CN108872978B - Overwater ship path planning method based on high-frequency ground wave radar ocean current data - Google Patents
Overwater ship path planning method based on high-frequency ground wave radar ocean current data Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S13/88—Radar or analogous systems specially adapted for specific applications
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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Abstract
The invention provides a method for planning a marine ship path based on ocean current data of a high-frequency ground wave radar, which can solve the problem of how to dynamically plan a most fuel-saving driving path for a marine water ship by using an ocean current field detected by the high-frequency ground wave radar. The invention relates to a method for planning a path of an overwater ship based on ocean current data of a high-frequency ground wave radar, which is characterized in that after the ship starts a voyage, the current position and the terminal position are reported to a high-frequency ground wave radar central station of the local sea area at a fixed frequency; and after receiving the ship position and the end point position, the radar central station combines real-time ocean current data to plan a path according to a genetic algorithm, finds out an optimal path and sends the optimal path to the ship.
Description
Technical Field
The invention belongs to the field of data application of high-frequency ground wave radar detection signals, and particularly relates to a method for planning a water ship path based on high-frequency ground wave radar ocean current data.
Background
The high-frequency ground wave radar is a new ocean remote sensing device and has the advantages of large area, all weather, low cost and the like. The information of the ocean surface flow field, the wave field and the wind field of tens of thousands of square kilometers can be inverted by short-time detection. The maximum detection distance can reach 400 kilometers and even more. At present, high-frequency ground wave radars reach the level of business operation at home and abroad, and the detection of wind, wave and ocean current makes a major breakthrough, but how to secondarily utilize the detected large-range wind, wave and current information further provides scientific research, production and life and military meteorological services, and still has huge exploration space.
When a ship sails on the sea surface, the ship is influenced by ocean currents and is influenced by ocean current resistance or driving force during the advancing process (particularly, when the target of the ship is smaller, the speed is lower, the influence of the ocean currents is larger, and the ship is typically a small fishing boat). In order to reduce the resistance of the ocean current (countercurrent) to the maximum extent and utilize the power of the ocean current (concurrent) to the maximum extent and save fuel, a reasonable optimal path can be provided according to the ocean current characteristics in the navigation region. The high-frequency ground wave radar system can detect and invert ocean current data in a radar signal coverage range in real time, and under the normal meteorological condition, a ship is mainly influenced by superposition of ocean currents and self power in the process of sailing. The ocean current and the power of the ship are used as influence factors, a genetic algorithm is utilized, a global optimal solution with the minimum energy consumption is planned, and the dynamic path planning of the sea surface ship can be realized. At present, no technology for carrying out real-time dynamic path planning on a sea surface ship by utilizing a high-frequency ground wave radar system to invert ocean current data exists.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for planning the path of an overwater ship based on ocean current data of a high-frequency ground wave radar, which can solve the problem of how to dynamically plan the most fuel-saving driving path for an offshore water ship by utilizing an ocean current field detected by the high-frequency ground wave radar.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for planning the route of a ship on water based on ocean current data of a high-frequency ground wave radar comprises the steps that after the ship starts a voyage, the current position and the terminal position are reported to a central station of the high-frequency ground wave radar in the sea area at a fixed frequency; and after receiving the ship position and the end point position, the radar central station combines real-time ocean current data to plan a path according to a genetic algorithm, finds out an optimal path and sends the optimal path to the ship.
Further, path planning is carried out according to a genetic algorithm, and an optimal path is found out, and the method specifically comprises the following steps:
step one, determining a path coding scheme: setting a linear distance S from the current position of the ship to an end point when each position is reported, equally dividing the distance d into units, wherein d is an integer larger than zero, dividing S into ROUNDUP (S/d) sections, defining the total number of the sections as m, setting the direction from a starting point to the end point as the positive direction of an x axis, setting the direction perpendicular to the x direction and passing through the starting point as a y axis, and setting the positive direction of the y axis as the direction pointed by the anticlockwise rotation of the x axis by 90 degrees, wherein a path from the starting point to the end point is formed by a series of key nodes, and connecting all the key nodes by straight lines from the starting point to form a complete path;
step two, population initialization: randomly generating n paths as an algorithm initial population according to the path coding scheme of the first step;
step three, determining a fitness function: calculating the energy consumption value of the connecting line between every two key nodes aiming at each path, and defining the energy consumption value as EiI is more than or equal to 1 and less than or equal to m, then the sum is the energy consumption of the ship for running a whole path and is defined as WkK is more than or equal to 1 and less than or equal to n; the fitness function for energy consumption is expressed as:
wherein, CiFor the speed, i.e. the resultant speed, of the ship, which is externally represented between two key nodesiIs the length of the connection of the two key nodes,
Vcur_iis the speed of the ocean current at the ith key node, and beta is Vcur_iThe included angle of the direction of the key node and the connecting line of the ith section key node is formed; vi+1The hydrostatic speed of the ship at the (i +1) th key node is represented, K is a constant greater than zero, and t is more than or equal to 0 and less than or equal to (l)i/Ci)；
Step four, arranging the population from small to large according to a fitness function, and selecting the first n R with the best fitnesscsTrace, rest n x (1-R)cs) The path with poor fitness is eliminated, and the path with the best fitness n (1-R) is selectedcs) Stripe path replication to replace eliminated nX (1-R)cs) Wherein R iscsTo select a probability;
step five, arranging the population from small to large according to a fitness function and according to the cross probability RexThe first n RexTwo pathsTwo are used as cross objects;
step six, arranging the population from small to large according to a fitness function, and arranging the population according to a variation probability RvaN is R isvaCarrying out mutation operation on the path;
and step seven, repeating the steps four to six, stopping circulation after the preset times X are executed, and selecting a path with the minimum fitness from the current population as an optimal path.
Further, after the central station calculates the optimal path according to a genetic algorithm, three continuous key nodes are sent to the ship to serve as a planned path.
Further, when the linear distance between the current position of the ship and the terminal point is less than or equal to the linear equal-division unit distance between the current position of the ship and the terminal point, planning is not carried out any more, and the terminal point position is directly sent to the ship.
Further, after the ship sends the current position and the terminal position to the radar central station, the path planning calculation of the radar central station needs a certain time, and the navigation path cannot be sent to the ship immediately. At the moment, the appointed ship continues to run according to the last planned path until the optimal path is received; if the route is not planned, or the last node of the route is reached, the vehicle directly travels straight towards the destination.
Further, the key node is: and drawing a vertical line on each unit equal division point which does not comprise a starting point and a terminal point, and randomly selecting one point on the vertical line as a key node.
Furthermore, the angle between the connecting line of the previous key node and the next key node and the positive direction of the x-axis is less than alpha, wherein alpha is more than 0, and the connecting line from the penultimate key node to the terminal point does not need to satisfy the constraint.
Further, α is 60 °.
Furthermore, all the equal division points in the straight line direction from the starting point to the end point are connected and are placed into the algorithm initial population as a default path.
Further, the nearest neighbor interpolation method is adopted to complete the interpolation of the ocean current velocity on the key node.
Furthermore, the radar central station inverts one ocean current data every ten minutes, and the inverted latest one ocean current data is used as real-time ocean current data.
Further, when the fitness function is arranged from small to large, the low fitness and the non-repeated fitness are preferentially arranged in front.
Compared with the prior art, the invention has the following advantages:
1. the high-frequency ground wave radar system is applied to dynamic path planning of marine ships for the first time, and the application field of the ground wave radar system is expanded.
2. The method for calculating the fitness function of the ship is provided for the first time by applying the genetic algorithm to the dynamic path planning of the ship at sea.
3. The ocean current data is used for planning the path of the marine ship based on energy consumption, and the method has great significance for saving fuel expenditure of small marine civil ships.
Drawings
FIG. 1 is a flow chart of a flight path planning;
FIG. 2 is a geometric plot of vessel speed under the influence of ocean currents;
FIG. 3, FIG. 4, FIG. 5 and FIG. 6 are schematic diagrams of optimal paths at different stages when the optimal paths are calculated by the genetic algorithm, respectively.
Detailed Description
The technical scheme of the invention is further specifically explained in the following by combining the attached drawings.
As shown in fig. 1, the method for dynamically planning paths of water vessels by using high-frequency ground wave radar to invert flow field data (the radar-ship communication and the geographic position service in this embodiment both use a domestic beidou system) includes the following steps:
the method comprises the following steps: after the ship starts a journey, the current position and the terminal position are reported to a high-frequency ground wave radar central station of the sea area at a fixed frequency.
Step two: and after the radar central station receives the ship position and the terminal position, path planning is carried out by combining real-time ocean current data. The radar central station can invert one ocean current data every ten minutes, and the latest ocean current data in the inverted time is taken as the real-time ocean current in actual operation in consideration of the continuity of the ocean current in time. The planning goal is to minimize the energy (fuel) consumed from the current location to the end point, and the planning principle is to use a genetic algorithm to find a globally optimal solution.
Step three, the genetic algorithm specific planning method comprises the following steps:
1. path coding scheme: assuming that the linear distance S from the current position of the ship to the terminal point during each position reporting is equal to the distance d as an equal division unit (d is an integer larger than zero), S is divided into ROUNDUP (S/d) sections, and the total number of the sections is defined as m (the ROUNDUP is rounded up by convention). The direction from the starting point to the end point is defined as the positive x-axis direction, the direction perpendicular to the x-axis and passing through the starting point is defined as the y-axis, and the positive y-axis direction is the direction in which the x-axis rotates 90 degrees counterclockwise. A path from the starting point to the end point is composed of a series of key nodes, and from the starting point, all the key nodes are connected by straight lines to form a complete path. We specify that the projection of the path on the x-axis is strictly incremental, i.e. not circuitous, which is consistent with our most everyday practice of sailing.
The selection rule of the key nodes is as follows: and (4) making a vertical line on each unit equal division point which does not comprise a starting point and an end point (the starting point and the end point are defaulted to be key nodes), and randomly selecting one point on the vertical line as a key node. For better reality and to ensure better convergence of the genetic algorithm, the connecting line of the previous key node and the next key node is defined to form an angle smaller than alpha (alpha is 60 degrees in general) with the positive direction of the x-axis. Note that α >0, and the penultimate key node to endpoint connection need not satisfy the above constraints.
2. Population initialization: the basic idea of the genetic algorithm is to perform multiple times of best-out iteration from an initialized population by genetic operator operations such as selection, crossing, mutation and the like by taking a fitness function as consideration, and finally taking the optimal solution in the population as a global optimal solution. The initialization population randomly generates n paths according to a path coding scheme. Considering that the straight line from the starting point to the end point is the optimal solution under most conditions, all the bisectors in the straight line direction from the starting point to the end point can be connected (the bisectors are the key nodes in this case), and the bisectors can be placed into the population as a default path, so that the algorithm can be converged more quickly.
3. Determination of fitness function: the goal of path planning in the method is to find the minimum energy consumption value from the current position to the end point, so that each path in the population needs to be evaluated, and the smaller the fitness function value is, the smaller the energy consumption is. Calculating the energy consumption value of the connecting line between every two key nodes aiming at each path, and defining the energy consumption value as EiI is more than or equal to 1 and less than or equal to m. Then accumulating, and defining as W the energy consumption of the ship for running a whole pathkAnd k is more than or equal to 1 and less than or equal to n. Expressed as:
assuming that the ship is between two key nodes, the externally presented speed (resultant speed) is constant, which is defined as CiThe length of the connecting line of the two key nodes is liThe time consumed by the ship between two key nodes is li/Ci. Defining the hydrostatic speed of the ship as VshipDefining the ocean current velocity as VcurThe combined speed C of any point on the connection line of any two key nodesiFrom VshipAnd VcurAnd the three-dimensional image is formed by superposing according to a triangle rule. (with reference to fig. 2) the function of the vessel hydrostatic speed versus time can be defined as a linear function, given that the distance between any two key nodes is not long, expressed as:
Vship＝(Vi+1-Vi)*Ci/li*t+Vi
(Virepresenting the hydrostatic velocity, V, of the ship at the ith key nodei+1The hydrostatic speed of the ship at the (i +1) th key node is represented, and t is more than or equal to 0 and less than or equal to (l)i/Ci)。)
Energy E available according to common physical knowledgeiIs the integral of power over time, expressed as:
p (t) is a function of power versus time, expressed as:
P(t)＝K[(Vi+1-Vi)*Ci/li*t+Vi]2(K is a constant greater than zero)
Further, the fitness function for energy consumption may be expressed as:
wherein V is a synthetic relationship between the current velocity of the ocean current and the hydrostatic velocity of the shipiThe calculation method comprises the following steps:
Vcur_iis the speed of the ocean current at the ith key node, and beta is Vcur_iThe included angle between the direction of the key node and the connecting line of the ith section of the key node is formed. According to the idea of genetic algorithm, the distribution of key nodes is random. However, the flow field data inverted by the high-frequency ground wave radar are distributed according to a certain rule, and it cannot be guaranteed that the ocean current velocity value can be directly read on the key node. Therefore, the velocity V of ocean current of the key nodecur_iInterpolation is also required for acquisition. The method directly adopts a nearest neighbor interpolation method to complete the interpolation of the ocean current velocity on the key node.
4. Genetic operator definition
Selecting operation: and sequencing the populations according to a fitness function, and arranging the populations from small to large according to the fitness. Note that here the ordering preferentially ranks the solutions with small fitness and without repetition in front, with the purpose of preventing faster falling into locally optimal solutions. Definition of RcsTo select a probability. Selecting the first n R with best individual fitnesscsTrace, rest n x (1-R)cs) The paths with poor fitness are eliminated. And the best n (1-R) of the fitness is addedcs) Stripe path replication to replace eliminated nX (1-R)cs) The path of (2).
And (3) cross operation: the population is sorted first, and the sorting rule is consistent with the selection operation. According to a certain cross probability RexAdding the first n RexThe strip paths are in opposite crossing direction. On both paths, p is randomly choseni，pj]The segments are taken as cross-segments, exchanging [ p ]i，pj]The longitude and latitude coordinates of the key nodes in between (i is more than or equal to 1 and less than or equal to m, j is more than or equal to i and less than or equal to m +1, and the i and j are the serial numbers of the key nodes).
Mutation operation: the population is sorted first, and the sorting rule is consistent with the selection operation. According to a certain mutation probability Rva，n*RvaAnd performing mutation operation on the path. Specifically, a key node p is randomly selected on a selected pathi(i ≠ 1, i ≠ (m +1)), and piThe coordinates are chosen again randomly, note that the selection rule is still constrained by the path key node definition.
After the path population is initialized, the loop iteration is carried out according to the selection-cross-variation process, each iteration can generate a path optimal solution with the minimum fitness, and the iteration is convergent. According to the actual situation, the iteration number is set to be X. After X iterations, a loop is skipped, and the final optimal solution can be regarded as the path with the minimum energy consumption.
Step four: after the ship sends the current position and the terminal point to the radar central station, a certain time (related to the number of the population and the number of the loop iteration) is needed for the path planning calculation of the central station, so that the navigation path cannot be sent to the ship immediately. At this time, the appointed ship continues to run according to the last planned route. If the route is not planned, or the last node of the route is reached, the vehicle directly travels straight towards the destination. The planned path here refers to the location of three key nodes in succession, which the central station sends out to the vessel.
Step five: after the central station calculates the optimal path according to a genetic algorithm, three continuous key nodes are sent to the ship through the Beidou in combination with the current position of the ship and serve as a planned path. And when the linear distance between the current position of the ship and the end point is less than or equal to the distance d between the equal dividing units, planning is not carried out any more, and the end point position is directly sent to the ship. So far, the whole path planning is finished under the condition that the ship correctly runs according to the navigation path.
The following examples are given by way of illustration:
the ship sends the current position and the target position to the ground wave radar central station, and the current position coordinates are as follows: a (117.8452,23.7270), the target position coordinates are: b (117.3227, 22.6688). After the position information is sent to the central station, the ship needs to inquire the current path planning information and drive towards the next key node. And if the planned path is reached to the last node of the current planned path or is not inquired, directly driving towards the destination point B.
After receiving the positions of A and B, the central station reads the current real-time ocean current file LoDo _160730_0920_7.865M _ Current. xml, and then starts to calculate according to the positions and the ocean current distribution. The ocean Current file is generated by real-time inversion of a central station, LODO represents a ground wave radar station area, here represents an Taiwan strait sea area near the east dragon sea of Fujian, 160730_0920 represents data about 30, 9 and 20 points in 16 years, 7 months and 30 months, and Current represents that the data type is ocean Current.
The distance from A to B is 129.225km, 4km is an equal division unit, the connecting line from A to B can be divided into 33 segments, and therefore the total number of nodes of the finally formed path is 34 nodes. The population number of the genetic algorithm is set to 3000, that is, 3000 paths are randomly generated (a larger population number is selected, mainly to realize a higher convergence speed, and since the accuracy requirement of the ship navigation route is not high, a smaller number of iterations can be selected). Connecting all the equal division units of the line from A to B as a default straight line path, and marking as S1And putting into a population. According to the formula:
find out, the straight path S1Energy consumption of W1＝349.427。
The calculated W is only used as a reference value for path fitness comparison, is not real energy consumption, and can directly take the constant K to be 1.
The number of iterations is set to 200, the selection probability is set to 0.1, the crossover probability is set to 0.8, and the mutation probability is set to 0.5.
After 1 iteration, the result is shown in fig. 3, and the straight line is still the path with the minimum energy consumption between two points.
After 20 iterations, the result is shown in fig. 4, where the calculated path is not already a straight line and the energy consumption is 335.103.
After 100 iterations, the result is shown in fig. 5, where the path is smoother and the energy consumption is W-283.172.
After 200 iterations, the result is shown in fig. 6, where the path has changed by a small amount, but the power consumption is still 283.172, which is not reduced.
After 200 iterations, a loop is taken out, the path with the minimum fitness is taken as the optimal solution, and the process takes 227 seconds in total. The path format is a set of a series of key nodes, represented as:
(,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) (,) etc.) total 34 nodes, the first being the current position of the ship: a (117.8452,23.7270), the last point being the target location: b (117.3227, 22.6688). At the moment, the central station takes three continuous position points behind the point A as path information and sends the path information to the ship through the Beidou.
And after the ship receives the path information containing continuous 3 coordinate points sent by the central station, updating the path planning information. And adjusting the course and driving towards the first coordinate point of the planned path.
And circulating the process in such a way until the next planned coordinate point is the terminal point B, and ending the whole flight path planning process.
Claims (7)
1. A method for planning a path of a ship on water based on ocean current data of a high-frequency ground wave radar is characterized in that the current position of the ship is reported to a central station of the high-frequency ground wave radar in the sea area after the ship starts a voyage; after receiving the current position of the ship, the radar central station combines real-time ocean current data and the end position of the ship, plans a path according to a genetic algorithm, finds out an optimal path and sends the optimal path to the ship; the path planning is performed according to a genetic algorithm, and when an optimal path is found, a path coding scheme is firstly determined, specifically as follows:
setting the linear distance from the current position of the ship to the terminal as S when the current position is reported every time, equally dividing the distance d into units, dividing the S into m sections, setting the direction from the starting point to the terminal as the positive direction of an x-axis, setting the direction perpendicular to the x-direction and passing through the starting point as a y-axis, setting the positive direction of the y-axis as the direction which the x-axis rotates anticlockwise by 90 degrees, wherein a path from the starting point to the terminal is formed by a series of key nodes, and connecting all the key nodes by straight lines from the starting point to form a complete path;
calculating an energy consumption value of a connecting line between every two key nodes according to each path, then accumulating, and obtaining the energy consumption of the ship running a whole path, wherein the fitness function of the energy consumption is expressed as:
wherein, CiFor the speed, i.e. the resultant speed, of the ship, which is externally represented between two key nodesiIs the length of the connection of the two key nodes,
Vcur_iis the speed of ocean current at the ith key node, and beta is Vcur_iThe included angle of the direction of the key node and the connecting line of the ith section key node is formed; vi+1The hydrostatic speed of the ship at the (i +1) th key node is represented, K is a constant greater than zero, and t is more than or equal to 0 and less than or equal to (l)i/Ci)。
2. The method for planning the path of the water ship based on the ocean current data of the high-frequency ground wave radar as claimed in claim 1, wherein the path in the initial population is iterated for a plurality of times through the operations of selection, intersection and mutation genetic operators by taking the fitness function as a consideration, and finally the optimal solution is used as a global optimal solution; during selection operation, the initial population is arranged from small to large according to a fitness function, and the first n R with the best fitness is selectedcsTrace, rest n x (1-R)cs) The path with poor fitness is eliminated, and the path with the best fitness n (1-R) is selectedcs) Stripe path replication to replace obsolete n x (1-R)cs) Wherein R iscsTo select the probability, n is the random generation of n paths according to a path coding scheme.
3. The method for planning the path of the ship on the water based on the ocean current data of the high-frequency ground wave radar as claimed in claim 1 or 2, wherein after the central station calculates the optimal path according to a genetic algorithm, three continuous key nodes are sent to the ship to serve as the planned path; and when the linear distance between the current position of the ship and the terminal is less than or equal to the linear equal-division unit distance between the current position of the ship and the terminal, the planning is not carried out any more, and the terminal position is directly sent to the ship.
4. The method for planning the path of the ship on the water based on the ocean current data of the high-frequency ground wave radar as claimed in claim 1 or 2, wherein after the ship sends the current position to the radar central station, the path planning calculation of the radar central station needs a certain time, and a navigation path cannot be sent to the ship immediately; at the moment, the appointed ship continues to run according to the last planned path until the optimal path is received; if the route is not planned, or the last node of the route is reached, the vehicle directly travels straight towards the destination.
5. The method for planning the path of the ship on water based on the ocean current data of the high-frequency ground wave radar as claimed in claim 2, wherein the key nodes are as follows: making a vertical line on each unit equal division point which does not comprise a starting point and a terminal point, and randomly selecting a point on the vertical line as a key node; the angle between the connecting line of the previous key node and the next key node and the positive direction of the x axis is less than alpha, wherein alpha is greater than 0, and the connecting line from the penultimate key node to the terminal point does not need to meet the constraint.
6. The method for planning the path of the ship on water based on the ocean current data of the high-frequency ground wave radar as claimed in claim 1 or 2, wherein all the bisectors in the direction from the starting point to the ending point are connected and are placed into the initial population of the algorithm as a default path.
7. The method for planning the path of the ship on water based on the high-frequency ground wave radar ocean current data as claimed in claim 1 or 2, wherein the radar central station inverts one field of ocean current data every ten minutes, and the inverted latest one field of ocean current data is used as the real-time ocean current data.
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