CN109782779B  AUV path planning method in ocean current environment based on population hyperheuristic algorithm  Google Patents
AUV path planning method in ocean current environment based on population hyperheuristic algorithm Download PDFInfo
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Abstract
The invention discloses an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm, and belongs to the field of underwater robots. Firstly, initializing a population, and then obtaining initial comprehensive cost values of all individuals in the whole population according to a cost function; then setting a population basic operation set and a corresponding operation selection probability vector; selecting an individual in the population, operating and selecting a population basic operation corresponding to the maximum probability in the probability vector, operating the individual, repeating the population basic operation until the whole population is traversed, and finishing one iteration; adjusting operation selection probability vectors, and performing next iteration until the iteration times reach a set value; and finally, after the iteration is finished, selecting an individual with the minimum cost value, combining the starting point, the end point and the fixed control point of the B spline curve to form a group of control points, and then generating an optimal path. The method solves the problems that the path planned by the existing AUV path planning method is not smooth, is difficult to track and has high energy consumption.
Description
Technical Field
The invention belongs to the field of underwater robots, and relates to an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm.
Background
AUV, Autonomous Underwater Vehicle, also called Autonomous Underwater robot, has the advantages of large range of motion, good maneuverability, safety, intellectualization and the like, and is an important tool for completing various Underwater tasks. The traditional path planning method for the mobile robot mainly represents the environment as a discretized grid map, and searches the absolute shortest path from a starting point to an end point by adopting search algorithms such as D and A. The path planning method based on search has two problems, on one hand, when the size of a map is increased, especially when the map is expanded to a threedimensional space, the efficiency of a search algorithm is sharply reduced; on the other hand, the method results in a discrete, nonsmooth path which is difficult for the robot to track, especially for underdriven robots.
Because the torpedo type AUV only depends on the propeller at the tail to generate power, the torpedo type AUV is a typical underactuated system, and the mode that the existing mobile robot obtains a discrete path is difficult to apply to the torpedo type AUV of the underactuated system. Therefore, the AUV path planning method puts higher requirements on the smoothness of the planned path, and the path not only needs to be smooth, but also cannot exceed the maximum turning radius.
In addition, the underwater movement of the AUV is greatly influenced by ocean current information, when the difference between the movement direction of the AUV and the flow direction of the ocean current is large, the movement precision of the AUV is greatly influenced, and the AUV consumes extra energy for keeping the original route. Therefore, the path planned by the path planning method of the AUV not only has a requirement on smoothness, but also needs to follow the direction of ocean current movement as much as possible, so that the AUV has higher movement precision and more energysaving capability.
A B spline curve is used for representing an AUV motion path, the position of a control point of the AUV motion path is used as a parameter, and a path planning problem is modeled into a nonlinear optimization problem under a multiconstraint condition, so that the final path can meet various constraints. The populationbased metaheuristic algorithm has great advantages in solving the nonlinear optimization problem, such as a firefly algorithm, a particle swarm algorithm, a fish swarm algorithm and the like. Many similar algorithms have appeared in recent years, and scholars consider the algorithms to lack innovativeness but change similar ideas into a simulation scene. Therefore, a population metaheuristic algorithm trying to synthesize a population metaheuristic algorithm is generated, a metaheuristic algorithm framework is designed by managing population operation, and a simulated scene replacement algorithm with low innovation is stopped.
The' 2018105195080 patent discloses a UUV energy consumption optimizationbased remote marine path planning method that takes into account the effects of ocean circulation. Firstly, determining the actual ground navigation speed of a UUV and realtime updated ocean current information; initializing various parameters required by an optimization algorithm; randomly generating path control nodes through spatial decomposition modeling, and generating an initial path by using BSpline fitting; then entering a QPSO algorithm iteration process, calculating the average optimal position of particles corresponding to the current path, and calculating the energy consumption of the UUV corresponding to the current path; and finally, determining a final optimal solution through a corresponding optimization process, outputting a final path control node, and fitting according to the final control node to generate a path track with optimal energy consumption. According to the method, a B spline curve represents a path, a QPSO algorithm is adopted for optimizing and selecting the position of a control point of the spline curve, but the optimization function takes the UUV dynamic model into consideration and is at the cost of actual energy consumption, the calculation is complex, and the method is difficult to extend to other types of UUV.
The' 2014107663462 patent discloses an underwater vehicle path planning method based on dynamic planning. Under the condition that the future condition of ocean current is known, firstly, rasterizing an area to be planned, then, calculating time values consumed between search points by using a speed synthesis model, and planning a reachable optimal path in a twodimensional plane by using a dynamic planning algorithm by taking the time values as weights. The method searches for a path meeting ocean current information on a rasterized map, and the obtained path meets ocean current constraints but cannot meet smoothness constraints.
Therefore, aiming at the problems, the invention provides an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm.
Disclosure of Invention
The invention aims to: the AUV path planning method based on the population hyperheuristic algorithm in the ocean current environment is provided, and the problems that the path planned by the existing AUV path planning method is not smooth, is difficult to track and has high energy consumption are solved.
The technical scheme adopted by the invention is as follows:
an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm comprises the following steps:
step 1: initializing a population, and obtaining initial comprehensive cost values of all individuals in the whole population according to a cost function which integrates time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint;
step 2: setting a population basic operation set and a corresponding operation selection probability vector; selecting an individual in the population, then selecting a population basic operation corresponding to the maximum probability in the probability vector by a selection operation, using the population basic operation for the individual, and updating the comprehensive cost value of the individual; repeating the process until the whole population is traversed, and finishing one iteration; then, adjusting operation selection probability vectors, and performing next iteration until the iteration times reach a set value;
and step 3: after iteration is finished, selecting an individual with the minimum cost value, namely an optimal individual, combining a starting point, an end point and a fixed control point of the B spline curve to form a group of control points, and generating an optimal path by the group of control points.
Further, the specific steps of step 1 are:
step 1.1: setting a starting point and an end point of the AUV path, connecting the starting point and the end point, generating M equally divided points on the connecting line, drawing a vertical line on each equally divided point according to the connecting line of the starting point and the end point to obtain M vertical lines, and generating i on each vertical line_{max}A random point, thereby obtaining i_{max}The set of random points, the ith set of random points being denoted as RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2})，i∈(1,2,…,i_{max})；
Step 1.2: starting from the starting point, a fixed control point p is added in a short distance along the starting direction of the AUV_{1}Starting from the end point, a fixed control point p is added by a short distance in the opposite direction to the direction of termination of the AUV_{2}For controlling the starting and ending directions of the curved path;
step 1.3: according to step 1.1 and step 1.2, with a start point, an end point, a set of random points RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Fixed control point p_{1}And a fixed control point p_{2}As the control points of one Bspline curve, the number of the control points is n, where n is M +4, and the positions of the control points areCombining the curve order K to obtain i consisting of discrete points_{max}B spline curve, the curve equation is:
wherein the content of the first and second substances,andxaxis and Yaxis coordinates, B, of control points, respectively_{i,K}(t) is the basis function of the Kth order Bspline curve, and the control points and the basis function determine the shape of the Bspline curve;
step 1.4: from i_{max}Bspline curve to obtain i_{max}An AUV path, represented as:
wherein the orientation ψ of the AUV is:
step 1.5: will i_{max}Initializing a group of random points to obtain a population, and initializing a group of random points RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Representing an individual, i.e. a solution x_{i}Wherein, the dimension of the individual is M, and the number of the individual is i_{max}The population P is:
that is to say that the first and second electrodes,
step 1.6: calculating the initial cost value of each individual according to a cost function integrating time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint, wherein the cost function combines time and a penalty function, and the expression is as follows:
wherein, T_{cost}Is a time penalty, obtained by dividing the path distance by the average velocity,andrepresenting twodimensional points on the i +1 th and ith discrete paths,  v  is the average velocity of the AUV motion,
ρ_{cost}is the curvature cost, obtained by accumulating the approximate curvature at each discrete point,
M_{cost}is the map cost, obtained by accumulating the values of each discrete point on the map (1 for obstacles, 0 for regions),
O_{cost}is ocean current information constraint and is obtained by accumulating the product of the included angle between the path direction of each discrete point and the water flow direction and the water flow vector modulus, h is the number of discrete points on a B spline curve, theta (DEG) is an included angle solving function,representing a velocity vectorAnd water flow rateThe included angle between the two parts is included,representing the speed magnitude;
step 1.7: respectively carrying out normalization processing on the four initial cost values, wherein different cost values have different dimensions and pass through the maximum cost valueAnd minimum cost valueTo obtain a normalized cost value
Step 1.8: and weighting and summing the four normalized cost values through different weights to obtain the initial comprehensive cost values of all individuals in the whole population P:
wherein the content of the first and second substances,respectively normalized time cost, curvature cost, map cost and ocean current cost, omega_{1}，ω_{2}，ω_{3}，ω_{4}Are weights of different cost values.
Further, the specific steps of step 2 are:
step 2.1: setting a population basic operation set and a corresponding operation selection probability vector, wherein the population basic operation is o<·>Represents, solution x to input^{t}Outputting a new solution x after an iteration^{t+1}The expression is o<x^{t}>＝x^{t+1}The population basic operation is randomly moved, randomly jumped for a long distance, an individual with a following cost value smaller than the population basic operation, an individual with a minimum following cost value, an individual with the best following history or mutation, and the population basic operation set combines and represents the population basic operation as O { O_{1},o_{2},…,o_{k}And (5) each group basic operation corresponds to an operation selection probability vector which is P { P_{1},p_{2},...,p_{k}}；
Step 2.2: selecting one individual in the population P in the step 1.5, and then selecting the population basic operation o corresponding to the maximum probability in the probability vector by the selecting operation_{i}；
Step 2.3: basic operations on individuals selected in step 2.2 using population_{i}And updating the comprehensive cost value of the current individual according to the steps 1.6 to 1.8;
step 2.4: repeating steps 2.2 and 2.3 until the whole population is traversed, using the population basic operation o once for each individual_{i}Then, completing one iteration;
step 2.5: and (4) evaluating the operation condition of the iteration in the step (2.4), adjusting the operation selection probability vector, and returning to the step (2.2) for the next iteration until the iteration number reaches a set value.
Further, in the population basic operation of step 2.1:
the random shift refers to a shift solution x_{i}To itNeighborhood, the expression is: x is the number of_{i}:＝x_{i}+λ_{min}Rand u, where λ_{min}Is the step size of the centralized search,is a random number subject to uniform distribution, u is a random unit direction vector;
the random longdistance jitter is a moving solution x_{i}To the remote location, the expression is: x is the number of_{i}:＝x_{i}+λ_{max}U, where λ_{max}Is the step size of the diversity search, u is the vector in the random unit direction;
the individuals with less followup cost value than themselves mean that x is solved_{i}To all cost values less than x_{i}The solution of (2) is shifted by the expression: x is the number of_{i}:＝x_{i}+λ·rand·(x_{j}x_{i}) For all satisfy f (x)_{j})＜f(x_{i}) Solution x of_{j}X is solved_{i}All move towards it;
the individual with the minimum following cost value means that the solution x is converted into_{i}Moving to the solution with the minimum cost value, namely the optimal solution, the expression is: x is the number of_{i}:＝x_{i}+λ·rand·(x_{b}x_{i}) Wherein x is_{b}Is a solution of the optimal individual representation in the population;
the history following the best individual means that the solution x is to be solved_{i}The position of the individual with the minimum cost value, namely the individual with the best history, obtained after the individual is evolved for multiple times is moved, and the expression is as follows:wherein the content of the first and second substances,represents the solution x_{i}Historical best position of;
the mutation refers to solving x_{i}Mutation to obtain a new solution x_{i}′。
Furthermore, the evaluation of the iterative operation of step 2.4 in step 2.5 means that the centralized finger is guaranteedThe number is balanced with a diversity index, which is evaluated by the best cost value before and after iteration, i.e. in terms ofAndthe size relationship of (a); the diversity index is evaluated by the sum of the distances of all solutions and the central solution, and the formula is as follows:wherein the central solutionI.e. according to d^{t1}And d^{t}Is determined by the magnitude relationship of (a).
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the AUV path planning method based on the population hyperheuristic algorithm in the ocean current environment adopts the B spline curve to represent the AUV path, optimizes and selects the control point position of the B spline curve by using the population hyperheuristic algorithm, and further obtains the optimal path meeting the time efficiency, the environment map, the AUV body motion curvature radius and the ocean current information constraint, the obtained path is continuous and smooth and meets the ocean current information constraint, and the AUV tracking difficulty and the tracking energy consumption are reduced.
2. The B spline curve adopted by the invention can obtain a complex and smooth path by using a small number of control points, and the ocean current information constraint is defined only through the geometric relationship between the path and the ocean current information, so that the complexity of the method is reduced.
3. The invention models the path planning problem into a nonlinear optimization problem by using a cost function, and solves the problem by using the population hyperheuristic algorithm to further obtain the Bspline curve control points.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other relevant drawings can be obtained according to the drawings without inventive effort, wherein:
FIG. 1 is a flow chart of an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm;
FIG. 2 is a schematic diagram of the fixed control points in step 1.2 of the present invention;
FIG. 3 is a diagram illustrating the path planning result in an environment containing only ocean current information according to the present invention;
FIG. 4 is a diagram illustrating the results of the path planning in an environment containing obstacle and ocean current information according to the present invention;
FIG. 5 is a graph of the time and cost stability analysis of the invention running 200 times.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of embodiments of the present invention generally described herein and illustrated in the figures may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a nonexclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The AUV path planning method based on the population hyperheuristic algorithm in the ocean current environment solves the problems that the path planned by the existing AUV path planning method is not smooth, is difficult to track and has high energy consumption.
An AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm comprises the following steps:
step 1: initializing a population, and obtaining initial comprehensive cost values of all individuals in the whole population according to a cost function which integrates time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint;
step 2: setting a population basic operation set and a corresponding operation selection probability vector; selecting an individual in the population, then selecting a population basic operation corresponding to the maximum probability in the probability vector by a selection operation, using the population basic operation for the individual, and updating the comprehensive cost value of the individual; repeating the process until the whole population is traversed, and finishing one iteration; then, adjusting operation selection probability vectors, and performing next iteration until the iteration times reach a set value;
and step 3: after iteration is finished, selecting an individual with the minimum cost value, namely an optimal individual, combining a starting point, an end point and a fixed control point of the B spline curve to form a group of control points, and generating an optimal path by the group of control points.
According to the method, the AUV path is represented by the B spline curve, and a complex and smooth path can be obtained by using a small number of control points; modeling the path planning problem into a nonlinear optimization problem by using a cost function, and optimizing and selecting the control point position of the B spline by using a population hyperheuristic algorithm, wherein the population hyperheuristic algorithm integrates the advantages of various population element heuristic algorithms, has great advantages when solving the nonlinear optimization problem, and is simple in calculation and better in convergence; further, an optimal path satisfying time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint is obtained, the ocean current information constraint is defined only through the geometric relation between the path and the ocean current information, and the complexity of the method is reduced; the finally obtained path is continuous and smooth and meets the ocean current information constraint, and the tracking difficulty and the tracking path energy consumption of the robot are reduced.
The features and properties of the present invention are described in further detail below with reference to examples.
Example one
The preferred embodiment of the present invention provides an AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm, as shown in fig. 1, including the following steps:
step 1: initializing a population, and obtaining initial comprehensive cost values of all individuals in the whole population according to a cost function which integrates time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint;
step 1.1: setting a starting point and an end point of the AUV path, connecting the starting point and the end point, generating M equally divided points on the connecting line, drawing a vertical line on each equally divided point according to the connecting line of the starting point and the end point to obtain M vertical lines, and generating i on each vertical line_{max}A random point, thereby obtaining i_{max}The set of random points, the ith set of random points being denoted as RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2})，i∈(1,2,…,i_{max})；
Step 1.2: starting from the starting point, a fixed control point p is added in a short distance along the starting direction of the AUV_{1}From the end pointIncreasing a fixed control point p by a short distance in the opposite direction to the direction of termination of the AUV_{2}For controlling the starting and ending directions of the curved path;
step 1.3: according to step 1.1 and step 1.2, with a start point, an end point, a set of random points RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Fixed control point p_{1}And a fixed control point p_{2}As the control points of one Bspline curve, the number of the control points is n, where n is M +4, and the positions of the control points areCombining the curve order K to obtain i consisting of discrete points_{max}B spline curve, the curve equation is:
wherein the content of the first and second substances,andxaxis and Yaxis coordinates, B, of control points, respectively_{i,K}(t) is the basis function of the Kth order Bspline curve, and the control points and the basis function determine the shape of the Bspline curve;
step 1.4: from i_{max}Bspline curve to obtain i_{max}An AUV path, represented as:
wherein the orientation ψ of the AUV is:
step 1.5: will i_{max}Initializing group random points to obtain a population, and initializing a group with the groupMachine point RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Representing an individual, i.e. a solution x_{i}Wherein, the dimension of the individual is M, and the number of the individual is i_{max}The population P is:
that is to say that the first and second electrodes,
step 1.6: calculating the initial cost value of each individual according to a cost function integrating time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint, wherein the cost function combines time and a penalty function, and the expression is as follows:
wherein, T_{cost}Is a time penalty, obtained by dividing the path distance by the average velocity,andrepresenting twodimensional points on the i +1 th and ith discrete paths,  v  is the average velocity of the AUV motion,
ρ_{cost}is the curvature cost, obtained by accumulating the approximate curvature at each discrete point,
M_{cost}is the map cost, obtained by accumulating the values of each discrete point on the map (1 for obstacles, 0 for regions),
O_{cost}is ocean current information constraint and is obtained by accumulating the product of the included angle between the path direction of each discrete point and the water flow direction and the water flow vector modulus, h is the number of discrete points on a B spline curve, theta (DEG) is an included angle solving function,representing a velocity vectorAnd water flow rateThe included angle between the two parts is included,representing the speed magnitude;
step 1.7: respectively carrying out normalization processing on the four initial cost values, wherein different cost values have different dimensions and pass through the maximum cost valueAnd minimum cost valueTo obtain a normalized cost value
Step 1.8: and weighting and summing the four normalized cost values through different weights to obtain the initial comprehensive cost values of all individuals in the whole population P:
wherein the content of the first and second substances,respectively normalized time cost, curvature cost, map cost and ocean current cost, omega_{1}，ω_{2}，ω_{3}，ω_{4}The AUV path planning problem is modeled into a nonlinear cost function minimization problem, wherein the smaller the comprehensive cost value of the AUV path is, the better the AUV path is;
step 2: setting a population basic operation set and a corresponding operation selection probability vector; selecting an individual in the population, then selecting a population basic operation corresponding to the maximum probability in the probability vector by a selection operation, using the population basic operation for the individual, and updating the comprehensive cost value of the individual; repeating the process until the whole population is traversed, and finishing one iteration; then, adjusting operation selection probability vectors, and performing next iteration until the iteration times reach a set value;
step 2.1: setting a population basic operation set and a corresponding operation selection probability vector, wherein the population basic operation is o<·>Represents, solution x to input^{t}Outputting a new solution x after an iteration^{t+1}The expression is o<x^{t}>＝x^{t+1}The population basic operation is randomly moved, randomly jumped for a long distance, an individual with a following cost value smaller than the population basic operation, an individual with a minimum following cost value, an individual with the best following history or mutation, and the population basic operation set combines and represents the population basic operation as O { O_{1},o_{2},…,o_{k}And (5) each group basic operation corresponds to an operation selection probability vector which is P { P_{1},p_{2},...,p_{k}}, wherein:
the random shift refers to a shift solution x_{i}To its neighborhood, the expression is: x is the number of_{i}:＝x_{i}+λ_{min}Rand u, where λ_{min}Is the step size of the centralized search,is a random number subject to uniform distribution, u is a random unit direction vector;
the random longdistance jitter is a moving solution x_{i}To the remote location, the expression is: x is the number of_{i}:＝x_{i}+λ_{max}U, where λ_{max}Is a diversified searchThe step length of the cable, u is the vector in the random unit direction;
the individuals with less followup cost value than themselves mean that x is solved_{i}To all cost values less than x_{i}The solution of (2) is shifted by the expression: x is the number of_{i}:＝x_{i}+λ·rand·(x_{j}x_{i}) For all satisfy f (x)_{j})＜f(x_{i}) Solution x of_{j}X is solved_{i}All move towards it;
the individual with the minimum following cost value means that the solution x is converted into_{i}Moving to the solution with the minimum cost value, namely the optimal solution, the expression is: x is the number of_{i}:＝x_{i}+λ·rand·(x_{b}x_{i}) Wherein x is_{b}Is a solution of the optimal individual representation in the population;
the history following the best individual means that the solution x is to be solved_{i}The position of the individual with the minimum cost value, namely the individual with the best history, obtained after the individual is evolved for multiple times is moved, and the expression is as follows:wherein the content of the first and second substances,represents the solution x_{i}Historical best position of;
the mutation refers to solving x_{i}Mutation to obtain a new solution x_{i}′；
Step 2.2: selecting one individual in the population P in the step 1.5, and then selecting the population basic operation o corresponding to the maximum probability in the probability vector by the selecting operation_{i}；
Step 2.3: basic operations on individuals selected in step 2.2 using population_{i}And updating the comprehensive cost value of the current individual according to the steps 1.6 to 1.8;
step 2.4: repeating steps 2.2 and 2.3 until the whole population is traversed, using the population basic operation o once for each individual_{i}Then, completing one iteration;
step 2.5: evaluating the iterative operation condition of the step 2.4, adjusting the operation selection probability vector, and returningStep 2.2, carrying out the next iteration until the iteration times reach a set value, wherein the evaluation of the iteration operation condition of the step 2.4 means that the centralized index and the diversified index are ensured to be balanced; the centralized index is evaluated by the best cost value before and after iteration, i.e. in terms ofAndthe size relationship of (a); the diversity index is evaluated by the sum of the distances of all solutions and the central solution, and the formula is as follows:wherein the central solutionI.e. according to d^{t1}And d^{t}The size relationship of (a); the incremental relationship of the centralization index to the diversity index is shown in table 1,
TABLE 1
As can be seen from the above table, when evaluating the iterative operation condition, ifThen the centralization index is higher in this iteration, if d is the same time^{t1}＞d^{t}If so, it means that there is a small diversity index in the iteration, and therefore, it is necessary to reduce the centralized index, increase the diversity index, correspondingly adjust the selection probability, and then return to step 2.2 to continue the iteration; otherwise, after adjusting the selection probability corresponding to table 1, the iteration is continued by returning to step 2.2.
And step 3: after iteration is finished, selecting an individual with the minimum cost value, namely an optimal individual, combining a starting point, an end point and a fixed control point of the B spline curve to form a group of control points, and generating an optimal path by the group of control points.
The method is applied to an environment only containing ocean current information, and the planned path is shown in FIG. 3; the method is applied to an environment containing obstacles and ocean current information, and the planned path is shown in FIG. 4; it can be seen that the obtained path is continuous and smooth, and the tracking difficulty of the AUV can be reduced.
During the experiment, the stability of the invention over time and the stability of the cost are recorded for 200 times, and an analysis chart as shown in fig. 5 is made, so that the operation time is concentrated between 0.6s and 0.64s, and two abnormal values appear above the range of the maximum value and the minimum value, but are both within 0.66 s; the final cost value is concentrated between 610 and 635, and three abnormal values appear above the maximum value 635 but are all within 655, so that the method has better stability and can be applied to the path planning process of the actual AUV.
In summary, the invention adopts the Bspline curve to represent the AUV path, and a complex and smooth path can be obtained by using a small number of control points; modeling the path planning problem into a nonlinear optimization problem by using a cost function, and optimizing and selecting the control point position of the B spline by using a population hyperheuristic algorithm, wherein the population hyperheuristic algorithm integrates the advantages of various population element heuristic algorithms, has great advantages when solving the nonlinear optimization problem, and is simple in calculation and better in convergence; further, an optimal path satisfying time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint is obtained, the ocean current information constraint is defined only through the geometric relation between the path and the ocean current information, and the complexity of the method is reduced; the finally obtained path is continuous and smooth and meets the ocean current information constraint, and the tracking difficulty and the tracking path energy consumption of the robot are reduced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents and improvements made by those skilled in the art within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. An AUV path planning method in an ocean current environment based on a population hyperheuristic algorithm is characterized by comprising the following steps:
step 1: initializing a population, and obtaining initial comprehensive cost values of all individuals in the whole population according to a cost function integrating time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint, wherein the specific steps in the step 1 are as follows:
step 1.1: setting a starting point and an end point of the AUV path, connecting the starting point and the end point, generating M equally divided points on the connecting line, drawing a vertical line on each equally divided point according to the connecting line of the starting point and the end point to obtain M vertical lines, and generating i on each vertical line_{max}A random point, thereby obtaining i_{max}The set of random points, the ith set of random points being denoted as RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2})，i∈(1,2,…,i_{max})；
Step 1.2: starting from the starting point, a fixed control point p is added in a short distance along the starting direction of the AUV_{1}Starting from the end point, a fixed control point p is added by a short distance in the opposite direction to the direction of termination of the AUV_{2}For controlling the starting and ending directions of the curved path;
step 1.3: according to step 1.1 and step 1.2, with a start point, an end point, a set of random points RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Fixed control point p_{1}And a fixed control point p_{2}As the control points of one Bspline curve, the number of the control points is n, where n is M +4, and the positions of the control points areCombining the curve order K to obtain i consisting of discrete points_{max}B spline curve, the curve equation is:
wherein the content of the first and second substances,andxaxis and Yaxis coordinates, B, of control points, respectively_{i,K}(t) is the basis function of the Kth order Bspline curve, and the control points and the basis function determine the shape of the Bspline curve;
step 1.4: from i_{max}Bspline curve to obtain i_{max}An AUV path, represented as:
wherein the orientation ψ of the AUV is:
step 1.5: will i_{max}Initializing a group of random points to obtain a population, and initializing a group of random points RP_{M,i}(rp_{1,i},rp_{2,i},…rp_{k,i},…rp_{M,i}，rp_{k,i}∈R^{2}) Representing an individual, i.e. a solution x_{i}Wherein, the dimension of the individual is M, and the number of the individual is i_{max}The population P is:
that is to say that the first and second electrodes,
step 1.6: calculating the initial cost value of each individual according to a cost function integrating time efficiency, an environment map, an AUV body motion curvature radius and ocean current information constraint, wherein the cost function combines time and a penalty function, and the expression is as follows:
wherein, T_{cost}Is a time penalty, obtained by dividing the path distance by the average velocity,andrepresenting twodimensional points on the i +1 th and ith discrete paths,  v  is the average velocity of the AUV motion,
ρ_{cost}is the curvature cost, obtained by accumulating the approximate curvature at each discrete point,
M_{cost}the map cost is obtained by accumulating the value of each discrete point on the map, wherein the value of the discrete point on the map requires that the barrier is 1, the passable area is 0,
O_{cost}is ocean current information constraint and is obtained by accumulating the product of the included angle between the path direction of each discrete point and the water flow direction and the water flow vector modulus, h is the number of discrete points on a B spline curve, theta (DEG) is an included angle solving function,representing a velocity vectorAnd water flow rateThe included angle between the two parts is included,representing the speed magnitude;
step 1.7: respectively carrying out normalization processing on the four initial cost values, wherein different cost values have different dimensions and pass through the maximumCost valueAnd minimum cost valueTo obtain a normalized cost value
Step 1.8: and weighting and summing the four normalized cost values through different weights to obtain the initial comprehensive cost values of all individuals in the whole population P:
wherein the content of the first and second substances,respectively normalized time cost, curvature cost, map cost and ocean current cost, omega_{1}，ω_{2}，ω_{3}，ω_{4}Are weights of different cost values;
step 2: setting a population basic operation set and a corresponding operation selection probability vector; selecting an individual in the population, then selecting a population basic operation corresponding to the maximum probability in the probability vector by a selection operation, using the population basic operation for the individual, and updating the comprehensive cost value of the individual; repeating the process until the whole population is traversed, and finishing one iteration; then, adjusting operation selection probability vectors, and performing next iteration until the iteration times reach a set value;
and step 3: after iteration is finished, selecting an individual with the minimum cost value, namely an optimal individual, combining a starting point, an end point and a fixed control point of the B spline curve to form a group of control points, and generating an optimal path by the group of control points.
2. The AUV path planning method based on the population hyperheuristic algorithm under the ocean current environment according to claim 1, wherein the specific steps of the step 2 are as follows:
step 2.1: setting a population basic operation set and a corresponding operation selection probability vector, wherein the population basic operation is o<·>Represents, solution x to input^{t}Outputting a new solution x after an iteration^{t+1}The expression is o<x^{t}>＝x^{t+1}The population basic operation is randomly moved, randomly jumped for a long distance, an individual with a following cost value smaller than the population basic operation, an individual with a minimum following cost value, an individual with the best following history or mutation, and the population basic operation set combines and represents the population basic operation as O { O_{1},o_{2},…,o_{k}And (5) each group basic operation corresponds to an operation selection probability vector which is P { P_{1},p_{2},...,p_{k}}；
Step 2.2: selecting one individual in the population P in the step 1.5, and then selecting the population basic operation o corresponding to the maximum probability in the probability vector by the selecting operation_{i}；
Step 2.3: basic operations on individuals selected in step 2.2 using population_{i}And updating the comprehensive cost value of the current individual according to the steps 1.6 to 1.8;
step 2.4: repeating steps 2.2 and 2.3 until the whole population is traversed, using the population basic operation o once for each individual_{i}Then, completing one iteration;
step 2.5: and (4) evaluating the operation condition of the iteration in the step (2.4), adjusting the operation selection probability vector, and returning to the step (2.2) for the next iteration until the iteration number reaches a set value.
3. The AUV path planning method in ocean current environment based on population hyperheuristic algorithm of claim 2, characterized in that in step 2.1:
the random shift refers to a shift solution x_{i}To its neighborhood, the expression is: x is the number of_{i}:＝x_{i}+λ_{min}Rand u, where λ_{min}Is the step size of the centralized search,is a random number subject to uniform distribution, u is a random unit direction vector;
the random longdistance jitter is a moving solution x_{i}To the remote location, the expression is: x is the number of_{i}:＝x_{i}+λ_{max}U, where λ_{max}Is the step size of the diversified search, u is the random unit direction vector;
the individuals with less followup cost value than themselves mean that x is solved_{i}To all cost values less than x_{i}The solution of (2) is shifted by the expression: x is the number of_{i}:＝x_{i}+λ·rand·(x_{j}x_{i}) For all satisfy f (x)_{j})＜f(x_{i}) Solution x of_{j}X is solved_{i}All move towards it;
the individual with the minimum following cost value means that the solution x is converted into_{i}Moving to the solution with the minimum cost value, namely the optimal solution, the expression is: x is the number of_{i}:＝x_{i}+λ·rand·(x_{b}x_{i}) Wherein x is_{b}Is a solution of the optimal individual representation in the population;
the history following the best individual means that the solution x is to be solved_{i}The position of the individual with the minimum cost value, namely the individual with the best history, obtained after the individual is evolved for multiple times is moved, and the expression is as follows:wherein the content of the first and second substances,represents the solution x_{i}Historical best position of;
the mutation refers to solving x_{i}Mutation to obtain a new solution x_{i}′。
4. The AUV path planning method in ocean current environment based on population hyperheuristic algorithm of claim 2, wherein the evaluation step 2.4 in step 2.5 is performed in an iterative manner to ensure that the centralized index, which is evaluated by the optimal cost value before and after iteration, is balanced with the diversity indexAndthe size relationship of (a); the diversity index is evaluated by the sum of the distances of all solutions and the central solution, and the formula is as follows:wherein the central solutionI.e. according to d^{t1}And d^{t}Is determined by the magnitude relationship of (a).
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