CN114610046A - Unmanned ship dynamic safety trajectory planning method considering dynamic water depth - Google Patents
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Abstract
The invention relates to a dynamic safe track planning method for an unmanned ship considering dynamic water depth. The track planning method utilizes basic parameters of the unmanned ship to calculate the minimum safe water depth for navigation; defining water depth risk and barrier risk by integrating sea waves, ocean currents and dynamic and static barriers, and quantitatively evaluating the path safety level; and updating the track according to the target information output by the sensing system in real time and the navigation information output by the navigation system. The method has the advantages that the dynamic water depth factor and the water surface obstacle factor threatening navigation safety under the interference conditions of ocean currents, waves and the like in the actual ocean environment are fully considered, the defect that only discrete angle search can be carried out when a traditional A-star algorithm uses a rasterized map is overcome, and the safety and the practicability of the planned track are effectively improved.
Description
Technical Field
The invention belongs to the technical field of unmanned ship path planning, and particularly relates to an unmanned ship dynamic safety trajectory planning method considering dynamic water depth.
Background
The autonomous unmanned boat/water surface robot is an important member of a marine robot, is an important node for connecting air, sky and deep sea, becomes an important tool for marine scientific research, marine resource development, marine environment monitoring and national marine safety protection, and is increasingly paid attention by various countries. However, unmanned ship motion planning and intelligent decision making technology is still one of bottleneck factors limiting the realization of high autonomy.
In a high-dynamic and high-uncertainty marine environment, high autonomy of unmanned ship individuals and unmanned ship clusters is realized, and a plurality of factors including sea chart identification and environment modeling, behavior decision and motion planning, a robust path tracking control method and the like need to be considered. Compared with unmanned vehicles and unmanned aerial vehicles, the unmanned ship has a more complex operation environment, except the interference of complex environments such as wind, wave and flow, the inertia and motion response time of the unmanned ship are also longer than those of the unmanned vehicles and the unmanned vehicles, and larger uncertainty is brought to actual control and planning. When the unmanned ship decision planning problem is solved, the traditional motion planning method still has more defects when facing the complex environment. There is a need to design a reliable behavior decision and motion planning method under complex constraints including environmental constraints, kinematic and dynamic constraints, navigation rule constraints, and optimization target constraints.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides the unmanned ship dynamic safe track planning method considering the dynamic water depth, optimizes the traditional shortest path planning algorithm, solves the problem that the traditional method does not consider the environmental disturbance influence caused by sea waves and ocean currents so as to generate a high collision risk unsmooth air route, and can effectively improve the safety and the practicability of path planning.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a dynamic safe track planning method of an unmanned ship considering dynamic water depth comprises the following steps:
step 1, setting parameters related to a planning task of the unmanned ship;
step 2, constructing a global environment model according to the parameters in the step 1;
step 3, performing global path planning by utilizing an LT-D Lite algorithm;
step 4, establishing a local situation map based on the speed position and the speed information of the unmanned ship according to the dynamic target information input by the sensing system of the unmanned ship;
and 5, judging whether the current position of the unmanned ship is a terminal point, finishing planning if the current position of the unmanned ship reaches the terminal point, and repeating the step 4 if the current position of the unmanned ship does not reach the terminal point until the current position of the unmanned ship reaches the terminal point.
Moreover, the specific implementation method of the step 1 is as follows: the task-related parameters include: the unmanned ship has the advantages of size, draught, operability, starting point position, end point position, set task starting time and sailing speed.
Further, the step 2 includes the steps of:
step 2.1, extracting a preprocessed vector electronic chart from a chart library according to coordinates of a starting point position and an end point position of the unmanned ship navigation in the step 1, and extracting electronic chart attribute data, navigation chart water depth data, current data, obstacle data and chart positioning errors from the vector chart and performing corresponding format conversion and attribute data processing;
2.2, calculating the minimum safe water depth, and determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart;
step 2.3, according to the navigation sea chart water depth data and the obstacle data obtained in the step 2.1 and the resolution of the grid obtained in the step 2.2, interpolating the water depth by adopting an interpolation algorithm to obtain the rasterized predicted water depth;
step 2.4, acquiring hydrological, meteorological and tidal information of a task water area through forecast data according to the set task starting time and the set navigation speed in the step 1;
step 2.5, according to the resolution of the grid obtained in the step 2.2, processing the hydrological, meteorological and tidal information of the task water area obtained in the step 2.4, and obtaining dynamic water depth distribution and ocean current distribution by utilizing an interpolation method on the basis of the step 2.3;
and 2.6, constructing a global environment model according to the minimum safe water depth calculated in the step 2.2 and the dynamic water depth distribution and ocean current distribution obtained in the step 2.5.
Moreover, the specific implementation method of the step 2.2 is as follows: through establishing hydrodynamic force model or pond experiment, the navigation attitude change characteristics of unmanned ship under different hydrodynamic force conditions are analyzed and calculated, and the navigation attitude change in the actual marine environment is estimated:
according to
The minimum safe water depth is calculated,
wherein S isminAt minimum safe water depth, zmaxThe maximum amplitude of downward heave motion generated when the unmanned ship sails in irregular waves at different sailing speeds; l is a coxswain of the unmanned boat; thetamaxThe maximum amplitude value of pitching motion generated when the unmanned ship sails in irregular waves under different working conditions;
the average draught of the unmanned boat; e.g. of the typeencThe water depth value error of the electronic chart is obtained;
determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart: according to the minimum turning radius RminSea chart positioning error epThe resolution of the grid is calculated by the unmanned boat captain L and the range of the electronic chart, and the size of the grid is selected so that the number of the grids is not more than 108While the grid size should not be less than 3L + Rmin+epNot more than 10L +4Rmin+2ep。
Moreover, the specific implementation method of the step 2.3 is as follows: and (3) adopting a spline function interpolation algorithm containing obstacles, taking the obstacle data obtained in the step (2.1) as an obstacle input element, taking the water depth data as interpolation input data, setting a smoothing coefficient in a spline interpolation function, and interpolating the sparse water depth points to obtain the rasterized predicted water depth.
Moreover, the specific implementation method of the step 2.5 is as follows: the instantaneous water depth is:
S(x,y,t)＝D(x,y)+T(x,y,t)+Δ(x,y,t)
wherein S (x, y, t) is the instantaneous water depth value of the (x, y) grid at the moment t; d (x, y) is the static water depth value of the grid point, and T (x, y, T) is the tide value of the grid time T; Δ (x, y, T) is the water depth change due to other factors affecting the water level change at time T of the grid point, and the sum of T (x, y, T) and Δ (x, y, T) constitutes the dynamic water depth term in the instantaneous water depth.
Moreover, the specific calculation method in step 3 is as follows:
wherein g (N)i) Is the ith node NiActual distance cost to the starting node S; h (N)i) Is node NiAn estimated distance cost to the target node G;
the sum of the water depth risks from the starting node to the current node; alpha is a parameter for controlling water depth risk influence and is set according to the preference of a path planning target;
the sum of the water surface barrier risk degrees from the starting node to the current node; beta is a parameter for controlling the risk influence of the water surface barrier and is set according to the route planning target preference degree.
Further, the step 4 includes the steps of:
4.1, establishing a local situation map according to the global path planning obtained in the step 3 and target information input by a perception system of the unmanned ship;
step 4.2, predicting the track of the dynamic barrier according to target information input by a perception system of the unmanned ship;
4.3, judging whether collision risks exist during navigation along the current path or not according to the predicted track in the step 4.2, if so, performing the step 4.4, and if not, performing the step 5;
and 4.4, starting re-planning, updating the grids in the area according to the predicted track information and the dynamic water depth, generating a local path, and returning to the step 4.1 to update the local situation map.
And the sensing system in the step 4.1 comprises a navigation radar, a laser radar, an AIS, a panoramic camera and a depth finder.
Moreover, the specific implementation method of the step 4.3 is as follows: grid water surface collision risk:
wherein d is a node NiNode O to obstaclejEuclidean distance, a is a danger coefficient corresponding to different types of obstacles, vNiIs a grid NiVelocity c of medium sea currentNiIs a coefficient of the direction, and is,
is the direction of the sea current relative to true north, phigEpsilon [0,2 pi ]) as node NiAnd node OjAnd when k obstacle grids are arranged near the navigable node relative to the azimuth angle of true north, selecting the maximum value as the risk degree of the water surface obstacle of the node:
rS(Ni)＝max{rS[Ni,1],rS[Ni,2],...,rS[Ni,k]}。
the invention has the advantages and positive effects that:
1. the unmanned ship navigation safety track output method takes the basic parameters of the unmanned ship and the time position speed information output by the target information navigation system provided by the sensing system as input parameters, and finally outputs the unmanned ship navigation safety track comprehensively considering the dynamic water depth and the dynamic and static barriers on the water surface. The track planning method utilizes basic parameters of the unmanned ship to calculate the minimum safe water depth for navigation; defining water depth danger degree and obstacle danger degree by integrating sea waves, ocean currents and dynamic and static obstacles, and quantitatively evaluating the path safety level; and updating the track according to the target information output by the sensing system in real time and the navigation information output by the navigation system. The method has the advantages that the dynamic water depth factor and the water surface obstacle factor which threaten the navigation safety under the interference conditions of ocean currents, waves and the like in the actual ocean environment are fully considered, the defect that only discrete angle search can be carried out when the traditional A-star algorithm uses a rasterized map is overcome, and the safety and the practicability of the planned track can be effectively improved.
2. The unmanned ship trajectory planning method takes potential risks caused by sea waves, ocean current interference and dynamic obstacles into consideration in the calculation process, and can effectively balance the threats of underwater obstacles and water surface obstacles and the path length. Meanwhile, the planning result generated by the method is safer and more reasonable, and the planned global path turning points are fewer and smoother. If sea condition factors are not considered, the traditional path planning method is adopted for solving, and the situation that underwater obstacles are touched can occur when the water depth risk degree is high; when navigating along the global path, the collision with the water surface barrier occurs because the interference influence such as dynamic change of water depth and ocean current is ignored. The unmanned ship global path planning method and the unmanned ship global path planning system can effectively solve the problems and effectively improve the safety and the practicability of the unmanned ship global path planning.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the accompanying drawings.
A dynamic safe track planning method of an unmanned ship considering dynamic water depth comprises the following steps:
step 1, setting parameters related to a planning task of the unmanned ship.
The task-related parameters in this step include: the starting point and end point position, the starting time, the unmanned ship size and the operation performance parameters comprise, but are not limited to, unmanned ship attitude statistics, average draught, minimum turning radius, maximum speed, starting point position, end point position, set task starting time, set sailing speed and the like under various sea conditions.
And 2, constructing a global environment model according to the parameters in the step 1.
And 2.1, extracting the preprocessed vector electronic chart from the chart library according to the coordinates of the starting point position and the end point position of the unmanned ship navigation in the step 1, and extracting electronic chart attribute data, navigation sea area chart water depth data, ocean current data, obstacle data and chart positioning error from the vector chart, and performing corresponding format conversion and attribute data processing.
Wherein, the obstacle data comprises but is not limited to land area, navigation mark, bridge pier, no-navigation area, dry out reef, seabed area, etc.; and extracting a water depth point image layer, a seawater coverage area image layer, a ocean current image layer and a chart quality image layer.
And 2.2, calculating the minimum safe water depth, and determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart.
Through establishing a hydrodynamic model or a pool experiment, analyzing and calculating the sailing attitude change characteristics of the unmanned ship under different hydrodynamic conditions, and estimating the sailing attitude change in the actual marine environment, wherein hydrodynamic parameters specifically include but are not limited to a maximum downward heave value, a maximum pitch angle, average draught and a minimum turning radius.
According to
The minimum safe water depth is calculated,
wherein S isminAt the minimum safe water depth, zmaxThe maximum amplitude of downward heave motion generated when the unmanned ship sails in irregular waves at different sailing speeds; l is a coxswain of the unmanned boat; thetamaxThe maximum amplitude value of pitching motion generated when the unmanned ship sails in irregular waves under different working conditions;
the average draught of the unmanned boat; e.g. of the typeencThe water depth value error of the electronic chart is obtained.
And comprehensively considering the hydrodynamic characteristic parameters and the size of the electronic chart to determine the rasterization resolution of the electronic chart. Determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart: according to the minimum turning radius RminSea chart positioning error epThe resolution of the grid is calculated by the unmanned boat captain L and the range of the electronic chart, and the size of the grid is selected so that the number of the grids is not more than 108While the grid size should not be less than 3L + Rmin+epNot more than 10L +4Rmin+2ep。
And 2.3, interpolating the water depth by adopting an interpolation algorithm according to the navigation sea chart water depth data and the obstacle data obtained in the step 2.1 and the resolution of the grid obtained in the step 2.2 to obtain the rasterized predicted water depth.
The interpolation algorithm can adopt a spline function interpolation algorithm containing obstacles, the obstacle data obtained in the step 2.1 is used as an obstacle input element, the water depth data is used as interpolation input data, a smoothing coefficient in the spline interpolation function is set, and the sparse water depth point is interpolated to obtain the rasterized predicted water depth.
And 2.4, acquiring hydrological, meteorological and tidal information of a task water area through forecast data according to the set task starting time and the set navigation speed in the step 1.
And 2.5, processing the hydrological, meteorological and tidal information of the task water area obtained in the step 2.4 according to the resolution of the grid obtained in the step 2.2, and obtaining dynamic water depth distribution and ocean current distribution by utilizing an interpolation method on the basis of the step 2.3.
And (4) carrying out interpolation processing on the tide level data of the tide table by selecting cubic spline interpolation to obtain a change function of the tide level along with time. The instantaneous water depth of the grid at a certain moment can be obtained by performing superposition calculation on the static water depth and the sea level of the grid points. The instantaneous water depth is:
S(x,y,t)＝D(x,y)+T(x,y,t)+Δ(x,y,t)
wherein S (x, y, t) is the instantaneous water depth value of the (x, y) grid at the moment t; d (x, y) is the static water depth value of the grid point, and T (x, y, T) is the tide level value of the grid time T; Δ (x, y, T) is the water depth change due to other factors affecting the water level change at time T of the grid point, and the sum of T (x, y, T) and Δ (x, y, T) constitutes the dynamic water depth term in the instantaneous water depth.
And 2.6, constructing a global environment model according to the minimum safe water depth calculated in the step 2.2 and the dynamic water depth distribution and ocean current distribution obtained in the step 2.5.
And 3, performing global path planning by utilizing an LT-D Lite algorithm.
Wherein g (N)i) Is the ith node NiActual distance cost to the starting node S; h (N)i) Is node NiAn estimated distance cost to the target node G;
the sum of the water depth risks from the starting node to the current node; alpha is a parameter for controlling the influence of the water depth risk and is set according to the route planning target preference;
the sum of the water surface barrier risk degrees from the starting node to the current node; beta is a parameter for controlling the risk influence of the water surface barrier and is set according to the route planning target preference degree.
And 4, establishing a local situation map based on the speed position and the speed information of the unmanned ship according to the dynamic target information input by the sensing system of the unmanned ship.
And 4.1, establishing a local situation map according to the global path planning obtained in the step 3 and target information input by a perception system of the unmanned ship.
The specific implementation manner of the step is as follows: and determining an updated situation area according to the detection range of the sensor, and forming a local search space by grids in the area.
And 4.2, predicting the track of the dynamic obstacle according to the target information input by the perception system of the unmanned ship.
The specific implementation manner of the step is as follows: and predicting the motion trail of the dynamic target according to the target speed information provided by the sensing system to obtain the grid cost value which changes along with time in the local area. And if the same grid has collision risks at the same time, setting the collision cost value of the grid at the moment to be infinite.
And 4.3, judging whether collision risks exist during navigation along the current path or not according to the predicted track in the step 4.2, if so, performing the step 4.4, and otherwise, performing the step 5.
Grid water surface collision risk:
wherein d is a node NiNode O to obstaclejEuclidean distance, a is a danger coefficient corresponding to different types of obstacles, vNiIs a grid NiVelocity c of medium sea currentNiIs a coefficient of the direction, and is,
is the direction of the sea current relative to true north, phigEpsilon [0,2 pi) ] as node NiAnd node OjAnd when k obstacle grids are arranged near the navigable node relative to the azimuth angle of true north, selecting the maximum value as the risk degree of the water surface obstacle of the node: r isS(Ni)＝max{rS[Ni,1],rS[Ni,2],...,rS[Ni,k]}。
And 4.4, starting re-planning, updating the grids in the area according to the predicted track information and the dynamic water depth, generating a local path, and returning to the step 4.1 to update the local situation map.
And 5, judging whether the current position of the unmanned ship is a terminal point, finishing planning if the current position of the unmanned ship reaches the terminal point, and repeating the step 4 if the current position of the unmanned ship does not reach the terminal point until the current position of the unmanned ship reaches the terminal point.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (10)
1. A dynamic safe track planning method for an unmanned ship considering dynamic water depth is characterized by comprising the following steps: the method comprises the following steps:
step 1, setting parameters related to a planning task of the unmanned ship;
step 2, constructing a global environment model according to the parameters in the step 1;
step 3, performing global path planning by utilizing an LT-D Lite algorithm;
step 4, establishing a local situation map based on the speed position and the speed information of the unmanned ship according to the dynamic target information input by the sensing system of the unmanned ship;
and 5, judging whether the current position of the unmanned ship is a terminal point, finishing planning if the current position of the unmanned ship reaches the terminal point, and repeating the step 4 if the current position of the unmanned ship does not reach the terminal point until the current position of the unmanned ship reaches the terminal point.
2. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 1, characterized in that: the specific implementation method of the step 1 comprises the following steps: the task-related parameters include: the unmanned ship has the advantages of size, draught, operation performance, starting point position, end point position, set task starting time and sailing speed.
3. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 1 or 2, characterized in that: the step 2 comprises the following steps:
step 2.1, extracting a preprocessed vector electronic chart from a chart library according to coordinates of a starting point position and an end point position of the unmanned ship navigation in the step 1, and extracting electronic chart attribute data, navigation sea area chart water depth data, ocean current data, obstacle data and chart positioning errors from the vector chart and performing corresponding format conversion and attribute data processing;
2.2, calculating the minimum safe water depth, and determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart;
step 2.3, according to the navigation sea chart water depth data and the obstacle data obtained in the step 2.1 and the resolution of the grid obtained in the step 2.2, interpolating the water depth by adopting an interpolation algorithm to obtain the rasterized predicted water depth;
step 2.4, acquiring hydrological, meteorological and tidal information of a task water area through forecast data according to the set task starting time and the set navigation speed in the step 1;
step 2.5, according to the resolution of the grid obtained in the step 2.2, processing the hydrological, meteorological and tidal information of the task water area obtained in the step 2.4, and obtaining dynamic water depth distribution and ocean current distribution by utilizing an interpolation method on the basis of the step 2.3;
and 2.6, constructing a global environment model according to the minimum safe water depth calculated in the step 2.2 and the dynamic water depth distribution and ocean current distribution obtained in the step 2.5.
4. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 3, characterized in that: the specific implementation method of the step 2.2 is as follows: through establishing hydrodynamic force model or pond experiment, the navigation attitude change characteristics of unmanned ship under different hydrodynamic force conditions are analyzed and calculated, and the navigation attitude change in the actual marine environment is estimated:
according to
The minimum safe water depth is calculated,
wherein S isminAt minimum safe water depth, zmaxThe maximum amplitude of downward heave motion generated when the unmanned ship sails in irregular waves at different sailing speeds; l is a coxswain of the unmanned boat; thetamaxIs an unmanned boatThe maximum value of the amplitude of pitching motion generated by navigation in irregular waves under different working conditions;
the average draught of the unmanned boat; e.g. of the typeencThe water depth value error of the electronic chart is obtained;
determining the resolution of the grid according to the hydrodynamic characteristic parameters of the unmanned ship and the size of the chart: according to the minimum turning radius RminSea chart positioning error epThe resolution of the grid is calculated by the unmanned boat captain L and the range of the electronic chart, and the size of the grid is selected so that the number of the grids is not more than 108While the grid size should not be less than 3L + Rmin+epNot more than 10L +4Rmin+2ep。
5. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 3, characterized in that: the specific implementation method of the step 2.3 is as follows: and (3) adopting a spline function interpolation algorithm containing obstacles, taking the obstacle data obtained in the step (2.1) as an obstacle input element, taking the water depth data as interpolation input data, setting a smoothing coefficient in a spline interpolation function, and interpolating the sparse water depth points to obtain the rasterized predicted water depth.
6. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 3, characterized in that: the specific implementation method of the step 2.5 is as follows: the instantaneous water depth is:
S(x,y,t)＝D(x,y)+T(x,y,t)+Δ(x,y,t)
wherein S (x, y, t) is the instantaneous water depth value of the (x, y) grid at the moment t; d (x, y) is the static water depth value of the grid point, and T (x, y, T) is the tide value of the grid time T; Δ (x, y, T) is the water depth change due to other factors affecting the water level change at time T of the grid point, and the sum of T (x, y, T) and Δ (x, y, T) constitutes the dynamic water depth term in the instantaneous water depth.
7. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 1, characterized in that: the specific calculation method of the step 3 comprises the following steps:
wherein g (N)i) Is the ith node NiActual distance cost to the starting node S; h (N)i) Is node NiAn estimated distance cost to the target node G;
the sum of the water depth risks from the starting node to the current node; alpha is a parameter for controlling the influence of the water depth risk and is set according to the route planning target preference;
the sum of the water surface barrier risk degrees from the starting node to the current node; beta is a parameter for controlling the risk influence of the water surface barrier and is set according to the route planning target preference degree.
8. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 1, characterized in that: the step 4 comprises the following steps:
step 4.1, establishing a local situation map according to the global path planning obtained in the step 3 and target information input by a perception system of the unmanned ship;
step 4.2, predicting the track of the dynamic barrier according to target information input by a perception system of the unmanned ship;
4.3, judging whether collision risks exist during navigation along the current path or not according to the predicted track in the step 4.2, if so, performing the step 4.4, and if not, performing the step 5;
and 4.4, starting re-planning, updating the grids in the area according to the predicted track information and the dynamic water depth, generating a local path, and returning to the step 4.1 to update the local situation map.
9. The unmanned ship dynamic safety trajectory planning method considering dynamic water depth according to claim 8, characterized in that: and 4.1, the sensing system comprises a navigation radar, a laser radar, an AIS, a panoramic camera and a depth finder.
10. The unmanned ship dynamic safe track planning method considering dynamic water depth according to claim 8, characterized in that: the specific implementation method of the step 4.3 is as follows: grid water surface collision risk:
wherein d is a node NiNode O to obstaclejEuclidean distance, a is a danger coefficient corresponding to different types of obstacles, vNiIs a grid NiVelocity c of medium sea currentNiIs a coefficient of the direction, and is,
φce [0,2 π) is the direction of the ocean current relative to true north, φgEpsilon [0,2 pi) ] as node NiAnd node OjWhen k obstacle grids are arranged near the navigable node relative to the azimuth angle of true north, the maximum value is selected as the risk degree of the water surface obstacle of the node:
rS(Ni)＝max{rS[Ni,1],rS[Ni,2],...,rS[Ni,k]}。
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| CN117234219A (en) * | 2023-11-14 | 2023-12-15 | 中国船舶集团有限公司第七一九研究所 | Offshore cluster perception task track design method and computer readable medium |
| CN118050728A (en) * | 2024-04-16 | 2024-05-17 | 中国水利水电第十四工程局有限公司 | Target acquisition method and system for channel safety monitoring |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117234219A (en) * | 2023-11-14 | 2023-12-15 | 中国船舶集团有限公司第七一九研究所 | Offshore cluster perception task track design method and computer readable medium |
| CN117234219B (en) * | 2023-11-14 | 2024-02-02 | 中国船舶集团有限公司第七一九研究所 | Offshore cluster perception task track design method and computer readable medium |
| CN118050728A (en) * | 2024-04-16 | 2024-05-17 | 中国水利水电第十四工程局有限公司 | Target acquisition method and system for channel safety monitoring |
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