A method for building a digital capability model of an unmanned surface vehicle

Abstract

The application discloses a kind of construction methods of unmanned ship digital capability model, power model is used to build the power performance of different types of unmanned ship, increase the authenticity of digital capability model, and combine with load capacity model and environmental data interaction, realize the purpose of simulation verification to the navigation strategy algorithm of different boat type unmanned ship, improve the performance simulation capability of unmanned ship.The construction method of unmanned ship digital capability model provided by the application can realistically reproduce the physical form, attribute, behavior and rule of unmanned ship entity, the model is not only consistent with entity in geometric structure, but also can simulate the space-time state, behavior, function of entity, and then realize the model of boat platform and platform load, constitute the whole ship digital capability model, verify and test the effectiveness of unmanned ship navigation strategy algorithm.

Description

Technical Field

[0001] This invention belongs to the field of unmanned surface vessel (USV) technology, and specifically relates to a method for building a digital capability model of an USV. Background Technology

[0002] Unmanned surface vessels (USVs) are intelligent unmanned platforms capable of autonomous navigation in marine environments, performing tasks such as security patrols, marine mapping, and marine environmental monitoring. The advantage of USVs is their ability to perform repetitive and high-risk tasks for extended periods in harsh marine conditions, significantly improving the efficiency and safety of maritime operations. Different types of USVs possess varying power performance, payload capacity, and detection capabilities. Therefore, simulating and validating the navigation strategy algorithms for different USV types requires a simulation solution that can customize capabilities for each type. By modifying the power model and payload model within the capability model for different USV types, the digital capabilities of each USV can be constructed, which has significant practical implications for the simulation of USV navigation strategy algorithms. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for constructing a digital capability model of an unmanned surface vessel (USV), enabling highly realistic simulation of the USV's dynamic performance.

[0004] The objective of this invention is achieved through the following technical solution: a method for constructing a digital capability model for an unmanned surface vessel, specifically including the following steps:

[0005] Step 1: Establish an unmanned surface vessel (USV) dynamic model;

[0006] Step 2: Establish a payload equipment model, enable the payload equipment model to interact with environmental data, and complete the closed-loop verification of the unmanned surface vessel's digital capability model.

[0007] Furthermore, in step 1, the steps for constructing the unmanned surface vessel's dynamic model are as follows:

[0008] Step 1.1: Establish a mathematical model of the relationship between thrust, rudder angle and mass of the unmanned surface vessel (USV). Solve for the theoretical motion trend of the USV based on the known thrust and rudder angle. Calculate the speed that the USV can reach based on the thrust, rudder angle, mass and the resistance corresponding to different speeds.

[0009] Step 1.2: Establish the correspondence between the actual engine external characteristic curve and the actual speed of the unmanned surface vessel (USV), and solve for the drag coefficient of the USV at different speeds;

[0010] Step 1.3: Establish the motion model of the unmanned surface vessel based on Runge-Kutta's principle.

[0011] Compared with the prior art, the present invention has the following advantages:

[0012] This invention provides a method for constructing a digital capability model for unmanned surface vessels (USVs). It utilizes a dynamic model to construct the dynamic performance of different types of USVs, increasing the realism of the digital capability model. By combining a payload capability model with environmental data interaction, it aims to simulate and verify the navigation strategy algorithms of different USV types, thereby improving the performance simulation capabilities of USVs. The method for constructing a digital capability model for USVs provided by this invention can realistically reproduce the physical form, attributes, behavior, and rules of the USV entity. This model not only maintains geometric consistency with the entity but also simulates the entity's spatiotemporal state, behavior, and functions. This allows for the establishment of models of the vessel platform and its payloads, forming a complete digital capability model of the USV, and verifying and testing the effectiveness of the USV's navigation strategy algorithms. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for building a digital capability model of an unmanned surface vessel in this invention;

[0014] Figure 2 This is a schematic diagram of the forces acting on the hull of a single-powered ship in an embodiment of the present invention;

[0015] Figure 3 This is a schematic diagram of the forces acting on the hull of the dual-powered vessel in an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram of the forces acting on the hull of the multi-powered ship in an embodiment of the present invention;

[0017] Figure 5 This is the speed-drag fitting curve of the unmanned surface vessel in an embodiment of the present invention;

[0018] Figure 6 This is a schematic diagram of the execution flow of the navigation and positioning device model in an embodiment of the present invention;

[0019] Figure 7 This is a schematic diagram of the execution flow of the navigation radar device model in an embodiment of the present invention;

[0020] Figure 8 This is a schematic diagram of the execution flow of the optoelectronic device model in an embodiment of the present invention;

[0021] Figure 9 This is a schematic diagram of target tracking in an embodiment of the present invention. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0023] like Figure 1 As shown, the technical solution of the present invention provides a method for building a digital capability model of an unmanned surface vessel, specifically including the following steps:

[0024] Step 1: Establish an unmanned surface vessel (USV) dynamic model;

[0025] Step 1.1: Establish a mathematical model of the relationship between thrust, rudder angle and mass of the unmanned surface vessel (USV). Solve for the theoretical motion trend of the USV based on the known thrust and rudder angle. Calculate the speed that the USV can reach based on the thrust, rudder angle, mass and the resistance corresponding to different speeds.

[0026] Step 1.2: Establish the correspondence between the actual engine external characteristic curve and the actual speed of the unmanned surface vessel (USV), and solve for the drag coefficient of the USV at different speeds;

[0027] Step 1.3: Establish the motion model of the unmanned surface vessel based on Runge-Kutta's principle.

[0028] Step 2: Establish a payload equipment model, enable the payload equipment model to interact with environmental data, and complete the closed-loop verification of the unmanned surface vessel's digital capability model.

[0029] In one embodiment of the present invention, the method for establishing the mathematical model of the relationship between the thrust of a single-powered hull, the rudder angle, and the mass of the unmanned surface vessel in step 1.1 is as follows:

[0030] like Figure 2 As shown, a planar coordinate system is established with the center of the bow direction as the origin. The unmanned surface vessel (USV) is considered as a cuboid of a uniform medium. Ignoring the drag on the USV, and using the principles of rigid body mechanics, we can obtain:

[0031]

[0032] F a cosaL a +F a sinαL b =Iw (2)

[0033] F a cosα=mA (3)

[0034] Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ); m is the mass of the boat; F a For thrust; L a L is the lateral lever arm; b α is the longitudinal lever arm; α is the rudder angle; A is the longitudinal acceleration.

[0035] Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies:

[0036] F a cosαL a +F a sinαL b =f ω L b (4)

[0037] F a cosα=f v (5)

[0038] Among them, f ω The lateral drag of the unmanned surface vessel at an angular velocity ω; f v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

[0039] like Figure 3 As shown, in one embodiment of the present invention, the method for establishing the mathematical model of the relationship between the thrust of the dual-powered hull, the rudder angle, and the mass of the unmanned surface vessel in step 1.1 is as follows:

[0040] Establish a planar coordinate system with the center of the bow direction as the origin. Treating the unmanned surface vessel (USV) as a cuboid of a uniform medium, and neglecting drag, we can obtain the following using the principles of rigid body mechanics:

[0041]

[0042] F a cosaL a +F b cosβL a +F a sinαL b +F b sinβL b =Iw (7)

[0043] F a cosα+F b cosβ=mA (8)

[0044] Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ); m is the mass of the boat; F a For leftward thrust; F b For rightward thrust; L a L is the lateral lever arm; b α is the longitudinal lever arm; β is the left rudder angle; β is the right rudder angle; A is the longitudinal acceleration.

[0045] Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies:

[0046] F a cosαL a +F b cosβL a +F a sinαL b +F b sinβL b =f ω L b (9)

[0047] F a cosα+F b cosβ=f v (10)

[0048] Among them, f ω The lateral drag of the unmanned surface vessel at an angular velocity ω; f v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

[0049] like Figure 4 As shown, in one embodiment of the present invention, the method for establishing the mathematical model of the relationship between the thrust of the multi-powered hull, the rudder angle, and the mass of the unmanned surface vessel in step 1.1 is as follows:

[0050] Establish a planar coordinate system with the center of the bow direction as the origin. Treat the unmanned surface vessel (USV) as a cuboid of a uniform medium, neglecting the drag on the USV, and using the principles of rigid body mechanics, we can obtain:

[0051]

[0052] F a cosαL a +F b cosβL a +F c cosγL a +F a sinαL b +F b sinβL b +F c sinγL b =Iw (12)

[0053] F a cosα+F b cosβ+F c cosγ=mA (13)

[0054] Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ); m is the mass of the boat; Fa For leftward thrust; F b The rightward thrust; F c For lateral thrust; L a L is the lateral lever arm; b α is the longitudinal lever arm; β is the left rudder angle; γ is the right rudder angle; γ is the lateral thrust angle; and A is the longitudinal acceleration.

[0055] Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies:

[0056] F a cosαL a +F b cosβL a +F c cosγL a +F a sinαL b +F b sinβL b +F c sinγL b =f ω L b (14)

[0057] F a cosα+F b cosβ+F c cosγ=f v (15)

[0058] Among them, f ω The lateral drag of the unmanned surface vessel at an angular velocity ω; f v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

[0059] In one embodiment of the present invention, in step 1.2, the method for establishing the correspondence between the actual engine external characteristic curve and the actual speed of the unmanned surface vessel (USV) and solving the drag coefficient of the USV at different speeds is as follows:

[0060] According to the engine's external characteristic curve, the engine output power corresponding to different engine speeds can be determined. When the ship reaches a constant speed at a certain engine speed, we have:

[0061] P = v * f V (16)

[0062] Where P is the engine's output power; v is the unmanned surface vessel's speed; f V The resistance experienced by the ship.

[0063] By using data on speed and engine power obtained from actual boat tests, we can calculate the speed set {v1, v2…vi} and the resistance set {f1, f2…fi}, where speed vi corresponds to fi. The resistance corresponding to any speed between 0 and the maximum speed of the unmanned surface vessel can be obtained by using the difference method.

[0064] The drag value at any speed can be obtained by fitting a curve. The speed-drag fitting curve of an unmanned surface vessel is shown in the figure below. Figure 5 As shown.

[0065] For the lateral resistance, since the lateral velocities intersect and the longitudinal velocities are smaller, the resistance is considered as viscous assistance. Using Newton's law of internal friction and simplifying the velocity gradient linearly, we can obtain:

[0066] f ω =μKω (17)

[0067] Where μ is the fluid dynamic viscosity; K is a coefficient that needs to be corrected based on measured data; and ω is the angular velocity.

[0068] In one embodiment of the present invention, in step 1.3, the specific method for establishing the motion model of the unmanned surface vessel based on the Runge-Kutta principle is as follows:

[0069] According to Runge-Kutta's principle, the angular velocity ω at time t is the integral of the heading angular acceleration α over time t, and the longitudinal velocity v is the integral of the acceleration a over time t, that is:

[0070]

[0071]

[0072] In summary, a motion model for the unmanned surface vessel can be established.

[0073] In one embodiment of the present invention, the payload device model established in step 2 includes, but is not limited to, a navigation and positioning device model, a navigation radar device model, and an optoelectronic device model.

[0074] The performance of the payload devices largely determines the capabilities of the unmanned surface vessel (USV). Therefore, the completeness of the payload device model directly affects the application scenarios of the USV capability model. In this embodiment, the USV device message communication format is a publish-subscribe model. Each payload device has a corresponding payload service to communicate with, including receiving payload control commands and providing payload status information. The various payload services interact with each other through an information integration service.

[0075] In this embodiment, a navigation and positioning device model is first established. The data output by the navigation and positioning device model mainly includes the longitude, latitude, heading angle, and absolute speed of the unmanned surface vessel (USV). Based on the longitude and latitude coordinates of the USV at time t0 and the speed and heading angular velocity output by the USV's dynamic model, the latitude coordinates and heading angle of the USV at time t0+t are calculated. The navigation and positioning device model, combined with the X and Y axis accelerations output by the dynamic model, forms a navigation and positioning device message, which is then published to the information integration service. The message output frequency of this model is consistent with that of the actual device. The execution flow of the navigation and positioning device model is as follows: Figure 6 As shown.

[0076] Secondly, a navigation radar equipment model is established. The data output by the navigation radar equipment model mainly includes the target's azimuth, range, heading, and speed. Image processing is performed based on the environmental echo data to identify and obtain the target's azimuth and range. The identified targets are tracked, and their speed and heading are calculated. Tracked targets are newly created and assigned batch numbers, ultimately forming target result data, which is then published to the information integration service. The execution flow of the navigation radar equipment model is as follows: Figure 7 As shown.

[0077] Then, an optoelectronic device model is established. The data output by the optoelectronic device model mainly includes the target's azimuth, distance, target type, and recognition result. This device model receives video image data and performs target recognition on the images, obtaining the target type and recognition result. After target recognition, the model tracks the target. Once tracking is stable, servo commands are sent to adjust the target's position in the image, and laser ranging commands are sent to measure the distance from the target to the vessel. Combined with information such as the current angle of the optoelectronic device's servo mechanism, an optoelectronic data feedback message is generated and published to the information integration service. The execution flow of the optoelectronic device model is as follows: Figure 8 As shown.

[0078] Finally, models of other payloads are established based on actual needs, forming different capabilities. The input data for each payload model can be either simulation data or actual data, transmitted via network communication. Upon receiving the corresponding input data, the payload model processes, transforms, and calculates it according to the currently set parameters, and publishes the processed data to the information integration service using the same interface protocol as the actual equipment. After receiving the data from the payload service, the unmanned surface vessel (USV) navigation strategy algorithm performs corresponding algorithm calculations and sends control commands back to the corresponding payload service, completing the closed-loop simulation verification of the USV navigation strategy algorithm.

[0079] In this embodiment, the target tracking process is as follows: Figure 9 As shown.

[0080] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing a digital capability model of an unmanned surface vessel, characterized in that: The method specifically includes the following steps: Step 1: Establish an unmanned surface vessel (USV) dynamic model; Step 2: Establish a payload equipment model, enable the payload equipment model to interact with environmental data, and complete the closed-loop verification of the unmanned surface vessel's digital capability model; In step 1, the steps for constructing the unmanned surface vessel (USV) dynamic model are as follows: Step 1.1: Establish a mathematical model of the relationship between thrust, rudder angle and mass of the unmanned surface vessel (USV). Solve for the theoretical motion trend of the USV based on the known thrust and rudder angle. Calculate the speed that the USV can reach based on the thrust, rudder angle, mass and the resistance corresponding to different speeds. Step 1.2: Establish the correspondence between the actual engine external characteristic curve and the actual speed of the unmanned surface vessel (USV), and solve for the drag coefficient of the USV at different speeds; Step 1.3: Establish the motion model of the unmanned surface vessel based on Runge-Kutta's principle; In step 1.1, the mathematical model for the relationship between the thrust of the multi-powered hull, the rudder angle, and the mass of the unmanned surface vessel is established as follows: Establish a planar coordinate system with the center of the bow direction as the origin. Treat the unmanned surface vessel (USV) as a cuboid of a uniform medium, neglecting the drag on the USV, and using the principles of rigid body mechanics, we can obtain: F a cosαL a +F b cosβL a +F c cosγL a +F a sinαL b +F b sinβL b +F c sinγL b =Iw (12) F a cosα+F b cosβ+F c cosγ=mA (13) Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ), m is the mass of the boat, F a For the left thrust, F b For rightward thrust, F c For lateral thrust, L a L is the transverse lever arm. b Let α be the longitudinal lever arm, β be the left rudder angle, γ be the right rudder angle, and A be the lateral thrust angle. Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies: F a cosαL a +F b cosβL a +F c cosγL a +F a sinαL b +F b sinβL b +F c sinγL b =f ω L b (14) F a cosα+F b cosβ+F c cosγ=f v (15) Among them, f ω The lateral drag of the unmanned surface vessel, f, at an angular velocity ω. v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

2. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 1, characterized in that: In step 2, the established payload equipment models include, but are not limited to, navigation and positioning equipment models, navigation radar equipment models, and optoelectronic equipment models. The device message communication of the unmanned surface vessel is in the form of a publish-subscribe format. Each payload device has a corresponding payload service to communicate with, including receiving payload control commands and feeding back payload status information. The various payload services interact with each other through information integration services.

3. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 1, characterized in that: In step 1.1, the mathematical model for the relationship between the thrust of a single-powered hull, the rudder angle, and the mass of the unmanned surface vessel is established as follows: Establish a planar coordinate system with the center of the bow direction as the origin. Treat the unmanned surface vessel (USV) as a cuboid of a uniform medium, neglecting the drag on the USV, and using the principles of rigid body mechanics, we can obtain: F a cosαL a +F a sinαL b =Iw (2) F a cosα=mA (3) Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ), m is the mass of the boat, F a For thrust, L a L is the transverse lever arm. b Let α be the longitudinal lever arm, α be the rudder angle, and A be the longitudinal acceleration; Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies: F a cosαL a +F a sinαL b =f ω L b (4) F a cosα=f v (5) Among them, f ω The lateral drag of the unmanned surface vessel, f, at an angular velocity ω. v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

4. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 1, characterized in that: In step 1.1, the mathematical model for the relationship between the thrust of the dual-powered hull, the rudder angle, and the mass of the unmanned surface vessel is established as follows: Establish a planar coordinate system with the center of the bow direction as the origin. Treat the unmanned surface vessel (USV) as a cuboid of a uniform medium, neglecting the drag on the USV, and using the principles of rigid body mechanics, we can obtain: F a cosαL a +F b cosβL a +F a sinαL b +F b sinβL b =Iw (7) F a cosα+F b cosβ=mA (8) Where I is the moment of inertia (kg·m) 2 w is the angular acceleration (rad / s²). 2 ), m is the mass of the boat, F a For the left thrust, F b For rightward thrust, L a L is the transverse lever arm. b Let α be the longitudinal lever arm, β be the left rudder angle, β be the right rudder angle, and A be the longitudinal acceleration. Due to the viscous resistance of water, when the boat is traveling at a constant speed or turning at a constant speed, the following applies: F a cosαL a +F b cosβL a +F a sinαL b +F b sinβL b =f ω L b (9) F a cosα+F b cosβ=f v (10) Among them, f ω The lateral drag of the unmanned surface vessel, f, at an angular velocity ω. v Given the longitudinal resistance of the unmanned surface vessel at a speed of v, and considering the vessel's thrust, rudder angle, mass, and the resistance corresponding to different speeds, the achievable speed of the vessel can be calculated.

5. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 1, characterized in that: In step 1.2, the method for establishing the correspondence between the actual engine external characteristic curve and the actual speed of the unmanned surface vessel (USV) and solving for the drag coefficient of the USV at different speeds is as follows: According to the engine's external characteristic curve, the engine output power corresponding to different engine speeds can be determined. When the ship reaches a constant speed at a certain engine speed, we have: P=v*f V (16) Where P is the engine's output power, v is the unmanned surface vessel's speed, and f is the speed of the unmanned surface vessel. V The resistance experienced by the ship; By using data on speed and engine power obtained from actual boat tests, we can calculate the speed set {v1, v2…vi} and the resistance set {f1, f2…fi}, where speed vi corresponds to fi. The resistance corresponding to any speed between 0 and the maximum speed of the unmanned boat can be obtained by the difference method. The drag value at any speed can be obtained by fitting the curve; Using Newton's law of internal friction and simplifying the velocity gradient linearly, we get: f ω =μKω (17) Where μ is the fluid dynamic viscosity; K is a coefficient that needs to be corrected based on measured data; ω is the angular velocity, f ω The lateral drag of the unmanned surface vessel at an angular velocity ω.

6. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 1, characterized in that: In step 1.3, the specific method for establishing the motion model of the unmanned surface vessel based on Runge-Kutta's principle is as follows: According to Runge-Kutta's principle, the angular velocity ω at time t is the integral of the heading angular acceleration α over time t, and the longitudinal velocity v is the integral of the acceleration a over time t, that is:

7. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 2, characterized in that: First, a model of the navigation and positioning device is established. The data output by the navigation and positioning device model includes the longitude, latitude, heading angle, and absolute speed of the unmanned surface vessel (USV). Based on the longitude and latitude coordinates of the USV at time t0 and the speed and heading angular velocity output by the USV's dynamic model, the latitude coordinates and heading angle of the USV at time t0+t are calculated. The navigation and positioning device model combines the X and Y axis accelerations output by the dynamic model to form a message for the navigation and positioning device, which is then published to the information integration service. The message output frequency of the navigation and positioning device model is consistent with that of the actual device. Secondly, a navigation radar equipment model is established. The data output by the navigation radar equipment model includes the target's azimuth, distance, heading, and speed. Image processing is performed based on environmental echo data to identify and obtain target location and distance. The identified targets are tracked, and the target's speed and heading are calculated. Tracked targets are newly created and assigned batch numbers, and finally target result data is generated and published to the information integration service. Then, an optoelectronic device model is established. The data output by the optoelectronic device model includes the target's orientation, distance, target type, and recognition result. The optoelectronic equipment model receives video image data and performs target recognition on the images to obtain the target type and recognition result. After the target is identified, it tracks the target. After the tracking is stable, it sends servo commands to adjust the position of the target in the image and sends laser ranging commands to measure the distance of the target to the vessel. Combined with the current angle information of the optoelectronic equipment servo mechanism, it forms an optoelectronic data feedback message and publishes it to the information integration service. Finally, models for other loads are built based on actual needs, forming different capabilities.

8. The method for constructing a digital capability model of an unmanned surface vessel as described in claim 2, characterized in that: The input data for each load device model can be either simulation data or actual data, and the data is input via network communication; After receiving the corresponding input data, the load device model processes, transforms, and calculates the data according to the currently set parameters, and publishes the processed data to the information integration service according to the same interface protocol as the actual device. After receiving data from the payload service, the unmanned surface vessel (USV) navigation strategy algorithm performs corresponding algorithm calculations and sends control commands back to the corresponding payload service, thus completing the closed-loop simulation verification of the USV navigation strategy algorithm.

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