CN113917929A - Unmanned ship path optimization method and system based on hybrid particle swarm algorithm - Google Patents
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Abstract
The embodiment of the invention provides a method and a system for optimizing a path of an unmanned ship based on a hybrid particle swarm algorithm.
Description
Technical Field
The embodiment of the invention relates to the technical field of unmanned ships, in particular to an unmanned ship path optimization method and system based on a hybrid particle swarm algorithm.
Background
As an important measure of the national economic strategy, the research and application of unmanned ships promote the development of marine economy, are favorable for reducing the marine transportation cost and the labor cost, and can be particularly applied to various engineering mineral products, fishery transportation, ship and island resource supply, marine surveying and mapping and hydrological monitoring. With the increasing emphasis of the country on marine resources, the continuous frequency of marine exploration, mining and transportation activities, and the continuous development and progress of science and technology, the intelligent, systematized and unmanned ship system becomes a new development direction. In recent years, a novel research subject, namely a water surface unmanned ship, is developed by combining a ship with an advanced control technology, and the unmanned ship is a small water surface unmanned platform which can complete tasks such as target detection and the like through autonomous perception planning and autonomous navigation. Unmanned ships have wide and good development prospects in various fields, and the technology of the unmanned ships gradually becomes the focus of attention and the key object of research.
With the rapid development of artificial intelligence and deep learning, unmanned and intelligent development becomes one of the main directions of ship development. The unmanned ship as a full-automatic water surface robot can independently navigate in a complex marine environment so as to replace human beings to complete important tasks. The autonomous navigation capability is realized by depending on the accurate sensing of the ship to the environment, but the sensing of the existing unmanned ship to the surrounding navigation environment cannot meet the requirements of autonomous navigation real-time performance and accuracy in complex sea conditions and high-speed navigation.
Disclosure of Invention
The embodiment of the invention provides a hybrid particle swarm algorithm-based unmanned ship path optimization method and system, and aims to solve the problem that the perception of the existing unmanned ship to the surrounding navigation environment cannot meet the requirements of autonomous navigation real-time performance and accuracy in complex sea conditions and high-speed navigation.
In a first aspect, an embodiment of the present invention provides an unmanned ship path optimization method based on a hybrid particle swarm algorithm, including:
step S1, reselecting a plurality of new path points based on the current planned path of the unmanned ship, wherein the current planned path comprises a plurality of path segments which are sequentially connected, and each new path point is positioned on one path segment;
step S2, constructing a fitness function by using the path length minimum formed by connecting the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of each new path point as a second objective function, and using the distance average value minimum of each new path point from an obstacle as a third objective function;
and step S3, determining the adaptive value of each particle in the mixed particle algorithm by the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
Preferably, the step S1 specifically includes:
the optimal path points searched on the linked graph based on the Dijkstra algorithm are sequentially P0,P1,P2,…,Pn,P n+11; wherein, P0As a starting point, Pn+1Is the target point; the link line of the path point is L in sequencei(i＝1，2，…，n)；
Determining a link line LiThe expression for all points:
Pi(hi)＝Pi (0)+(Pi (1)-Pi (0))×hi
in the above formula, hiIs a proportionality coefficient of hi∈[0,1](ii) a d is the number of the link lines;
and
are respectively a link line LiTwo end points of (a).
Preferably, in step S2, the path length formed after connecting the new path points on all the adjacent link lines is:
in the above formula, diRepresents a link line LiNew path point P ofiAnd link line Li+1New path point P ofi+1Path segment length between; (x)i,yi) Is a new path point PiThe coordinates of (a).
Preferably, in step S2, the corner at each new path point is:
in the above formula, the first and second carbon atoms are,
indicating a new path point Pi-1To a new path point PiVector of (c), -Pi-1PiL represents the length of the vector;
in step S3, the path smoothness is determined based on the corner average at each new path point as:
in the above formula, k is a penalty coefficient, and k is alphaiIn (d) is greater than or equal to pi/2.
Preferably, in step S2, the shortest distance between the new path point and the obstacle is:
di＝min{PiPi (0),PiPi (1)}
in step S3, the distance average of the obstacles is:
in step S3, the path security coefficient is:
in the above formula, λ is a weight adjustment coefficient, and k is the number of new waypoints having a shortest distance to the obstacle of 0. Preferably, in step S3, the fitness function is:
FitV＝ω1*f1+ω2*f2+ω3*f3
in the above formula, f1The path length formed after the new path points on all the adjacent link lines; f. of2Is the path smoothness; f. of3Is a path security coefficient; omega1、ω2、ω3Are respectively a weight coefficient, omega1+ω2+ω3＝1。
Preferably, the step S3 specifically includes:
step S31, initializing parameters in the hybrid particle swarm algorithm, and randomly setting the speed and the position of each particle;
step S32, evaluating the fitness of each particle, and storing the position and fitness value of each particle in the extreme value p of each particlebestAnd all p are substitutedbestThe individual position of the optimum adaptation value and the adaptation value in (b) are saved to the global extremum gbestPerforming the following steps;
step S33, determining initial temperature t0；
Step S34, determining the particle p under the current temperature according to the fitness functioniAn adaptation value of;
step S35, betting on roulette from all piTo determine a global optimum pgP 'as substitute'g；
Step S36, updating the speed and position of each particle;
step S37, calculating the adaptive value of each particle, and updating pbestAnd gbestAnd carrying out annealing operation;
and step S38, if the preset stop condition is judged to be reached, stopping searching and outputting the optimal value of the particle swarm, otherwise, returning to the step S34.
In a second aspect, an embodiment of the present invention provides an unmanned ship path optimization system based on a hybrid particle swarm algorithm, including:
the route planning module reselects a plurality of new route points based on a current planned route of the unmanned ship, wherein the current planned route comprises a plurality of route sections which are sequentially connected, and each new route point is positioned on one route section;
the path optimization module is used for constructing a fitness function by using the path length minimum formed by connecting all the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of all the new path points as a second objective function and using the distance average value minimum of all the new path points from an obstacle as a third objective function;
and the mixed particle swarm solving module is used for determining the adaptive value of each particle in the mixed particle algorithm according to the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the program, implements the steps of the hybrid particle swarm algorithm-based unmanned ship path optimization method according to the first aspect of the present invention.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the hybrid particle swarm algorithm-based unmanned ship path optimization method according to the embodiment of the first aspect of the present invention.
The unmanned ship path optimization method and system based on the hybrid particle swarm algorithm provided by the embodiment of the invention take all path points in the current planned path of the unmanned ship as the basis, simultaneously consider the multi-point constraint, avoid the natural condition constraints such as obstacles and the like, divide the path points again, comprehensively consider the path length, the path smoothness and the path safety, realize the multi-objective optimization of the path length, the path smoothness and the path safety, and plan a global path which accords with the actual navigation of the unmanned ship.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a hybrid particle swarm algorithm-based unmanned ship path optimization method according to an embodiment of the invention;
FIG. 2 is a schematic corner view according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a shortest distance between a waypoint and an obstacle according to an embodiment of the invention;
FIG. 4 is a flow chart of a particle swarm algorithm based on simulated annealing according to an embodiment of the invention;
fig. 5 is a schematic physical structure diagram according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiment of the present application, the term "and/or" is only one kind of association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the terms "comprise" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a system, product or apparatus that comprises a list of elements or components is not limited to only those elements or components but may alternatively include other elements or components not expressly listed or inherent to such product or apparatus. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
With the rapid development of artificial intelligence and deep learning, unmanned and intelligent development becomes one of the main directions of ship development. The unmanned ship as a full-automatic water surface robot can independently navigate in a complex marine environment so as to replace human beings to complete important tasks. The autonomous navigation capability is realized by depending on the accurate sensing of the ship to the environment, but the sensing of the existing unmanned ship to the surrounding navigation environment cannot meet the requirements of autonomous navigation real-time performance and accuracy in complex sea conditions and high-speed navigation.
Therefore, the embodiment of the invention provides a method and a system for optimizing a path of an unmanned ship based on a hybrid particle swarm algorithm, which take all path points in the current planned path of the unmanned ship as a basis, simultaneously consider the constraints of multiple points and natural conditions such as obstacle avoidance and the like, divide the path points again, comprehensively consider the path length, the path smoothness and the path safety, realize the multi-objective optimization of the path length, the path smoothness and the path safety, and plan a global path which accords with the actual navigation of the unmanned ship. The following description and description will proceed with reference being made to various embodiments.
Fig. 1 provides a hybrid particle swarm algorithm-based unmanned ship path optimization method according to an embodiment of the present invention, which includes:
step S1, reselecting a plurality of new path points based on the current planned path of the unmanned ship, wherein the current planned path comprises a plurality of path segments which are sequentially connected, and each new path point is positioned on one path segment;
in this embodiment, as a preferred implementation manner, path points of the optimal path searched on the link graph by using Dijkstra algorithm may be P in sequence0,P1,P2,…,Pn,P n+11; wherein, P0As a starting point, Pn+1Is the target point; in other embodiments than the one of the present invention, other path search algorithms may also be utilized;
the link line of the path point is L in sequencei(i＝1，2，…，n)。
Optionally selecting one point on each link line as a new path point, determining the distance between the new path point and the obstacle, connecting the path lengths formed by the new path points on all the adjacent link lines, and forming a corner at each new path point;
is provided with
And
are respectively a chainWiring LiDetermines the link line LiExpressions for the remaining points:
in the above formula, hiIs a proportionality coefficient of hi∈[0,1](ii) a d is the number of link lines.
As can be seen from the above formula, when each link line that the optimal path passes through is obtained through the Dijkstra algorithm, only one set of parameters (h) needs to be given1,h2,…,hd) A new path from the starting point to the target point is obtained. The objective function of the optimization problem can be defined as follows:
in the above formula, n represents the number of path points in the path except the starting point and the target point; length ((P)i(hi),Pi+1(hi+1) Represents a new path point PiTo a new path point Pi+1When i is 0, P0(h0) Represents a starting point S; when i is n, Pn(hn) The target point T is represented. The solution of the hybrid particle swarm algorithm can be expressed as finding the optimal parameter (h)1,h2,…,hd) Such that formula L has the shortest distance.
In addition, in order to make the planned path conform to the actual navigation of the unmanned ship, not only the global shortest path is searched, the embodiment performs multi-objective path optimization, specifically, the optimization objective includes path length optimization, path smoothing optimization and path security optimization.
Step S2, forming a first objective function f with the minimum path length formed by connecting the new path points in all the path segments in sequence1The smoothness average value at each new path point is taken as the minimum of a second objective function f2Taking the minimum average value of the distances from each new path point to the obstacle as a third objective functionf3Constructing a fitness function
In this embodiment, if there are n route points in the route, the route is correspondingly composed of n-1 line segments. Assume that each path is of the form L ═ P0,P1,P2,…,Pn,Pn+1](ii) a Wherein n represents the number of path points except for the starting point and the target point in the path; p0Is a starting point S, Pn+1The target point T is represented.
The path length is obtained by calculating the sum of the lengths of the paths of the segments in the path. The length calculation formula of each path is as follows:
wherein (x)i,yi) Is a new path point PiThe coordinates of (a).
Thus, the path length f1The calculation formula of (2) is as follows:
path length f when the algorithm optimizes the path1The smaller the better.
Because the motion characteristics of the unmanned ship are influenced by factors such as the size of the unmanned ship, the navigation path of the unmanned ship should be as smooth and gentle as possible, namely, the corner value at each path point should be as small as possible. Path point PiThe corner is schematically shown in fig. 2, and the calculation formula is as follows:
in the above formula, the first and second carbon atoms are,
indicating a new path point Pi-1To a new path point PiVector of (c), -Pi-1PiAn expression vectorLength of (d); in this embodiment, the path smoothness is represented by an inflection point average value, and a calculation formula thereof is defined as follows:
in the above formula, k is alphaiThe number of the corner is larger than or equal to pi/2, also called a penalty coefficient, namely when a certain corner is larger than or equal to pi/2, the penalty is carried out on the target value. When n is 0, the path is a connecting line f from the starting point to the target point2The score has a value of 0.
From the above formula, f2The smaller the value of (d), the smaller the corner average value, the smoother the turn, and the smoother the path. Thus, the optimization objective is f2The smaller the value of (A), the better.
In order to improve the practicability of the planned path on a real ship, the influence of the self shape of the unmanned ship and the storm flow in the environment on the navigation of the unmanned ship needs to be considered. Therefore, during the navigation process of the unmanned ship, the unmanned ship not only needs to successfully avoid the obstacle, but also needs to be far away from the obstacle as much as possible so as to improve the navigation safety.
By combining the characteristics of the path planning in the embodiment, the shortest distance d between the path point and the barrieriGet
And
the smaller of the two distances, as shown in fig. 3, is:
di＝min{PiPi (0),PiPi (1)}
n represents the number of path points except the starting point and the target point in the path, and the distance average value of the obstacles is as follows:
in step S3, the path security coefficient is:
in the above formula, λ is a weight adjustment coefficient, which is used to solve the problem that the value is too small after the reciprocal of the average distance is calculated; k is the number of new path points with the shortest distance to the barrier being 0, also called penalty coefficient.
Determining a path safety coefficient based on the distance of the obstacle, wherein the path safety coefficient is smaller when the distance of the obstacle is larger; determining path smoothness based on the corners, constructing a fitness function based on the path safety coefficient, the path length and the path smoothness, and determining new path point coordinates on each link line when the fitness function is minimum.
In the algorithm in this embodiment, when the path point search is performed, the fitness function is used to evaluate the path point, so that the three objective functions need to be converted into the fitness function of the algorithm.
The embodiment of the invention adopts a weight coefficient method to solve the problem of mapping from a multi-target function to an algorithm fitness function. Distributing a weight value to each objective function, and then weighting and summing the objective functions to obtain a new fitness function, wherein the expression form of the fitness function is as follows:
FitV＝ω1*f1+ω2*f2+ω3*f3
in the above formula, f1The path length formed after the new path points on all the adjacent link lines; f. of2Is the path smoothness; f. of3Is a path security coefficient; omega1、ω2、ω3Are respectively a weight coefficient, omega1+ω2+ω3＝1。
With f1、f2、f3Is an objective function, wherein the objective function is taken to be the minimum, i.e. (h) when the fitness function needs to be minimized1,h2,…,hd) And obtaining the optimized path.
And step S3, determining the adaptive value of each particle in the mixed particle algorithm by the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
The flow of the hybrid particle swarm optimization based on the simulated annealing algorithm is shown in fig. 4, and the specific steps are as follows:
step S31, initializing parameters in the hybrid particle swarm algorithm, and randomly setting the speed and the position of each particle;
step S32, evaluating the fitness of each particle, and storing the position and fitness value of each particle in the extreme value p of each particlebestAnd all p are substitutedbestThe individual position of the optimum adaptation value and the adaptation value in (b) are saved to the global extremum gbestPerforming the following steps;
step S33, determining initial temperature t0；
Step S34, determining the particle p under the current temperature according to the fitness functioniAn adaptation value of;
step S35, betting on roulette from all piTo determine a global optimum pgP 'as substitute'g；
Step S36, updating the speed and position of each particle;
step S37, calculating the adaptive value of each particle, and updating pbestAnd gbestAnd carrying out annealing operation;
and step S38, if the preset stop condition is judged to be reached, stopping searching and outputting the optimal value of the particle swarm, otherwise, returning to the step S34.
The embodiment of the invention also provides an unmanned ship path optimization system based on a hybrid particle swarm algorithm, and the unmanned ship path optimization method based on the hybrid particle swarm algorithm in the embodiments comprises the following steps:
the path planning module is used for acquiring a plurality of path points in the current planned path of the unmanned ship and determining a link line of any two adjacent path points;
the path recombination module is used for selecting any point on each link line as a new path point, determining the distance between the new path point and the obstacle, connecting the path length formed by the new path points on all adjacent link lines and the corner at each new path point;
the multi-objective path optimization module determines a path safety coefficient based on the distance of the obstacles, wherein the path safety coefficient is smaller when the distance of the obstacles is larger; determining path smoothness based on the corners, constructing a fitness function based on the path safety coefficient, the path length and the path smoothness, and determining new path point coordinates on each link line when the fitness function is minimum.
Based on the same concept, an embodiment of the present invention further provides an entity structure schematic diagram, as shown in fig. 5, the server may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform the steps of the hybrid particle swarm algorithm based unmanned ship path optimization method as described in the various embodiments above. Examples include:
step S1, reselecting a plurality of new path points based on the current planned path of the unmanned ship, wherein the current planned path comprises a plurality of path segments which are sequentially connected, and each new path point is positioned on one path segment;
step S2, constructing a fitness function by using the path length minimum formed by connecting the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of each new path point as a second objective function, and using the distance average value minimum of each new path point from an obstacle as a third objective function;
and step S3, determining the adaptive value of each particle in the mixed particle algorithm by the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Based on the same concept, the embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program, where the computer program includes at least one code, and the at least one code is executable by a master control device to control the master control device to implement the steps of the hybrid particle swarm algorithm-based unmanned ship path optimization method according to the embodiments. Examples include:
step S1, reselecting a plurality of new path points based on the current planned path of the unmanned ship, wherein the current planned path comprises a plurality of path segments which are sequentially connected, and each new path point is positioned on one path segment;
step S2, constructing a fitness function by using the path length minimum formed by connecting the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of each new path point as a second objective function, and using the distance average value minimum of each new path point from an obstacle as a third objective function;
and step S3, determining the adaptive value of each particle in the mixed particle algorithm by the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
Based on the same technical concept, the embodiment of the present application further provides a computer program, which is used to implement the above method embodiment when the computer program is executed by the main control device.
The program may be stored in whole or in part on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.
Based on the same technical concept, the embodiment of the present application further provides a processor, and the processor is configured to implement the above method embodiment. The processor may be a chip.
In summary, the unmanned ship path optimization method and system based on the hybrid particle swarm algorithm provided by the embodiments of the present invention take each path point in the current planned path of the unmanned ship as a basis, consider the multiple point constraints and the natural condition constraints such as obstacle avoidance, etc. at the same time, re-divide the path points, and comprehensively consider the path length, the path smoothness and the path security, thereby realizing the multi-objective optimization of the path length, the path smoothness and the path security, and thus planning a global path that conforms to the actual navigation of the unmanned ship.
The embodiments of the present invention can be arbitrarily combined to achieve different technical effects.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid state disk), among others.
One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the above method embodiments. And the aforementioned storage medium includes: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A unmanned ship path optimization method based on a hybrid particle swarm algorithm is characterized by comprising the following steps:
step S1, reselecting a plurality of new path points based on the current planned path of the unmanned ship, wherein the current planned path comprises a plurality of path segments which are sequentially connected, and each new path point is positioned on one path segment;
step S2, constructing a fitness function by using the path length minimum formed by connecting the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of each new path point as a second objective function, and using the distance average value minimum of each new path point from an obstacle as a third objective function;
and step S3, determining the adaptive value of each particle in the mixed particle algorithm by the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
2. The unmanned ship path optimization method based on hybrid particle swarm optimization according to claim 1, wherein the step S1 specifically comprises:
the optimal path points searched on the linked graph based on the Dijkstra algorithm are sequentially P0,P1,P2,…,Pn,Pn+11; wherein, P0As a starting point, Pn+1Is the target point; the link line of the path point is L in sequencei(i＝1，2，…，n)；
Determining a link line LiThe expression for all points:
Pi(hi)＝Pi (0)+(Pi (1)-Pi (0))×hi
in the above formula, hiIs a proportionality coefficient of hi∈[0,1](ii) a d is the number of the link lines;
and
are respectively a link line LiTwo end points of (a).
3. The unmanned ship path optimization method based on hybrid particle swarm optimization according to claim 2, wherein in step S2, the path length formed after connecting the new path points on all adjacent links is:
in the above formula, diRepresents a link line LiNew path point P ofiAnd link line Li+1New path point P ofi+1Path segment length between; (x)i,yi) Is a new path point PiThe coordinates of (a).
4. The hybrid particle swarm algorithm-based unmanned ship path optimization method according to claim 3, wherein in the step S2, the corner at each new path point is:
in the above formula, the first and second carbon atoms are,
indicating a new path point Pi-1To a new path point PiVector of (c), -Pi-1PiL represents the length of the vector;
in step S3, the path smoothness is determined based on the corner average at each new path point as:
in the above formula, k is a penalty coefficient, and k is alphaiIn (d) is greater than or equal to pi/2.
5. The unmanned ship path optimization method based on hybrid particle swarm optimization according to claim 4, wherein in step S2, the shortest distance between the new path point and the obstacle is:
di＝min{PiPi (0),PiPi (1)}
in step S3, the distance average of the obstacles is:
in step S3, the path security coefficient is:
in the above formula, λ is a weight adjustment coefficient, and k is the number of new waypoints having a shortest distance to the obstacle of 0.
6. The unmanned ship path optimization method based on hybrid particle swarm optimization according to claim 5, wherein in step S3, the fitness function is:
FitV＝ω1*f1+ω2*f2+ω3*f3
in the above formula, f1The path length formed after the new path points on all the adjacent link lines; f. of2Is the path smoothness; f. of3Is a path security coefficient; omega1、ω2、ω3Are respectively a weight coefficient, omega1+ω2+ω3＝1。
7. The unmanned ship path optimization method based on hybrid particle swarm optimization according to claim 6, wherein the step S3 specifically comprises:
step S31, initializing parameters in the hybrid particle swarm algorithm, and randomly setting the speed and the position of each particle;
step S32, evaluating the fitness of each particle, and storing the position and fitness value of each particle in the extreme value p of each particlebestAnd all p are substitutedbestThe individual position of the optimum adaptation value and the adaptation value in (b) are saved to the global extremum gbestPerforming the following steps;
step S33, determining initial temperature t0；
Step S34, determining the particle p under the current temperature according to the fitness functioniAn adaptation value of;
step S35, betting on roulette from all piTo determine a global optimum pgP 'as substitute'g；
Step S36, updating the speed and position of each particle;
step S37, calculating the adaptive value of each particle, and updating pbestAnd gbestAnd carrying out annealing operation;
and step S38, if the preset stop condition is judged to be reached, stopping searching and outputting the optimal value of the particle swarm, otherwise, returning to the step S34.
8. An unmanned ship path optimization system based on a hybrid particle swarm algorithm is characterized by comprising:
the route planning module reselects a plurality of new route points based on a current planned route of the unmanned ship, wherein the current planned route comprises a plurality of route sections which are sequentially connected, and each new route point is positioned on one route section;
the path optimization module is used for constructing a fitness function by using the path length minimum formed by connecting all the new path points in all the path sections in sequence as a first objective function, using the smoothness average value minimum of all the new path points as a second objective function and using the distance average value minimum of all the new path points from an obstacle as a third objective function;
and the mixed particle swarm solving module is used for determining the adaptive value of each particle in the mixed particle algorithm according to the fitness function, iteratively updating the adaptive value of each particle based on the simulated annealing algorithm, storing the optimal value of each particle and the optimal value of the particle swarm, and determining the new path point coordinate on each link line when the fitness function is minimum.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps of the hybrid particle swarm algorithm based unmanned ship path optimization method according to any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the hybrid particle swarm algorithm based unmanned ship path optimization method according to any one of claims 1 to 7.
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