CN117608200B - Ocean aircraft path planning method - Google Patents
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Abstract
The invention discloses a method for planning a path of an ocean aircraft, and relates to the field of path planning. The method comprises the following steps: s1, acquiring underwater topography data of an ocean aircraft operation area; s2, constructing a cost function of the ocean aircraft path; s3, carrying out two-dimensional path planning on the XY plane; s4, planning a z-axis path according to the elevation information on the two-dimensional path planning line; and S5, performing B spline interpolation on the planned path, and smoothing the path to finish the three-dimensional path planning of the marine craft. According to the invention, through dimension reduction processing and an improved exponential distribution optimization algorithm, the exploration and development capacity of the optimization algorithm are balanced, the global searching and convergence capacity is balanced, and on the premise of not discarding underwater altitude information, three-dimensional path planning under a complex marine environment can be rapidly, stably and smoothly completed, so that the method has the advantages of higher optimizing speed, higher safety and stability and higher efficiency.
Description
Technical Field
The invention relates to the field of path planning, in particular to a method for planning a path of an ocean aircraft.
Background
The ocean resource value is huge. With the development of independent ocean observation and data acquisition technologies, ocean exploration and development are increasingly emphasized. As one of the main tools for underwater exploration, the ocean craft plays an important role in underwater tasks such as submarine exploration, the role of the intelligent ocean craft is more and more prominent, one of the key technologies of the intelligence of the ocean craft is the path planning problem of robots, and three-dimensional path planning of the ocean craft in the underwater environment is a key for guaranteeing safe navigation and reliable task completion.
The path planning problem of the underwater robot is different from the path planning of the water surface and the land, the motion of the robot under the water is three-dimensional motion, and the elevation information needs to be considered and the energy consumption of the robot is reduced as much as possible. The underwater environment is complex, the ravines are vertical and horizontal, the topography fluctuation is large, and for the traditional path planning algorithm, the problems of slow calculation, large path fluctuation, local optimum sinking and the like are easy to occur facing complex underwater three-dimensional information.
The excellent path planning algorithm can enable the robot to avoid dangers and barriers in the path, and improves task execution capacity. According to different application scenes, a plurality of different solutions are proposed by vast domestic and foreign scholars. However, along with the expansion of the application field of robots, the conventional path planning algorithm in early stage is difficult to meet the requirement of path optimization in complex environments, and the problems of insufficient computing capacity, low solving precision, long running time and the like are exposed. Therefore, researchers turn eyes to a group intelligent optimization algorithm with strong robustness, and a new direction is provided for solving the path planning problem.
The group optimization algorithm comprises a particle group optimization algorithm, a whale optimization algorithm, a gray wolf optimization algorithm and the like, and the group optimization algorithms simulate the behaviors of some living things in real life so as to perform optimization. The algorithm has the advantages of good global searching capability, high convergence speed, easy understanding and the like.
The core of the particle swarm algorithm is that the particle swarm is changed from disorder to target movement to the optimal solution direction in the space through mutual sharing information among particles, but the particle swarm algorithm contains a plurality of parameters, and the adjustment of the parameters affects the quality of the result. The whale optimization algorithm is a bionic meta-heuristic algorithm provided according to the behavior of the whale group on the small fish and shrimp, and the prey process is completed through shrink wrapping, bubble network attack and random search, and the position of the prey is the global optimal solution of the problem; the whale optimization algorithm has better exploration capability, but has limited development capability and slow convergence speed. The gray wolf optimization algorithm has a hierarchical structure and can establish the hierarchical advantage of the objective function. In addition, the objective function can be further subdivided into a fitness function and a cost function, so that the best solution can be obtained in all candidate solutions, but the convergence factor in the algorithm can influence the searching range of the algorithm, and in the variation trend of the convergence factor reduction, the global optimization and the local optimization of the algorithm cannot be balanced, so that the global optimal solution cannot be obtained.
Disclosure of Invention
Based on the technical problems, the invention provides a marine aircraft path planning method.
The technical scheme adopted by the invention is as follows:
a method of marine vehicle path planning comprising the steps of:
s1, acquiring underwater topography data of an ocean aircraft operation area;
S2, constructing a cost function of the ocean aircraft path;
s3, carrying out two-dimensional path planning on the XY plane
S31, initializing the position information of an index distribution optimization algorithm by using a guidance initialization method obeying normal distribution, and selecting path points according to a rule of normal distribution by taking a distance d from a straight line L connecting a starting point and an ending point as a parameter;
the distance d between the ith path point and the straight line L is subjected to the mean value of 0 and the standard deviation of 0 Normal distribution of (c):
；
the coordinates are distributed at equal intervals, Is determined by the following formula:
；
In the method, in the process of the invention, Is shown inWhen in treatment, the value on the straight line L is taken;
S32, constructing an exponential distribution optimization algorithm model, setting the maximum iteration times, evaluating the fitness value of an initial path according to a cost function, and carrying out incremental sequencing on the population according to the fitness value;
s33, taking the average value of the positions of the first three individuals of the fitness value as a guiding solution The method comprises the following steps:
；
In the method, in the process of the invention, Representing the position corresponding to the ith individual in the t-th iteration;
s34, defining a group of variables The value is equal to the initial population,；
S35, in the development stage, the position updating formula of the ith individual is as follows:
；
；
；
In the method, in the process of the invention, Is a process variable that is a function of the process,Is a random number uniformly generated in [0,1], and rand () represents a random number between [0,1 ];
s36, in the exploration stage, the position updating formula of the ith individual is as follows:
；
；
；
；
；
Wherein, Representing the average position of the population, T representing the maximum number of iterations, r1 and r2 representing random integers between [1, N ], and r1+.r2;
Development and exploration are performed randomly;
S37, after the development and exploration of one iteration are completed, performing cross mutation operation on the whole population; after the cross mutation operation is completed, greedy selection is carried out, individuals with excellent fitness are selected to be left, and the rest individuals are eliminated;
S38, adding the guide solution into the rows and columns of the population;
s39, updating the population obeys the principle that the individual u needs to be greedy selected and then updated Without greedy selection, directly byConstructing;
；
s310, if the maximum iteration times are reached, ending the iteration, and carrying out the following steps; if the maximum iteration number is not reached, calculating the fitness of the population individuals, and returning to the step S33 to continue iteration;
s4, planning a z-axis path according to the elevation information on the two-dimensional path planning line;
And S5, performing B spline interpolation on the planned path, and smoothing the path to finish the three-dimensional path planning of the marine craft.
The beneficial technical effects of the invention are as follows:
According to the ocean aircraft path planning method, the exploration and development capacity of the optimizing algorithm is balanced through the dimension reduction processing and the improved index distribution optimizing algorithm, the global searching and convergence capacity is balanced, the three-dimensional path planning under the complex ocean environment can be rapidly, stably and smoothly completed on the premise of not discarding underwater altitude information, the method has the advantages of faster optimizing speed, higher safety and stability and higher efficiency, and the practicability and effectiveness of the method are proved through actual ocean seabed topography verification.
Specifically, the marine craft path planning method of the invention has the following advantages:
1. Initializing an algorithm initial population by using a guidance initialization method obeying normal distribution; 2. the global searching capability of the algorithm is improved by combining the crossed variation thought of the genetic algorithm, so that the problem of local optimum is avoided; 3. introducing a guide solution in an original algorithm into iterative optimization of the population, and increasing the convergence rate of the algorithm; 4. the complex three-dimensional path planning problem is converted into two-dimensional path planning and a height two-dimensional independent process, so that the calculation dimension is reduced, and the algorithm efficiency is improved.
Drawings
The invention is further described with reference to the drawings and detailed description which follow:
FIG. 1 is a flow chart of a method for planning a path of an ocean going vehicle according to the present invention;
FIG. 2 is a detailed flow chart of the marine craft path planning method of the present invention, mainly illustrating the flow of the improved exponential distribution optimization algorithm;
FIG. 3 is a schematic view of guidance initialization following normal distribution in the marine craft path planning method of the present invention;
FIG. 4 is a schematic diagram of the cross variation in the marine craft path planning method of the present invention;
FIG. 5 is a schematic view of selected areas of the marine craft path planning method of the present invention when validated;
FIG. 6 is a three-dimensional path planning result diagram when the marine craft path planning method of the present invention is verified;
FIG. 7 is a plan view of the three-dimensional path planning result when the marine craft path planning method of the present invention is verified;
Fig. 8 is a three-dimensional path planning fitness change chart when the marine vehicle path planning method of the present invention is verified.
Detailed Description
While the marine craft path planning method is rapidly developed, there are still some problems:
Firstly, only plane path planning is considered, most of the altitude factors which do not consider underwater topography are researched by the existing ocean aircraft path planning method, a three-dimensional ocean environment is simply converted into a two-dimensional plane model to carry out a simulation experiment, or a plane model is randomly generated to carry out an algorithm experiment, so that the actual operation requirement of an ocean aircraft cannot be met;
secondly, the existing ocean aircraft underwater path planning method solves the problems of complex calculation, slow optimization speed, non-ideal path tortuosity, unstable optimization effect and the like when the path planning problem of a complex underwater environment is solved.
The exponential distribution optimization algorithm (exponential distribution optimizer) is a population optimization algorithm, and mainly inspires from an exponential probability distribution model in mathematics, and has high exploration capacity, but the convergence speed is low when the path planning problem is processed.
Aiming at the problems, the invention provides a three-dimensional path planning method of an ocean aircraft based on an improved or optimized exponential distribution optimization algorithm.
As shown in fig. 1, a method for planning a path of an ocean craft comprises the following steps:
s1, acquiring underwater topography data of an ocean aircraft operation area, wherein the underwater topography data can be acquired by in-situ measurement or global ocean topography data provided at GEBCO websites.
S2, constructing a cost function of the ocean aircraft path, and evaluating the path.
S21, a calculation formula of the cost function F is as follows:
(1)
S22, the cost function comprises a path length F l, a height F h and a step length F s, and the calculation formula is as follows:
(2)
(3)
(4)
(5)
where n is the number of waypoints, Represents the plane distance between the (i+1) th path point and the (i) th path point, (-) th path point and the (i) th path point, the (i) th path point is the (i) th path point, the (i) th) Indicating the location of the i-th waypoint,As the height of the end point,For the height of the i-th waypoint,For a specified maximum step size, assignment can be performed according to actual conditions.
S3, two-dimensional path planning is conducted on the XY plane.
The improved exponential distribution optimization algorithm mainly comprises a guidance initialization method which obeys normal distribution, a cross variation idea combined with a genetic algorithm and a guidance solutionIs introduced. As shown in fig. 2, the specific steps are as follows:
S31, firstly initializing the position information of an exponential distribution optimization algorithm by using a guidance initialization method obeying normal distribution, and selecting path points according to a rule of normal distribution by taking a distance d from a straight line L connecting a starting point and an ending point as a parameter.
As shown in FIG. 3, the distance d between the ith path point and the straight line L is 0 as the average value and 0 as the standard deviationNormal distribution of (c):
(6)
the coordinates are equally distributed, i.e. the x-axis is equally divided into n +1 parts, in particular n path points, i.e. the x-axis is equally divided, i.e. (end-start)/n +1, Is determined by the following formula:
(7)
In the method, in the process of the invention, Is shown inAnd when the value is in the position, the value on the straight line L is taken.
S32, constructing an exponential distribution optimization algorithm model, setting the maximum iteration times, evaluating the fitness value of the initial path according to the cost function, and carrying out incremental sequencing on the population according to the fitness value.
S33, taking the average value of the positions of the first three individuals of the fitness value as a guiding solutionThe method comprises the following steps:
(8)
In the method, in the process of the invention, Representing the location corresponding to the ith individual in the t-th iteration.
S34, defining a group of variablesThe value is equal to the initial population,。
S35, in the development stage, the position updating formula of the ith individual is as follows:
(9)
(10)
(11)
In the method, in the process of the invention, Is a process variable that is a function of the process,Is a random number uniformly generated in [0,1], and rand () represents a random number between [0,1 ].
S36, in the exploration stage, the position updating formula of the ith individual is as follows:
(12)
(13)
(14)
(15)
(16)
Wherein, Representing the average position of the population, T represents the maximum number of iterations, r1 and r2 represent random integers between [1, N ], and r1+.r2.
The processing of step S35 and step S36 is not sequential, i.e. development and exploration are performed randomly. The proportion of development and exploration is preferably half, that is to say the probability of development and exploration is 50% each and is random.
S37, fusing the crossed variation thought of the genetic algorithm, increasing the global searching capability of the algorithm, and avoiding the problem of sinking into local optimum. After one iteration of development and exploration is completed, the cross-variation operation is performed on the whole population, as shown in fig. 4.
Wherein the crossing is the ith individual and the ith individualThe individuals proceed to generate new individuals, the crossover position p is randomly generated, the mutation is a small probability event, and the probability of mutation of each individual is set to 0.2.
And after the cross mutation operation is finished, greedy selection is carried out, individuals with excellent fitness are selected to be left, and the rest individuals are eliminated.
S38, introducing instruction solutionThe guide solution is added to the line of the population. The guide solution is the average value of the first three individuals, and the convergence rate of the population can be accelerated by adding the guide solution into the population.
S39, updating the population obeys the principle that the individual u needs to be greedy selected and then updatedWithout greedy selection, directly byConstructing;
(17)
and S310, ending the iteration and exiting if the maximum iteration number is reached, and performing the following step S4. If the maximum iteration number is not reached, the fitness of the population individuals is calculated, and the step S33 is returned to continue iteration.
S4, planning a z-axis path according to the elevation information on the two-dimensional path planning line.
S41, acquiring a height value H of the corresponding submarine topography on the XY plane path pointThenCorresponding height value at pointThe method comprises the following steps:
(18)
Wherein, Is the safety height from the sea bottom when the ocean craft sails.
S42, willHeight from the previous path pointBy contrast, the height of the next path cannot be lower than that of the previous path point, so that the frequency of frequent ascending and descending of the robot is reduced, and the power consumption is reduced.
(19)
And S5, performing cubic B spline interpolation on the planned path, and smoothing the path to finish the three-dimensional path planning of the ocean aircraft.
The formula for B-spline interpolation is as follows:
(20)
(21)
In the method, in the process of the invention, The point of the path is represented by a point of the path,Represents the ith k-th order B-spline basis function, and is connected with the path pointIn a corresponding manner,For the argument, agree 0/0=0,Is a set of continuously changing values of non-decreasing sequence, with the first and last values being 0 and 1.
The verification of the marine craft path planning method of the present invention is performed by selecting the submarine topography of the areas from longitude and latitude (23.9282,135.9888) to (23.3844,136.6809), and the results are shown in fig. 5-8. FIG. 5 is a schematic view of selected areas of the marine craft path planning method of the present invention when validated. FIG. 6 is a three-dimensional path planning result diagram when the marine craft path planning method of the present invention is verified; the solid line a in the figure is a path obtained by adopting an improved exponential distribution optimization algorithm, the dotted line b is a path obtained by adopting an original exponential distribution optimization algorithm, and it is obvious from the figure that the path a is more gentle than the path b, the height change is small, and no large-amplitude rotation angle exists. FIG. 7 is a plan view of the three-dimensional path planning result when the marine craft path planning method of the present invention is verified; the solid line a in the figure is a path obtained by adopting an improved exponential distribution optimization algorithm, the broken line b is a path obtained by adopting an original exponential distribution optimization algorithm, and the path a is more gentle than the path b, and is not greatly forwarded and is shorter. FIG. 8 is a three-dimensional path planning fitness change chart of the marine craft path planning method of the present invention when validated; the curve in the graph represents the path fitness change of the improved exponential distribution optimization along with the increase of the iteration times, and it can be seen that the algorithm fitness quickly converges and reaches the vicinity of the optimal value in the initial iteration stage; at the later stage of iteration, the algorithm is not trapped in local optimization, and the path is continuously optimized.
The parts not described in the above modes can be realized by adopting or referring to the prior art.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A method of marine craft path planning, comprising the steps of:
s1, acquiring underwater topography data of an ocean aircraft operation area;
S2, constructing a cost function of the ocean aircraft path;
s3, carrying out two-dimensional path planning on the XY plane
S31, initializing the position information of an index distribution optimization algorithm by using a guidance initialization method obeying normal distribution, and selecting path points according to a rule of normal distribution by taking a distance d from a straight line L connecting a starting point and an ending point as a parameter;
The distance d of the ith waypoint from the straight line L obeys a normal distribution with a mean value of 0 and a standard deviation of δ:
d～N(0,δ2)；
The x i coordinates are equally spaced and y i is determined by the following formula:
yi＝yii+d；
Wherein y ii represents a value on the straight line L at x i;
S32, constructing an exponential distribution optimization algorithm model, setting the maximum iteration times, evaluating the fitness value of an initial path according to a cost function, and carrying out incremental sequencing on the population according to the fitness value;
s33, taking the average value of the positions of the first three individuals of the fitness value as a guiding solution Namely:
In the method, in the process of the invention, Representing the position corresponding to the ith individual in the t-th iteration;
s34, defining a group of variables w, wherein the values of the variables w are equal to the initial population,
S35, in the development stage, the position updating formula of the ith individual is as follows:
Where v i is a process variable, phi is a random number uniformly generated in [0,1], and rand () represents a random number between [0,1 ];
s36, in the exploration stage, the position updating formula of the ith individual is as follows:
Wherein M t represents the average position of the population, T represents the maximum number of iterations, r1 and r2 represent random integers between [1, N ], and r1+.r2;
Development and exploration are performed randomly;
S37, after the development and exploration of one iteration are completed, performing cross mutation operation on the whole population; after the cross mutation operation is completed, greedy selection is carried out, individuals with excellent fitness are selected to be left, and the rest individuals are eliminated;
S38, adding the guide solution into the rows and columns of the population;
S39, updating the population complies with the following principle that the individual u needs to be subjected to greedy selection and then is updated, and w does not need greedy selection and is directly formed by v;
s310, if the maximum iteration times are reached, ending the iteration, and carrying out the following steps; if the maximum iteration number is not reached, calculating the fitness of the population individuals, and returning to the step S33 to continue iteration;
s4, planning a z-axis path according to the elevation information on the two-dimensional path planning line;
And S5, performing B spline interpolation on the planned path, and smoothing the path to finish the three-dimensional path planning of the marine craft.
2. A method of planning a path for a marine craft according to claim 1, wherein step S2 comprises the steps of:
s21, a calculation formula of the cost function F is as follows:
F＝Fl+Fh+Fs；
S22, the cost function comprises a path length F l, a height F h and a step length F s, and the calculation formula is as follows:
Where n is the number of waypoints, D i is the planar distance between the (i+1) th waypoint and the (i) th waypoint, (x i,yi,zi) is the position of the (i) th waypoint, z e is the height of the end point, z i is the height of the (i) th waypoint, and step is the prescribed maximum step size.
3. The marine craft path planning method according to claim 1, characterized in that in step S36: the proportion of development and exploration is half of each other.
4. The marine craft path planning method according to claim 1, characterized in that in step S36: crossover is the ith individualThe individuals proceed to generate new individuals, the crossover position p is randomly generated, the mutation is a small probability event, and the probability of mutation of each individual is set to 0.2.
5. A method of planning a path for a marine craft according to claim 1, wherein step S4 comprises the steps of:
S41, acquiring a height value H (x i,yi) of the corresponding submarine topography on the XY plane path point, wherein a corresponding height value z i at the point (x i,yi) is:
zi＝H(xi,yi)+Hsafe；
Wherein H safe is the safety height from the sea floor when the marine craft sails;
s42, comparing z i with the height z i-1 of the previous path point, wherein the height of the next path cannot be lower than the height of the previous path point;
6. the marine craft path planning method according to claim 1, characterized in that in step S5: the B-spline interpolation is performed three times, and the formula of the B-spline interpolation is as follows:
Where P i represents the point of the path, Represents the ith k-th order B-spline basis function corresponding to the path point P i,/>Is an independent variable,/>Is a set of continuously changing values of non-decreasing sequence, with the first and last values being 0 and 1.
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