High-precision self-guard cooperative detection method for unmanned surface vehicle

Abstract

The invention provides a high-precision self-guard cooperative detection method for an unmanned surface vehicle, and the method comprises the following steps: obtaining the current coordinates of the unmanned surface vehicle, the coordinates of a collection range, the current heading information of the unmanned surface vehicle, the current pose information of the unmanned surface vehicle, and the point cloud data in the collection range, and carrying out the feature extraction and filtering processing; selecting a preset working point in the coordinates of the acquisition range, obtaining an unmanned ship navigation strategy by using the processed current coordinates of the unmanned ship and the current heading information of the unmanned ship based on an LOS algorithm, obtaining an unmanned ship obstacle avoidance strategy by using the processed point cloud data in the acquisition range based on a VO algorithm, and controlling the unmanned ship to move; repeating the steps after underwater communication and detection work; and when the distance between the current coordinate of the unmanned ship and the coordinate of the preset working point exceeds the deviation threshold value, obtaining a guard navigation strategy, and returning to the preset working point. The problems that an existing unmanned ship guard technology is not accurate enough and the tracking navigation capability is insufficient are solved.

Description

Technical Field

[0001] The invention belongs to the field of unmanned boat control, and in particular relates to a high-precision self-position cooperative detection method for a surface unmanned boat. Background Art

[0002] In the field of modern ocean exploration and monitoring, the use of unmanned surface vehicles (UAVs) for ocean data collection, monitoring and reconnaissance has become an efficient and low-cost means. With the growing demand for the development and utilization of marine resources and the protection of the marine environment, higher requirements are placed on UAV technology.

[0003] High-precision self-positioning means that the unmanned boat can accurately control and maintain its specific position on the water surface or on a preset route through its own positioning system and power system without external intervention. This capability is essential for performing precise marine scientific research, environmental monitoring, seabed topography mapping and other tasks. Traditional self-positioning technology relies on satellite navigation systems (such as GPS) for positioning, but in certain environments (such as polar waters, densely populated ship areas or deep-sea canyons), GPS signals may be interfered with or completely lost, which requires unmanned boats to have more advanced self-positioning technology, such as the combination of inertial navigation systems (INS) and GPS, sonar positioning systems, etc., to improve the accuracy and reliability of self-positioning.

[0004] Through the above analysis, the problems and defects of the existing technology are: the unmanned boat is affected by external disturbances or failures during navigation, resulting in insufficient navigation accuracy of the unmanned boat and lack of long-term accurate self-positioning capability, which brings immeasurable consequences to the unmanned boat guard system. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a high-precision self-position collaborative detection method for a surface unmanned boat, which solves the problems that the existing unmanned boat guarding technology is not accurate enough and the tracking navigation capability is insufficient.

[0006] The technical solution of the present invention is as follows:

[0007] The high-precision self-positioning cooperative detection method of a surface unmanned vehicle is characterized by comprising the following steps:

[0008] Obtain the current coordinates of the unmanned boat, the coordinates of the acquisition range, the current heading information of the unmanned boat, the current posture information of the unmanned boat and the point cloud data within the acquisition range, and perform feature extraction and filtering processing on them;

[0009] A preset working point is selected within the acquisition range coordinates, and the unmanned boat navigation strategy is obtained based on the LOS algorithm using the processed current coordinates of the unmanned boat and the current heading information of the unmanned boat. The unmanned boat obstacle avoidance strategy is obtained based on the VO algorithm using the processed point cloud data within the acquisition range. The unmanned boat movement is controlled according to the unmanned boat navigation strategy and the unmanned boat obstacle avoidance strategy, and the updated current coordinates of the unmanned boat, the current heading information of the unmanned boat, and the current posture information of the unmanned boat are obtained;

[0010] Repeat the above steps after underwater communication and detection work;

[0011] A deviation threshold is preset. When the distance between the current coordinates of the unmanned boat and the preset working point coordinates exceeds the deviation threshold, a position keeping navigation strategy is obtained using the current coordinates of the unmanned boat and the preset working point coordinates to return to the preset working point.

[0012] Preferably, in the high-precision self-positioning collaborative detection method for unmanned surface boats,

[0013] The target path is generated according to the position of the preset working point:

[0014] P n (x n ,y n ) n+1 (x n+1 ,y n+1 ) n+2 (x n+2 ,y n+2 );

[0015] where x n ,y n Indicates the position coordinates of the unmanned ship;

[0016] Take O b is the center of the circle, R los =nL (n>1) is the arc with radius and target path P n P n+1 Intersection distance P n+1 The closer intersection point is P los point, where L is the captain of the unmanned boat;

[0017] Calculate P los Point, the formula is:

[0018]

[0019]

[0020] According to R los The real-time lateral deviation error e of the unmanned boat relative to the route can be obtained:

[0021]

[0022] Note that G is an absolute angle, and the corresponding angle control amount that the unmanned boat needs to turn is Δψ G =ψ G -ψ;

[0023] Regarding the route switching problem, when the unmanned boat approaches the target point P n+1 When the target path needs to be P n P n+1 Switch to P n+1 P n+2 ;

[0024] When the unmanned boat enters n+1 It is within the acceptance circle with center R0 as radius, that is, , start switching the target path;

[0025] Take e as a system state variable, and define a new state variable as

[0026] The system model is:

[0027]

[0028] The interference term can be expressed as ω = [ητ1 ητ2 ητ3 ητ4] T ; τ1, τ2, τ3, τ4∈[-1,1] are random numbers of standard distribution, and η is the interference intensity coefficient.

[0029] Preferably, in the high-precision self-positioning collaborative detection method for unmanned surface boats,

[0030] For the unmanned boat obstacle avoidance algorithm, it is assumed that the obstacle speed uncertainty measure is Δv B and Δφ B , for Δv B and Δφ B Discrete the data into N and M parts respectively;

[0031] The actual speed of the obstacle can be regarded as a speed set:

[0032]

[0033] In the reference velocity space of the unmanned boat, the velocity of the obstacle v B After superimposing it on the unmanned boat, the obstacle can be regarded as stationary;

[0034] The uncertainty of the obstacle speed is Δv BThe UVO area is equivalent to the obstacle at a certain speed v. B ', thus the uncertainty of obstacle speed is transformed into certainty, which is defined as;

[0035] v B '=v B +Δv B '

[0036]

[0037] where Δv B ' represents the velocity vector between the vertices of the original VO region and the UVO region;

[0038] The UVO region can be defined as:

[0039]

[0040] in

[0041] Preferably, in the high-precision self-positioning collaborative detection method for unmanned surface boats,

[0042] The surface unmanned boat is equipped with GPS, inertial navigation, and laser radar to obtain the current coordinates of the unmanned boat, the coordinates of the acquisition range, the current heading information of the unmanned boat, the current posture information of the unmanned boat, and the point cloud data within the acquisition range;

[0043] The unmanned surface boat is also provided with a visible light sensor, a navigation radar and an AIS sensor.

[0044] Preferably, in the high-precision self-positioning collaborative detection method for the surface unmanned boat, the point cloud data within the acquisition range includes obstacles, surface ships and surge information.

[0045] Preferably, in the high-precision self-positioning collaborative detection method for unmanned surface boats,

[0046] A winch is arranged on the bow deck of the unmanned surface boat, the winch is driven by a motor, and an underwater communicator is arranged at the cable head end of the winch;

[0047] The maximum depth of the underwater communicator is 50 meters;

[0048] The underwater communicator is initially placed in a sea well with a diameter of 150 mm at the front end of the surface unmanned boat.

[0049] The present invention has the following beneficial effects:

[0050] By controlling the equipment of the unmanned boat to establish a communication connection with the ground control center, information sharing and transmission can be achieved, and real-time decision-making for collaborative detection tasks can be achieved;

[0051] Through accurate processing and analysis of sensor data, high-precision positioning and effective path planning of surface unmanned vehicles in collaborative detection tasks can be achieved;

[0052] In terms of unmanned boat navigation, the present invention adopts an unmanned boat track guidance calculation method based on the LOS algorithm. LOS navigation has the advantages of accurate target tracking and simple calculation. It is widely used in the fields of missile interception tracking control, underwater vehicles and ship path following control, and has achieved good navigation effects.

[0053] In terms of unmanned boat obstacle avoidance, the VO method is used for obstacle avoidance, which can quickly calculate the obstacle avoidance strategy and is suitable for application scenarios that require real-time response; and parameters can be adjusted according to different application scenarios; by predicting the movement trajectory of obstacles, possible collisions can be predicted and avoided in advance, enhancing the safety of the unmanned boat self-defense system.

[0054] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of an embodiment of a high-precision self-positioning cooperative detection method for an unmanned surface vehicle provided by the present invention;

[0056] Figure 2 An overall layout diagram of an unmanned boat in one embodiment of a high-precision self-positioning cooperative detection method for a surface unmanned boat provided by the present invention;

[0057] Figure 3 A system framework diagram of an unmanned boat in one embodiment of the high-precision self-positioning collaborative detection method for a surface unmanned boat provided by the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0059] like Figure 1 As shown, the present invention provides a high-precision self-positioning collaborative detection method for a surface unmanned boat, which comprises the following steps:

[0060] Step S1: using the multi-sensor system carried by the surface unmanned boat, including GPS, inertial navigation, laser radar, etc., to collect the current heading, position, pitch angle and obstacle information of the unmanned boat;

[0061] Specifically, the coordinate system of the unmanned boat is established to perceive the environment. It is mainly equipped with various surface detection sensors to realize the perception and modeling of the water environment, obtain obstacles, targets of interest, typical geographical features, etc., and provide data support for the autonomous navigation system.

[0062] According to the coordinate information of the working area located by using the GPS positioning service, the acquisition range coordinate information is set according to the working area range, and the heading information is obtained;

[0063] According to the above, the inertial navigation device is used to obtain the current posture information of the unmanned boat, and the speed, yaw angle and position information in the navigation coordinate system are obtained;

[0064] Using the laser radar device to obtain point cloud data within the acquisition range, including information such as obstacles, sea surface ships and surges;

[0065] Based on the working range and the point cloud information of the acquisition range, feature identification is performed to obtain multi-target distinction;

[0066] Point cloud target features are extracted from the multi-target partitions respectively, and sea surface clutter is filtered out.

[0067] The surrounding environment information of the unmanned boat is obtained through various environmental sensing devices and processed to obtain information such as point cloud distribution, heading, position and pitch angle after feature processing.

[0068] Step S2: Set the location of the target point of the unmanned boat, and the unmanned boat starts the autonomous tracking mode to reach the target point while avoiding obstacles.

[0069] Specifically, it uses lidar, visible light, navigation radar, AIS and other sensors to obtain external environment information, and responds to instructions or routes set by the user. The autonomous navigation subsystem controls the unmanned aerial survey boat to follow the preset route and perform real-time avoidance when encountering dangerous situations.

[0070] Set the position of the unmanned boat target point: P n (x n ,y n ), P n+1 (x n+1 ,y n+1 ), P n+2 (x n+2 ,y n+2 ). The generated target path is:

[0071] P n (x n ,y n ) n+1 (x n+1 ,y n+1 )n+2 (x n+2 ,y n+2 ). where x n ,y n Indicates the location coordinates of the unmanned ship.

[0072] Take O b is the center of the circle, R los =nL (n>1) (L is the length of the ship) is the arc with radius and the target path P n P n+1 Intersection distance P n+1 A closer intersection point is P los point.

[0073] Calculate P los Point, the formula is:

[0074]

[0075]

[0076] Furthermore, according to R los The real-time lateral deviation error e of the unmanned boat relative to the route can be obtained:

[0077]

[0078] Note that G is an absolute angle, and the corresponding angle control amount that the unmanned boat needs to turn is Δψ G =ψ G -ψ.

[0079] Furthermore, for the route switching problem, when the unmanned boat approaches the target point P n+1 When the target path needs to be P n P n+1 Switch to P n+1 P n+2 .

[0080] When the unmanned boat enters n+1 It is within the acceptance circle with center R0 as radius, that is, , the target path switching starts.

[0081] In order to design the overall controller of the unmanned boat, the path lateral tracking error e is gradually reduced to achieve the purpose of high-precision straight line path following. The following system model needs to be introduced, and e is used as a system state variable, so the new state variable is defined as

[0082] The system model is:

[0083]

[0084] The interference term can be expressed as ω = [ητ1 ητ2 ητ3 ητ4] T ; τ1, τ2, τ3, τ4∈[-1,1] are random numbers of standard distribution, and η is the interference intensity coefficient.

[0085] Furthermore, for the unmanned boat obstacle avoidance algorithm, it is assumed that the obstacle speed uncertainty measure is Δv B and Δφ B , for Δv B and Δφ B Discretize it into N and M parts respectively. Then the actual speed of the obstacle can be regarded as a speed set:

[0086]

[0087] In the reference velocity space of the unmanned boat, the velocity of the obstacle v B After being superimposed on the unmanned boat, the obstacle can be regarded as stationary. Therefore, the uncertainty Δv of the obstacle speed is B The circle with radius v can be equivalent to the velocity set of the obstacle under the condition of velocity uncertainty as shown in the above formula. Therefore, the UVO area can be equivalent to the obstacle at a certain speed v B ', thereby converting the uncertainty of obstacle speed into certainty. It is defined as;

[0088] v B '=v B +Δv B '

[0089]

[0090] where Δv B ' represents the velocity vector between the vertices of the original VO region and the UVO region.

[0091] The UVO region can be defined as:

[0092]

[0093] in

[0094] In terms of unmanned boat navigation, the present invention adopts an unmanned boat track guidance calculation method based on the LOS algorithm. LOS navigation has the advantages of accurate target tracking and simple calculation. It is widely used in the fields of missile interception tracking control, underwater vehicles and ship path following control, and has achieved good navigation effects. In terms of unmanned boat obstacle avoidance, the VO method is used for obstacle avoidance, which can quickly calculate the obstacle avoidance strategy and is suitable for application scenarios that require real-time response; and parameters can be adjusted according to different application scenarios; by predicting the movement trajectory of obstacles, possible collisions can be predicted and avoided in advance, thereby enhancing the safety of the unmanned boat self-defense system.

[0095] Step S3: The unmanned boat lowers the underwater communicator in the target area, conducts underwater communication and completes the detection mission.

[0096] Specifically, if Figure 2 As shown, the underwater communicator is lowered by the cooperation of a winch and the underwater communicator.

[0097] The winch is driven by a synchronous belt, and the motor drives the cable connected to the underwater communicator to realize the lowering and raising of the underwater communicator. The underwater communicator is initially placed in a sea well with a diameter of 150 mm at the front end of the unmanned boat.

[0098] By setting up a winch and cable, the maximum depth of the underwater communicator is 50 meters.

[0099] The underwater communicator located at the bow of the boat is installed in a suspended manner. A small winch and bracket are installed on the bow deck. The winch is connected to the underwater communicator through a cable and is usually stored in a sea well near the winch.

[0100] When in use, the underwater communicator is lowered into the water below the bottom of the boat by remotely controlling the winch.

[0101] Step S4: After the unmanned boat leaves the target area with the ocean current, it returns to the preset working point at a fixed time to realize the self-guarding function.

[0102] Specifically, if Figure 3 shown.

[0103] When the unmanned boat detects that it has deviated from the preset working point by more than the set threshold due to the influence of the ocean current, it starts the power system, automatically adjusts the course according to the calculated optimal route, and returns to the preset working point;

[0104] After the unmanned boat reaches the preset working point, it continues to monitor the position and repeats the above steps as needed to achieve the self-positioning function;

[0105] The threshold value may be adjusted according to marine environmental conditions or mission requirements;

[0106] The power system includes an electric propeller, which automatically adjusts the propulsion force and heading according to the distance and deviation angle between the unmanned boat and the preset working point to minimize energy consumption while ensuring rapid and accurate return to the preset working point;

[0107] When the unmanned boat is actually running, the navigation and positioning equipment obtains the current position and posture status of the platform body in real time, including information such as longitude and latitude, speed, heading, three-axis angle and three-axis speed, and performs navigation solution according to the navigation mission information and the position status of the platform body. The solution process includes multiple different levels such as navigation layer-guidance layer-control layer, and finally generates control commands that can be actually executed by the drive actuator, such as rudder angle value and throttle (speed) value. Finally, a set of embedded real-time control systems is used to control the movement of the engine and steering mechanism to achieve turning and acceleration and deceleration actions, thereby completing the autonomous navigation task. Among them, if environmental perception equipment such as navigation radar, laser radar, camera, etc. detects dynamic and static obstacles on the sea surface, the automatic obstacle avoidance algorithm is called for solution, and the unmanned boat is guided to avoid obstacles from multiple levels such as route planning and speed and heading adjustment to ensure the safe navigation of the unmanned boat. And the current position is detected regularly to realize the self-defense function.

[0108] The present invention also provides an unmanned surface boat system with a self-maintaining position function, comprising:

[0109] (a) a navigation system configured to monitor the geographic location of the unmanned boat in real time and compare it with the location of a preset working point;

[0110] (b) a control unit configured to receive position information provided by the navigation system and calculate an optimal route to return to the preset working point when the unmanned boat deviates from the preset working point by more than a set threshold;

[0111] (c) A power system is electrically connected to the control unit, receives a heading adjustment command, and adjusts the propulsion force and heading of the unmanned boat according to the command of the control unit to achieve a self-guarding function.

[0112] The control unit is further configured to adjust a threshold value for deviation from a preset working point according to marine environmental conditions or mission requirements.

[0113] The unmanned boat can flexibly and autonomously switch between working and resting modes, complete the lowering and self-defense functions of the underwater communicator, and can also change different control thresholds according to environmental changes and different needs.

[0114] The present invention discloses a high-precision self-positioning collaborative detection method based on a surface unmanned boat, which relates to the field of control, including collecting the current heading, position and pitch angle of the unmanned boat and other information according to the multi-sensor system carried by the surface unmanned boat, including GPS, inertial navigation, laser radar, etc.; setting the position of the target point of the unmanned boat, the unmanned boat starts the autonomous tracking mode, and uses the VO method to avoid obstacles while reaching the target point; the unmanned boat lowers the underwater communicator in the target area to complete the detection task; after the unmanned boat leaves the target area with the ocean current, it returns to the working point at a fixed time to realize the self-positioning function; establishes a communication connection with the ground control center to realize information sharing and transmission, and real-time decision-making of collaborative detection tasks. The present invention realizes high-precision positioning and effective path planning of the surface unmanned boat in the collaborative detection task through accurate processing and analysis of sensor data. The method has the advantages of strong real-time performance, wide adaptability, high precision, etc., and can be widely used in water area inspection, marine environment monitoring and other fields.

[0115] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. A high-precision self-positioning cooperative detection method for unmanned surface vessels, characterized in that: The following steps are involved: Obtain the current coordinates of the unmanned boat, the coordinates of the acquisition range, the current heading information of the unmanned boat, the current posture information of the unmanned boat and the point cloud data within the acquisition range, and perform feature extraction and filtering processing on them; A preset working point is selected within the acquisition range coordinates, and the unmanned boat navigation strategy is obtained based on the LOS algorithm using the processed current coordinates of the unmanned boat and the current heading information of the unmanned boat. The unmanned boat obstacle avoidance strategy is obtained based on the VO algorithm using the processed point cloud data within the acquisition range. The unmanned boat movement is controlled according to the unmanned boat navigation strategy and the unmanned boat obstacle avoidance strategy, and the updated current coordinates of the unmanned boat, the current heading information of the unmanned boat, and the current posture information of the unmanned boat are obtained; Repeat the above steps after underwater communication and detection work; A deviation threshold is preset. When the distance between the current coordinates of the unmanned boat and the preset working point coordinates exceeds the deviation threshold, a position keeping navigation strategy is obtained using the current coordinates of the unmanned boat and the preset working point coordinates to return to the preset working point.

2. The high-precision self-positioning cooperative detection method for an unmanned surface vehicle according to claim 1, characterized in that: The target path is generated according to the position of the preset working point: P n (x n ,y n )P n+1 (x n+1 ,y n+1 )P n+2 (x n+2 ,y n+2 ); where x n ,y n Indicates the position coordinates of the unmanned ship; Take O b is the center of the circle, R los =nL (n>1) is the arc with radius and target path P n P n+1 Intersection distance P n+1 The closer intersection point is P los point, where L is the captain of the unmanned boat; Calculate P los Point, the formula is: According to R los The real-time lateral deviation error e of the unmanned boat relative to the route can be obtained: Note that G is an absolute angle, and the corresponding angle control amount that the unmanned boat needs to turn is Δψ G =ψ G -ψ; Regarding the route switching problem, when the unmanned boat approaches the target point P n+1 When the target path needs to be P n P n+1 Switch to P n+ 1P n+2 ; When the unmanned boat enters n+1 It is within the acceptance circle with center R0 as radius, that is, , start switching the target path; Take e as a system state variable, and define a new state variable as The system model is: The interference term can be expressed as ω = [ητ1 ητ2 ητ3 ητ4] T ; τ1, τ2, τ3, τ4∈[-1,1] are random numbers of standard distribution, and η is the interference intensity coefficient.

3. The high-precision self-positioning cooperative detection method for an unmanned surface vehicle according to claim 2, characterized in that: For the unmanned boat obstacle avoidance algorithm, it is assumed that the obstacle speed uncertainty measure is Δv B and Δφ B , for Δv B and Δφ B Discrete the data into N and M parts respectively; The actual speed of the obstacle can be regarded as a speed set: In the reference velocity space of the unmanned boat, the velocity of the obstacle v B After superimposing it on the unmanned boat, the obstacle can be regarded as stationary; The uncertainty of the obstacle speed is Δv B The UVO area is equivalent to the obstacle at a certain speed v. B ', thereby transforming the obstacle speed uncertainty into certainty, defined as; v B '=v B +Δv B ' where Δv B ' represents the velocity vector between the vertices of the original VO region and the UVO region; The UVO region can be defined as: in 4. The high-precision self-positioning cooperative detection method for an unmanned surface vehicle according to claim 3, characterized in that: The surface unmanned boat is equipped with GPS, inertial navigation, and laser radar to obtain the current coordinates of the unmanned boat, the coordinates of the acquisition range, the current heading information of the unmanned boat, the current posture information of the unmanned boat, and the point cloud data within the acquisition range; The unmanned surface boat is also provided with a visible light sensor, a navigation radar and an AIS sensor.

5. The high-precision self-positioning cooperative detection method for an unmanned surface vehicle according to claim 4, characterized in that: The point cloud data within the acquisition range includes obstacles, sea surface ships and surge information.

6. The high-precision self-positioning cooperative detection method for an unmanned surface vehicle according to claim 5, characterized in that: A winch is arranged on the bow deck of the unmanned surface boat, the winch is driven by a motor, and an underwater communicator is arranged at the cable head end of the winch; The maximum depth of the underwater communicator is 50 meters; The underwater communicator is initially placed in a sea well with a diameter of 150 mm at the front end of the surface unmanned boat.

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