where Indicates the dynamic threshold parameter; Indicates the static threshold parameter; It is a speed-related factor; They represent Signals following the multi-port segmentation event triggering rules These represent the position tracking error of the UAV, the position tracking error of the ship, the attitude tracking error of the UAV, and the heading tracking error of the ship, respectively. This represents the difference between the desired signal and the current signal. and This indicates the set error threshold. Indicates the lower limit of the dynamic threshold; and Threshold adjustment parameters designed to address different errors; This indicates the ship's forward speed in the appendage coordinate system. This represents the resultant velocity of the UAV in the attached coordinate system.
[0021] Specifically, the multi-port segmented event triggering mechanism is a dual-factor triggering mechanism that takes into account both error status and speed changes, and can dynamically adjust the triggering threshold according to the real-time error and speed changes at different stages of the task.
[0022] S5. Design a ship-UAV cooperative bridge controller based on the aforementioned ship-UAV cooperative bridge nonlinear model; implement ship-UAV collision avoidance cooperative speed change navigation control based on the aforementioned optimized collision avoidance path, hyperbolic tangent speed change mechanism, multi-port segmented event triggering mechanism and ship-UAV cooperative bridge controller, so that the ship and UAV converge to the desired path and maintain cooperative stability.
[0023] In a specific embodiment, S5, the specific steps for designing the ship-UAV cooperative bridge controller include: S51. Define the position loop and attitude loop tracking errors of the ship-UAV cooperative bridge system as follows: The corresponding expression is as shown in equation (11): (11) in, Indicates the expected heading angle of a ship or drone; Indicates the desired pitch angle of the drone; Indicates the expected roll angle of the drone; To stabilize the tracking error in equation (11), a virtual control law is designed based on the Backsteeping method, expressed as: (14) in, A virtual control law representing the position tracking error of a ship-UAV cooperative bridge system; The virtual control law representing the ship's heading error. A virtual control law representing the attitude tracking error of a UAV; express The first derivative with respect to time This indicates the desired location signal of the drone; This represents the desired attitude angle signal of the UAV; All are design parameters; It is a design value used to ensure that the ship is always behind the virtual ship; ; S52. Define the dynamic errors of USV and UAV, quantify the deviation between the actual state and the expected state of the ship and UAV, and the specific construction form is shown in Equation (15): (15) in, and These represent the position dynamics errors of the USV and UAV, respectively. and These represent the attitude dynamics errors of the USV and UAV, respectively. Indicates the current attitude angle of the drone; This indicates the ship's current bow turning angular velocity; This indicates the current speed of the drone. This embodiment introduces Dynamic Surface Control (DSC) technology, specifically expressed as shown in Equation (16): (16) in, This represents the first-order filter corresponding to the position and attitude tracking errors in a ship-UAV cooperative bridge system. The time derivative of the virtual control law representing the system's position and attitude tracking error; Indicate design parameters; This represents the initial value of the first-order filter; Represents the initial value of the virtual control law; This represents the error between the output of the first-order filter and the virtual control law. Represents continuous real-valued functions defined in the respective domains of system position and attitude; S53. In order to enable the ship-UAV cooperative bridge system to converge to the desired path with high accuracy, this embodiment designs the control law of the ship-UAV cooperative bridge controller based on the dynamic error of the USV and UAV, and in combination with the nonlinear model of the ship-UAV cooperative bridge, i.e., Equation (1), the virtual control law, i.e., Equation (14), and dynamic surface control technology, as shown in Equation (17): (17) in, , These represent the position and attitude control inputs for the UAV, respectively. All are design parameters; It is the total lift generated by the drone; Gain for drones; For the control inputs of the UAV in the x, y, and z directions; It is a dynamic threshold parameter for the multi-port event triggering mechanism; , and Gaussian activation function for multilayer neural networks; and These represent the virtual control laws for the position loop and attitude loop of the ship after being filtered by a first-order filter, respectively. and These represent the virtual control laws for the position loop and attitude loop of the UAV after being filtered by a first-order filter, respectively. Specifically, in this embodiment, the output of the first-order filter designed by equation (16) is substituted into the control law of equation (17), and the direct differentiation of the virtual control law is replaced by the filtered derivative, thus avoiding differential explosion.
[0024] Designed for estimating adaptive parameters , , , The adaptive law is shown in equation (18): (18) in, This represents the estimated values of the adaptive parameters of the ship-UAV cooperative bridge system. All of these represent adaptive design parameters.
[0025] In this embodiment, based on the controller design described above, and to facilitate stability analysis of the control system, the candidate Lyapunov functions for the position loop and attitude loop are selected as follows: As shown in equation (19): (19) in, Lyapunov functions representing the position loops of the USV and UAV; Describe the Lyapunov function of the attitude loop of the USV and UAV, guaranteeing and The stability of the ship-UAV collaborative bridge system can be guaranteed by the stability of the system. These represent the errors between the actual and estimated values of the adaptive parameters, respectively. , This represents the update law for the adaptive attitude parameters of ships and UAVs, with a design value that is positive. These represent the first-order filter errors of the attitude loop for ships and UAVs, respectively. This indicates the drone's attitude loop tracking error; Using Young's inequality and combining it with multi-layer neural networks (MNNs) technology, and combining the control law (equation (17)) and the adaptive law (equation (18), the Lyapunov function in equation (19) is differentiated, and the results are shown in equations (20) and (21): (20) (twenty one) and , ; , ; in, These are the design parameters after scaling Young's inequality. for The maximum value, for The maximum value; , , These represent the parameters of the first-order filter; The update law representing the adaptive parameters of the position and attitude of ships and UAVs; These represent the adaptive design parameters; This represents the error between the actual and estimated values of adaptive parameters for ships and drones. a This represents a non-negative constant residual term.
[0026] Equation (20) proves the stability of the position loop of the USV and UAV, and Equation (21) proves the stability of the attitude loop of the USV and UAV. Since the stability of both the position loop and the attitude loop is satisfied, the stability of the ship-UAV cooperative bridge system is proved, as shown in Equation (22): (twenty two) in, , . Specifically, this embodiment approximates the nonlinear terms of the system using multi-layer neural network technology, that is... All passed Approaching, This is the weight matrix. This is a Gaussian activation function for a multilayer neural network. While ensuring improved nonlinear approximation capability, a simple control law form can also be derived, and a corresponding adaptive law can be designed, thus significantly improving the applicability of the algorithm in maritime engineering practice.
[0027] To verify the effectiveness of the method proposed in this embodiment, a simulation numerical experiment was conducted under simulated marine environmental disturbances: Generate a bridge obstacle measuring 100m long, 20m wide, and 5m high within a 500m x 500m sea area. The bridge piers are 30m high, 5m in radius, and spaced 40m apart. The bridge safety zone threshold is set to 3m. A 38m long vessel and a 0.486kg drone are selected as the controlled objects. Set the drone's target point. Ship target point The initial state variables for the ship-UAV configuration are set as follows: .
[0028] Figure 4 The trajectory diagram shows the collision avoidance guidance and path tracking control performance of the proposed ship-UAV cooperative bridge system. Figure 4 As can be seen, the collision avoidance method proposed in this embodiment can plan the shortest safe collision avoidance path. The virtual ship can make the polygonal collision avoidance path continuous, realizing refined guidance of the obstacle avoidance path. Thus, the speed obstacle avoidance algorithm for the safe zone of a cuboid bridge has been effectively verified. In addition, the control accuracy of the ship-UAV cooperative bridge crossing system is relatively precise, and the error is within the permissible range of marine engineering. Figure 5 and Figure 6 The control input of the ship-UAV method proposed in this embodiment is shown. As can be seen from the figure, the forward and turning torques of the ship meet the engineering requirements, and the control lift of the UAV is also adapted to its own weight. The entire bridge crossing process is completed with relatively small control force and control torque. Figure 7This demonstrates that the control algorithm proposed in this embodiment has smaller errors and faster convergence rate compared to traditional multi-port event-triggered control algorithms. Figure 8 The paper compares the trigger interval effects of two different event triggering mechanisms. As shown in the figure, the trigger interval of the algorithm proposed in this embodiment is significantly improved compared to the comparison algorithm. This reflects that, under high control precision, the multi-port segmented event triggering mechanism proposed in this embodiment can save more resources, while simultaneously considering the impact of error and speed. Figure 7 Analysis shows that the trigger interval during acceleration and deceleration is shorter than that during error stabilization. In summary, based on the analysis of simulation results and a comparison of the two algorithms, the method proposed in this embodiment has the following advantages: (1) In the process of obstacle avoidance guidance, a geometric velocity obstacle method for ship-UAV cooperative bridge crossing system was designed, and the continuous curve of the polyline path was combined with DVS-DVA. By setting the bridge safety zone threshold and constructing the nearest safe meeting point, and combined with the designed hyperbolic tangent speed change mechanism, a new solution was provided for the collision avoidance and high-precision path tracking problem of ship-UAV cooperative bridge crossing system in bridge crossing scenario. (2) During the collaborative bridge crossing process, considering the high-precision control requirements of the ship-UAV collaborative bridge crossing system, an adaptive multi-port segmented event triggering mechanism was designed. By combining error and speed dual triggering mechanisms, the resource saving efficiency under variable speed conditions is improved, while the control accuracy under large error and large speed change conditions is also improved. Furthermore, by reducing unnecessary triggering, the resources of the ship-UAV collaborative bridge crossing system are saved. In addition, the introduction of MNNs technology to solve the nonlinear term problem of the ship-UAV collaborative bridge crossing system can effectively approximate the nonlinear term of the ship-UAV collaborative bridge crossing system and improve the overall control accuracy of the collaborative bridge crossing system.
[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles in bridge areas, characterized in that, The specific steps include: S1. Establish a nonlinear model of ship-UAV cooperative bridge system based on ship-UAV cooperative bridge system; S2. The geometric velocity obstacle method is used to plan the initial collision avoidance reference path for the ship-UAV cooperative bridge system; S3. The initial collision avoidance reference path is smoothed by using DVS-DVA guidance technology to obtain an optimized collision avoidance path; a hyperbolic tangent speed change mechanism is designed so that ships and UAVs can adjust their speed in real time based on the hyperbolic tangent speed change mechanism. S4. Design a multi-port segmented event triggering mechanism for the ship-UAV collaborative bridge system to realize on-demand triggering transmission of multi-port status data during ship-UAV collaborative bridge operations; S5. Design a ship-UAV cooperative bridge controller based on the aforementioned ship-UAV cooperative bridge nonlinear model; implement ship-UAV collision avoidance cooperative speed change navigation control based on the aforementioned optimized collision avoidance path, hyperbolic tangent speed change mechanism, multi-port segmented event triggering mechanism and ship-UAV cooperative bridge controller, so that the ship and UAV converge to the desired path and maintain cooperative stability.
2. The ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles according to claim 1, characterized in that, In S1, the nonlinear model of the ship-UAV cooperative bridge system is a ship four-degree-of-freedom model and a UAV six-degree-of-freedom nonlinear mixed-order Euler-Lagrange mathematical model, expressed as shown in formulas (1) and (2): (1) (2) in, (3) in, These represent the ship's forward displacement, yaw displacement, bow angle, and roll angle in the nautical coordinate system, respectively. These represent the displacements of the UAV along the horizontal, vertical, and yaw axes in the nautical coordinate system, as well as the UAV's pitch, roll, and yaw angles, respectively. These represent the ship's forward speed, drift speed, roll rate, and bow rate in the attached coordinate system, respectively. These represent the forward velocities of the UAV along the horizontal, vertical, and lateral axes in the attached coordinate system, respectively. These represent the angular velocities of the UAV rotating around the horizontal, vertical, and angular axes in the attached coordinate system, respectively. Represents the form of the first derivative; These represent the nonlinear dynamics terms of the ship and the drone, respectively. and These represent the hydrodynamic added mass coefficients for the ship's forward, lateral, and bow degrees of freedom, respectively. This indicates the total mass of the drone, including inertial mass and added mass; This represents the hydrodynamic mass factor of a ship in its roll degree of freedom; It is gravitational acceleration; It is the distance between the rotor axis of the drone and the center of mass of the drone; and This represents the moment of inertia of the drone about its main axis; It represents the amount of external disturbances acting on the ship's degrees of freedom of forward, drift, yaw, and roll; This represents the amount of external disturbance acting on the translational and rotational degrees of freedom of the UAV; and These represent the forward thrust torque and yaw control torque of the ship, respectively. This indicates the total lift generated by the drone; and These represent the roll moment, pitch moment, and yaw moment of the UAV, respectively. , and This represents the elements of the rotation matrix of the drone along the x, y, and z directions.
3. The ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles according to claim 2, characterized in that, In S2, the specific steps for planning the initial collision avoidance reference path for the ship-UAV cooperative bridge system using the geometric velocity obstacle method include: Setting ship target points based on navigation experience Coordinated target points with corresponding drones Set the initial position of the drone. and the initial position of the unmanned vessel The length of the bridge is set as follows: Width is The height is Set bridge safety zone threshold. This creates a bridge safety zone. The length, width, and height of the bridge safety zone are respectively , and ; When the USV and UAV are synchronized and coordinated, the desired heading angle of the ship is set. Desired yaw angle of the UAV And the expected pitch angle of the drone As shown in equation (4): (4) in, and These represent the desired position signals of the drone and the ship in the x and y directions, respectively. ; This represents the desired position signal of the UAV in the z-direction; These represent the displacements of the UAV along the horizontal, vertical, and lateral axes in the nautical coordinate system, respectively. These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. Based on three typical scenarios of UAVs crossing bridges, the number of intersections between the straight line formed by the initial position point and the cooperative target point and the surface of the bridge's safe zone is obtained. As shown in equation (5): (5) in, Represents the bridge safety zone. This represents the boundary surface formed by the union of the six planes of the bridge safety zone; It is a line segment The set of points on the map represents the safe path planned by the drone; The center point is P , radius is The open spherical surface; The edges of the visible bridge safety zone are defined as visible edges, and the mathematical expression for a visible edge is: ； in, Visible edge, The set of edges representing the bridge safety zone and These are the two endpoints of the visible edge corresponding to the drone's starting point; Indicates the distance between the drone's starting point and the visible edge. The two endpoints A unique plane jointly determined; Based on three typical scenarios and combined with visible edges, a set of surfaces tangent to the bridge safety zone is established, as shown in equation (6): (6) in, and These are the two endpoints of the visible edge corresponding to the drone's target point; This is the set of faces obtained by connecting the initial position of the UAV to the endpoints of the nine edges; This is the set of faces obtained by connecting the UAV target point to the endpoints of the nine edges; and These are the sets of surfaces tangent to the bridge safety zone from the initial position point and the target point, respectively. E and F Let each represent the set of intersection surfaces of the common tangents from the starting point to the target point; Based on the set of faces, we obtain the publicly visible edges, and then obtain the nearest safe meeting point. At the same time, combined with the number of intersection points The initial collision avoidance reference path of the UAV is obtained, as shown in equation (7): (7) in, , and Representing points respectively P Position coordinates in the x, y, and z directions; , and These represent the position coordinates of the UAV target point in the x, y, and z directions, respectively. This indicates the position coordinates of the drone's starting point in the x-direction; and These represent the yaw angle and pitch angle of the UAV at the current moment, respectively. The roll angle of the UAV is obtained based on the anti-decoupling technology and the UAV collision avoidance reference path, as shown in equation (8): (8) in, and These represent the control inputs of the UAV in the x, y, and z directions, respectively. When the USV and UAV coordinate asynchronously, the reference heading angle of the ship is derived based on the relative attitude between the DVS and USV, as shown in equation (9): (9) in, and These represent the position tracking errors of the ship in the x and y directions, respectively. , , These represent the forward displacement and lateral drift displacement of the ship in the nautical coordinate system, respectively. and This indicates the desired position signal of the ship in the x and y directions.
4. The ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles according to claim 3, characterized in that, In S3, the specific steps for designing the hyperbolic tangent shifting mechanism include: Based on the initial point Recent security meeting encountered some issues and target point The circular arc path is determined, and the turning rate of the UAV turning along the circular arc path is derived. , represented as: ； in, This indicates the turning radius of the current drone path; This represents the expected speed of the drone traveling along a circular path; Design a hyperbolic tangential speed change mechanism for a ship-UAV cooperative bridge system to address... Configure it as follows: (10) in, For time variables, Peak time, For the maximum speed of the ship-drone collaborative bridge system, The acceleration coefficient of the ship-UAV cooperative bridge system directly reflects the speed response characteristics of the system. , Indicates the ship's expected forward speed; and These represent the maximum speeds in the gear shifting mechanisms of ships and drones, respectively. and This represents the acceleration coefficient of ships and drones.
5. The ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles according to claim 4, characterized in that, In S4, the multi-port segmented event triggering mechanism is designed as shown in equations (12) and (13): (12) in, (13) in, These are design parameters. It is the threshold parameter, where Indicates the dynamic threshold parameter; Indicates the static threshold parameter; It is a speed-related factor; They represent Signals following the multi-port segmentation event triggering rules These represent the position tracking error of the UAV, the position tracking error of the ship, the attitude tracking error of the UAV, and the heading tracking error of the ship, respectively. This represents the difference between the desired signal and the current signal. and This indicates the set error threshold. Indicates the lower limit of the dynamic threshold; and Threshold adjustment parameters designed to address different errors; This indicates the ship's forward speed in the appendage coordinate system. This represents the resultant velocity of the UAV in the attached coordinate system.
6. The ship-UAV cooperative collision avoidance control method based on geometric velocity obstacles according to claim 5, characterized in that, In S5, the specific steps for designing a ship-UAV cooperative bridge controller include: S51. Define the position loop and attitude loop tracking errors of the ship-UAV cooperative bridge system as follows: The corresponding expression is as shown in equation (11): (11) in, Indicates the expected heading angle of a ship or drone; Indicates the desired pitch angle of the drone; Indicates the expected roll angle of the drone; To stabilize the tracking error in equation (11), a virtual control law is designed based on the Backsteeping method, expressed as: (14) in, A virtual control law representing the position tracking error of a ship-UAV cooperative bridge system; The virtual control law representing the ship's heading error. A virtual control law representing the attitude tracking error of a UAV; express The first derivative with respect to time This indicates the desired location signal of the drone; This represents the desired attitude angle signal of the UAV; All of these are design parameters; It is a design value used to ensure that the ship is always behind the virtual ship; ; S52. Define the dynamic errors of USV and UAV, quantify the deviation between the actual state and the expected state of the ship and UAV, and the specific construction form is shown in Equation (15): (15) in, and These represent the position dynamics errors of the USV and UAV, respectively. and These represent the attitude dynamics errors of the USV and UAV, respectively. Indicates the current attitude angle of the drone; This indicates the ship's current bow turning angular velocity; This indicates the current speed of the drone. The dynamic surface control technique is introduced, and its specific expression is shown in equation (16): (16) in, This represents the first-order filter corresponding to the position and attitude tracking errors in a ship-UAV cooperative bridge system. The time derivative of the virtual control law representing the system's position and attitude tracking error; Indicate design parameters; This represents the initial value of the first-order filter; Represents the initial value of the virtual control law; This represents the error between the output of the first-order filter and the virtual control law. Represents continuous real-valued functions defined in the respective domains of system position and attitude; S53. Based on the dynamic errors of USV and UAV, and combined with the ship-UAV cooperative bridge nonlinear model (Equation (1), the virtual control law (Equation (14)) and dynamic surface control technology, the control law of the ship-UAV cooperative bridge controller is designed as shown in Equation (17): (17) in, , These represent the position and attitude control inputs for the UAV, respectively. All of these are design parameters; It is the total lift generated by the drone; Gain for drones; For the control inputs of the UAV in the x, y, and z directions; It is a dynamic threshold parameter for the multi-port event triggering mechanism; , and Gaussian activation function for multilayer neural networks; and These represent the virtual control laws for the position loop and attitude loop of the ship after being filtered by a first-order filter, respectively. and These represent the virtual control laws for the position loop and attitude loop of the UAV after being filtered by a first-order filter, respectively. Designed for estimating adaptive parameters , , , The adaptive law is shown in equation (18): (18) in, This represents the estimated values of the adaptive parameters of the ship-UAV cooperative bridge system. All of these represent adaptive design parameters.