An unmanned super-speed ship mine-sweeping path planning method

Abstract

The application discloses a kind of unmanned superhigh-speed warship mine-sweeping path planning method, to realize in " superhigh-speed " condition, unmanned warship autonomous obstacle avoidance and guarantee mine-sweeping task optimum, the application proposes a kind of improved artificial potential field method path planning method, 1) the dynamics model of superhigh-speed unmanned warship is established;2) determine dynamics constraint;3) real-time acquisition channel information, mine and other obstacle information;4) unmanned warship channel path planning is carried out by improved artificial potential field method.Specifically, the potential field function considering attractive force and repulsive force is established, the target point is attractive force, the obstacle is repulsive force, and the smooth obstacle avoidance path is planned, and the path planning is divided into static environment global path and dynamic environment local path;Through the method, unmanned superhigh-speed warship can autonomously avoid mine and other obstacles, and quickly and efficiently complete the mine-sweeping task in channel area, so as to guarantee the safety of ship channel navigation.The application has the practicability of warship path planning.

Description

Technical Field

[0001] This invention relates to the field of unmanned surface vessel (USV) path planning, and in particular to a method for minesweeping path planning for unmanned ultra-high-speed vessels. Background Technology

[0002] Unmanned surface vessels (USVs) can perform tasks such as intelligence gathering, rapid strikes, mine clearance, and marine surveying by carrying different modules. Path planning is a key technology for autonomous navigation of USVs and an important problem that needs to be solved. Path planning for USVs is divided into two types: global path planning and local path planning. Global path planning is based on known environmental and obstacle information, and commonly used algorithms include Dijkstra's algorithm, A* algorithm, ant colony algorithm, genetic algorithm, and intelligent droplet algorithm. Local path planning is based on unknown environmental and obstacle information, and commonly used algorithms include artificial potential field method, velocity obstacle method, and vector field histogram method.

[0003] The minesweeping path planning method for unmanned ultra-high-speed vessels differs from that of general unmanned surface vessels (USVs). Firstly, it is "ultra-high-speed," meaning that after modifying retired aircraft engines, the unmanned vessel's cruising speed can reach over 150 knots. Secondly, it involves "minesweeping," requiring the USV not only to autonomously and safely avoid obstacles but also to clear mines from the waterway area as quickly and completely as possible to protect the safety of shipping lanes. Summary of the Invention

[0004] In view of this, the present invention provides a method for minesweeping path planning for unmanned ultra-high-speed vessels, comprising:

[0005] Step S1: Establish a six-degree-of-freedom motion model for the ultra-high-speed unmanned surface vessel;

[0006] Step S2: Determine the dynamic constraints of the unmanned surface vessel, i.e., the minimum turning radius;

[0007] Step S3: Obtain real-time information on waterway mines and other obstacles;

[0008] Step S4 involves planning the unmanned surface vessel's (USV) path by improving the artificial potential field method. Specifically, this includes establishing the attractive and repulsive potential fields of the potential field method and optimizing the path.

[0009] Specifically, step S1 includes:

[0010] S11: Establish a coordinate system for the spatial motion of the ultra-high-speed unmanned surface vessel. The origin of the coordinate system is at the center of gravity of the vessel. The positive X-axis points towards the bow, the positive Y-axis points towards the starboard side, and the Z-axis points vertically downward.

[0011] S12: Establish a six-degree-of-freedom maneuvering motion model for the ultra-high-speed unmanned surface vessel; the projection of the six-degree-of-freedom maneuvering mathematical model of the unmanned surface vessel onto the coordinate system is...

[0012]

[0013] Where: M is the mass matrix of the unmanned surface vessel; m is the mass of the unmanned surface vessel; I x I y I y For the moment of inertia of the unmanned surface vessel about its center of mass, F x F y F z denoted as , where K, M, and N are the external forces acting on the unmanned surface vessel (USV) during its navigation, respectively; denoted as , where is the input torque during its navigation; denoted as u, v, and w are the linear velocities of sway, roll, and heave of the USV; and denoted as angular velocities of roll, pitch, and yaw of the USV.

[0014] The forces acting on the hull are:

[0015]

[0016] Wherein: subscripts G, B, I, V, L, J, and D represent the gravity, buoyancy, inertial force, viscous force, dynamic lift, waterjet propulsion, and environmental disturbance force acting on the hull, respectively.

[0017] Specifically, step S2 includes: determining the dynamic constraints of the unmanned surface vessel, i.e., the minimum turning radius, which includes: based on routine test data and experience, giving the minimum turning radius r of the unmanned surface vessel. min .

[0018] Specifically, in step S4, the unmanned surface vessel (USV) path planning is performed using an improved artificial potential field method, which includes:

[0019] S41: Processes and distinguishes obstacle information within the heading range υ*10s of the unmanned surface vessel. The obstacles can be divided into two categories: mines and other obstacles.

[0020] S42: Analyze and process the distribution of mine obstacles within the heading range υ*10s. Using the Floyd algorithm, obtain the approximate shortest path for mine clearance within the heading range υ*10s. Assume the obtained path is:

[0021] Mine1→Mine2→Mine3…→Mine n

[0022] Where Mine represents a sea mine, and n is the number of sea mines identified within the course range (υ*10s).

[0023] Analyze and process dynamic obstacle information within the heading range υ*10s, denoted as D_barrier

[0024] Analyze and process static obstacle information within a heading range of υ*10s, denoted as S_barrier.

[0025] S43: Path planning for unmanned surface vessels based on the improved artificial potential field method includes:

[0026] In the path planning of the unmanned surface vessel (USV), Mine1 within the heading range υ*10s is regarded as the target point. Upon reaching the safe area near Mine1, the mine-clearing work is completed. Upon reaching Mine1, the obstacle information is updated in real time at the millisecond level through the USV's onboard sensor system, and the path planning to reach Mine1 is continuously adjusted. After reaching Mine1, S41 is re-executed until the mine-clearing of the waterway is completed.

[0027] The process of reaching Mine1 is achieved through an improved artificial potential field method.

[0028] Specifically, step S43, improving the artificial potential field method, includes:

[0029] S431: Establishing the gravitational field function of the improved artificial potential field method:

[0030]

[0031] Where η is the proportional gain coefficient, ρ(q,q) g Let q be a vector representing the position of the unmanned surface vessel relative to the target point. g Euclidean distance between them | qq g |, where the vector direction is the position of the unmanned surface vessel pointing towards the target point;

[0032] The corresponding gravitational force F att (X) represents the negative gradient of the gravitational field:

[0033]

[0034] S432: Establish the repulsive field function of the improved artificial potential field method:

[0035]

[0036] Where k is a direct proportionality coefficient, ρ(q,q) g ) is a vector representing the distance between the unmanned surface vessel and the obstacle |qq g |,ρ0 is the maximum distance at which the obstacle affects the unmanned surface vessel;

[0037] The corresponding gravitational force F req (X) represents the negative gradient of the gravitational field:

[0038]

[0039]

[0040] Where F reo1 The direction is from the obstacle to the unmanned boat, Freo2 The direction is from the unmanned vessel pointing towards the target point.

[0041] S433: Establishing the resultant potential field of the improved artificial potential field method:

[0042]

[0043] Combined efforts:

[0044] F(X)=F att (X)+F req (X)

[0045] S433: Dynamically constrained path planning, where the maximum angle difference between adjacent path points is:

[0046]

[0047] Where, r min This represents the minimum turning radius of the unmanned surface vessel.

[0048] When the angle Δθ of adjacent paths i Satisfying Δθ i >Δθ max When, let Δθ i =Δθ max This limits the radius of curvature of the path;

[0049] S434: Based on the dynamic model in step S1, correct adjacent paths according to the MPC prediction model and plan a smooth path;

[0050] In S434, based on the dynamic model of the unmanned surface vessel (USV) and then on the MPC prediction model, the motion state of the USV under the current path for a future period of time is predicted. If the prediction result shows that the path is not smooth enough and there is a large motion jitter, the positional relationship of adjacent path points can be adjusted to correct the path and make the overall path smoother.

[0051] Beneficial effects:

[0052] The mine-clearing path planning method for ultra-high-speed unmanned vessels of this invention enables unmanned vessels to autonomously avoid obstacles and navigate under ultra-high-speed conditions.

[0053] The dynamic modeling and constraints of this invention can accurately describe the motion state of unmanned vessels.

[0054] The multi-source obstacle information fusion method of the present invention can acquire obstacle information in the navigation area in real time.

[0055] The improved artificial potential field method of this invention can be used to plan smooth, real-time obstacle avoidance paths for unmanned vessels.

[0056] The method of combining global and local path planning in this invention enables unmanned vessels to quickly and safely avoid obstacles.

[0057] The method of this invention can greatly improve the mine-clearing efficiency of unmanned ultra-high-speed vessels, shorten the mine-clearing time, and reduce the mine-clearing risk.

[0058] The path planning method of this invention can effectively ensure the safety of ship navigation channels. Attached Figure Description

[0059] Figure 1 This is the coordinate system used in this invention;

[0060] Figure 2 This is a schematic diagram of the unmanned ultra-high-speed vessel minesweeping path planning method in this invention. Detailed Implementation

[0061] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0062] This invention provides a method for minesweeping path planning for unmanned ultra-high-speed vessels, the coordinate system of which is shown in the figure below. Figure 1 As shown in the flowchart, Figure 2 As shown, it includes:

[0063] Step S1: Establish a six-degree-of-freedom motion model of the ultra-high-speed unmanned surface vessel; Step S1 includes:

[0064] S11: Establish a coordinate system for the spatial motion of the ultra-high-speed unmanned surface vessel, such as... Figure 1 As shown, the origin of the coordinate system is at the center of gravity of the boat, the positive X-axis points towards the bow, the positive Y-axis points towards the starboard side, and the Z-axis points vertically downwards.

[0065] S12: Establish a six-degree-of-freedom maneuvering motion model for the ultra-high-speed unmanned surface vessel; the projection of the six-degree-of-freedom maneuvering mathematical model of the unmanned surface vessel onto the coordinate system is...

[0066]

[0067] Where: M is the mass matrix of the unmanned surface vessel; m is the mass of the unmanned surface vessel; I x I y I y For the moment of inertia of the unmanned surface vessel about its center of mass, F x F y F z Let K, M, and N be the external forces acting on the unmanned surface vessel (USV) during its navigation, u, v, and w be the linear velocities of its sway, roll, and heave, and p, q, and r be the angular velocities of its roll, pitch, and yaw. The forces acting on the hull are:

[0068]

[0069] Wherein: subscripts G, B, I, V, L, J, and D represent the gravity, buoyancy, inertial force, viscous force, dynamic lift, waterjet propulsion, and environmental disturbance force acting on the hull, respectively.

[0070] Step S2, determine the dynamic constraints of the unmanned surface vessel (USV), i.e., the minimum turning radius; this includes: determining the dynamic constraints of the USV, i.e., the minimum turning radius, by using routine test data and combining experience to determine the minimum turning radius r of the USV. min .

[0071] Step S3: Real-time acquisition of information on waterway mines and other obstacles; identification and location of mines and other obstacles using equipment such as the UAV's onboard vision system, infrared system, and millimeter-wave radar. Detection and differentiation of underwater mines and obstacles using sonar and multi-beam sonar systems. Detection of underwater metal-containing mines using magnetic and electric field detectors; accessing databases to obtain previously known waterway mine distribution information; data integration and multi-source heterogeneous fusion to improve obstacle identification accuracy; classification of identified obstacles by type, such as mines, buoys, and reefs. Real-time tracking of obstacle drift in flowing water.

[0072] Accurately inputting obstacle information into the path planning system enables unmanned surface vessels to plan and adjust their routes in real time.

[0073] Step S4 involves planning the unmanned surface vessel's (USV) path using an improved artificial potential field method. This specifically includes establishing the attractive and repulsive potential fields of the potential field method and optimizing the path. Specifically, step S4, which involves planning the USV path using an improved artificial potential field method, includes:

[0074] S41: Processes and distinguishes obstacle information within the heading range υ*10s of the unmanned surface vessel. The obstacles can be divided into two categories: mines and other obstacles.

[0075] S42: Analyze and process the distribution of mine obstacles within the heading range υ*10s. Using the Floyd algorithm, obtain the approximate shortest path for mine clearance within the heading range υ*10s. Assume the obtained path is:

[0076] Mine1→Mine2→Mine3…→Mine n

[0077] Where Mine represents a sea mine, and n is the number of sea mines identified within the course range (υ*10s).

[0078] Analyze and process dynamic obstacle information within the heading range υ*10s, denoted as D_barrier

[0079] Analyze and process static obstacle information within a heading range of υ*10s, denoted as S_barrier.

[0080] S43: Path planning for unmanned surface vessels based on the improved artificial potential field method includes:

[0081] In the path planning of the unmanned surface vessel (USV), Mine1 within the heading range of υ*10s is regarded as the target point. Upon reaching the safe area near Mine1, the mine-clearing work is completed. Upon reaching Mine1, the obstacle information is updated in real time at the millisecond level through the USV's onboard sensor system, and the path planning to reach Mine1 is continuously adjusted. After reaching Mine1, S41 is re-executed until the mine-clearing of the waterway is completed.

[0082] The process of reaching Mine1 is achieved through an improved artificial potential field method.

[0083] The improved artificial potential field method in step S43 specifically includes:

[0084] S431: Establishing the gravitational field function of the improved artificial potential field method:

[0085]

[0086] Where η is the proportional gain coefficient, ρ(q,q) g Let q be a vector representing the position of the unmanned surface vessel relative to the target point. g Euclidean distance between them | qq g |, where the vector direction is the position of the unmanned surface vessel pointing towards the target point.

[0087] The corresponding gravitational force F att (X) represents the negative gradient of the gravitational field:

[0088]

[0089] S432: Establish the repulsive field function of the improved artificial potential field method:

[0090]

[0091] Where k is a direct proportionality coefficient, ρ(q,q) g ) is a vector representing the distance between the unmanned surface vessel and the obstacle |qq g |,ρ0 is the maximum distance at which the obstacle affects the unmanned surface vessel;

[0092] The corresponding gravitational force F req (X) represents the negative gradient of the gravitational field:

[0093]

[0094]

[0095] Where F reo1 The direction is from the obstacle to the unmanned boat, F reo2 The direction is from the unmanned vessel pointing towards the target point.

[0096] S433: Establishing the resultant potential field of the improved artificial potential field method:

[0097]

[0098] Combined efforts:

[0099] F(X)=F att (X)+F req (X)

[0100] S433: Dynamically constrained path planning, where the maximum angle difference between adjacent path points is:

[0101]

[0102] Where, r min This represents the minimum turning radius of the unmanned surface vessel.

[0103] When the angle Δθ of adjacent paths i Satisfying Δθ i >Δθ max When, let Δθ i =Δθ max This limits the radius of curvature of the path;

[0104] S434: Based on the dynamic model in step S1, correct adjacent paths according to the MPC prediction model and plan a smooth path.

[0105] In S434, based on the dynamic model of the unmanned surface vessel (USV) and then on the MPC prediction model, the motion state of the USV under the current path for a future period of time is predicted. If the prediction result shows that the path is not smooth enough and there is a large motion jitter, the positional relationship of adjacent path points can be adjusted to correct the path and make the overall path smoother.

[0106] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0107] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for minesweeping path planning for unmanned ultra-high-speed vessels, characterized in that, include: Step S1: Establish a six-degree-of-freedom motion model for the ultra-high-speed unmanned surface vessel; Step S2: Determine the dynamic constraints of the unmanned surface vessel, i.e., the minimum turning radius; Step S2 includes: determining the dynamic constraints of the unmanned surface vessel, i.e., the minimum turning radius, which includes: based on routine test data and experience, determining the minimum turning radius of the unmanned surface vessel. ; Step S3: Obtain real-time information on waterway mines and other obstacles; Step S4 involves planning the unmanned surface vessel's (USV) path using an improved artificial potential field method. This specifically includes establishing the attractive and repulsive potential fields of the potential field method and optimizing the path. Step S4, specifically the planning of the USV path using the improved artificial potential field method, includes: S41: Handling the heading range of unmanned surface vessels * Obstacle information fusion and differentiation within 10 seconds; obstacles can be divided into two categories: mines and other obstacles. S42: Analyze and process heading range *Distribution of mine obstacles within 10 seconds, with heading range obtained using the Floyd algorithm. *The approximate shortest path in a 10-second Minesweeper game, assuming the obtained path is: in, Represents a mine, where n is the range of the course. *Number of mines identified within 10 seconds Analysis and processing of heading range Dynamic obstacle information within 10 seconds is denoted as D_barrier. Analysis and processing of heading range Static obstacle information within 10 seconds is denoted as S_barrier. S43: Path planning for unmanned surface vessels based on the improved artificial potential field method includes: In the path planning of unmanned surface vessels, the heading range is... Within 10 seconds Consider it the target point, reach Minesweeping operations were completed in the nearby safe area, and [the team / entity] arrived. In the process, the unmanned surface vessel (USV) uses its onboard sensor system to update obstacle information in real time at the millisecond level, continuously adjusting its approach. Route planning, arrival Then, S41 is executed again until the minesweeping of the waterway is completed; arrive The process is achieved through an improved artificial potential field method.

2. The unmanned ultra-high-speed vessel minesweeping path planning method as described in claim 1, characterized in that, Step S1 includes: S11: Establish a coordinate system for the spatial motion of the ultra-high-speed unmanned surface vessel. The origin of the coordinate system is at the center of gravity of the vessel. The positive X-axis points towards the bow, the positive Y-axis points towards the starboard side, and the Z-axis points vertically downward. S12: Establish a six-degree-of-freedom maneuvering motion model for the ultra-high-speed unmanned surface vessel; the projection of the six-degree-of-freedom maneuvering mathematical model of the unmanned surface vessel onto the coordinate system is... in: The mass matrix of the unmanned surface vessel; For the quality of unmanned surface vessels; , , The moment of inertia of the unmanned surface vessel about its center of mass. , , The external forces experienced by the unmanned surface vessel during its navigation. , , This is the input torque during the unmanned surface vessel's navigation. , , The linear velocities of the unmanned surface vessel's sway, roll, and heave are given. , , The angular velocities of the unmanned surface vessel's roll, pitch, and stern. The forces acting on the hull are: Wherein: subscripts G, B, I, V, L, J, and D represent the gravity, buoyancy, inertial force, viscous force, dynamic lift, waterjet propulsion, and environmental disturbance force acting on the hull, respectively.

3. The unmanned ultra-high-speed vessel minesweeping path planning method as described in claim 1, characterized in that, The improved artificial potential field method in step S43 specifically includes: S431: Establishing the gravitational field function of the improved artificial potential field method: in It is a positive proportional gain coefficient. It is a vector representing the position of the unmanned surface vessel relative to the target point. Euclidean distance between The vector direction is the position of the unmanned surface vessel pointing towards the target point; Corresponding gravity The negative gradient of the gravitational field: S432: Establish the repulsive field function of the improved artificial potential field method: in It is a direct proportionality coefficient. It is a vector representing the distance between the unmanned surface vessel and the obstacle. , The maximum distance at which an obstacle affects the unmanned surface vessel; Corresponding gravity The negative gradient of the gravitational field: in The direction was from the obstacle towards the unmanned boat. The direction is from the unmanned vessel pointing towards the target point; S433: Establishing the resultant potential field of the improved artificial potential field method: Combined efforts: S433: Dynamically constrained path planning, where the maximum angle difference between adjacent path points is: in, This represents the minimum turning radius of the unmanned surface vessel. When the angle of adjacent paths satisfy season This limits the radius of curvature of the path; S434: Based on the dynamic model in step S1, correct adjacent paths according to the MPC prediction model and plan a smooth path; In S434, based on the dynamic model of the unmanned surface vessel (USV) and then on the MPC prediction model, the motion state of the USV under the current path for a future period of time is predicted. If the prediction result shows that the path is not smooth enough and there is a large motion jitter, the positional relationship of adjacent path points can be adjusted to correct the path and make the overall path smoother.

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