CN117170413B - Unmanned aerial vehicle path planning method and device based on modified sine and cosine algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle path planning method and device based on a modified sine and cosine algorithm, wherein the method comprises the following steps: constructing a starting point, a middle node, an end point and an adaptability function of the unmanned aerial vehicle, and initializing a candidate solution of the unmanned aerial vehicle based on a chaotic logic diagram; iteratively updating the node positions of each unmanned aerial vehicle based on a sine and cosine algorithm, simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, comparing the fitness function values before and after updating, and reserving a larger fitness function value; judging whether iteration stop conditions are met, and determining a path corresponding to the maximum fitness function value as an optimal planning path when the iteration stop conditions are met. According to the scheme, the convergence speed and quality of the final solution are improved by optimizing the unmanned aerial vehicle cluster initialization.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle path planning method and device based on a modified sine and cosine algorithm.
Background
Unmanned Aerial Vehicle (UAV) is a modern avionics equipment, which is one of the potential necessary trends in future warfare, as it can perform dangerous, repetitive tasks in remote and hazardous environments. The objective of the unmanned aerial vehicle routing problem is to find an optimal or near optimal flight path with minimal threat costs between the initial location and the desired destination, while satisfying certain constraints. In recent years, unmanned aerial vehicle route planning problems are widely studied in the military and civil fields. Part of intelligent algorithms have been applied in this problem, for example CN115016499a discloses a path planning method based on SCA-QL, in which a solution of unmanned aerial vehicle optimal solution is solved by combining a modified sine and cosine algorithm with other algorithms, but the above algorithm still has the problems of slow convergence speed during iteration and easy sinking into local optimal solution.
Disclosure of Invention
The present invention has been made in view of the above problems, and it is an object of the present invention to provide a method and apparatus for unmanned aerial vehicle path planning based on a modified sine and cosine algorithm that overcomes or at least partially solves the above problems.
According to one aspect of the invention, there is provided an unmanned aerial vehicle path planning method based on a modified sine and cosine algorithm, the method comprising:
constructing a starting point, a middle node, an end point and an adaptability function of the unmanned aerial vehicle, and initializing a candidate solution of the unmanned aerial vehicle based on a chaotic logic diagram;
iteratively updating the node positions of each unmanned aerial vehicle based on a sine and cosine algorithm, simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, comparing the fitness function values before and after updating, and reserving a larger fitness function value;
judging whether iteration stop conditions are met, and determining a path corresponding to the maximum fitness function value as an optimal planning path when the iteration stop conditions are met.
In some embodiments, initializing the candidate solution for the drone based on the chaotic logic diagram includes:
generating a chaotic variable vector based on a chaotic logic diagram formula; let y be j And if the j-th chaotic variable is a bifurcation coefficient, the formula of the chaotic logic diagram is as follows:
repeating the steps to generate a corresponding chaotic variable vector for each candidate solution;
and initializing the candidate solution based on the chaos variable vector and the upper and lower boundaries of the corresponding variable.
In some embodiments, the method further comprises:
the iteration formula of the sine and cosine algorithm comprises a control parameter r 1 Will be r as follows 1 The formula is replaced into an iterative formula of the sine and cosine algorithm:
wherein,custom parameters, t is the current iteration number, MAX iteration Maximum number of iterations>Is r 1 Is a maximum value of (a).
In some embodiments, the method further comprises:
introducing a convergence factor CF into an iteration formula of the sine and cosine algorithm, wherein the convergence factor CF is used for limiting an initial position in the iteration formula:
。
in some embodiments, the fitness function is the inverse of a composite cost comprising at least one of: path cost, obstacle threat cost, radar threat cost, missile threat cost, air defense threat cost, weather threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost, or unmanned aerial vehicle collision cost.
In some implementations, determining whether the iteration stop condition is met includes:
judging whether the iteration times are equal to the maximum iteration times or not, or judging whether the fitness function value is smaller than or equal to a preset threshold value, and stopping iteration if any one of the two judgment results is yes.
In some embodiments, the method further comprises:
and smoothing the optimal path by using a B-spline difference curve.
According to another aspect of the present invention, there is provided an unmanned aerial vehicle path planning apparatus based on a modified sine and cosine algorithm, the apparatus comprising:
the initialization module is suitable for constructing a starting point, a middle node, an end point and a fitness function of the unmanned aerial vehicle, and initializing candidate solutions of the unmanned aerial vehicle based on the chaotic logic diagram;
the iteration module is suitable for carrying out iteration update on the node positions of the unmanned aerial vehicles based on a sine and cosine algorithm, and simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, and reserving a larger fitness function value;
and the planning module is suitable for judging whether iteration stop conditions are met, and determining the path corresponding to the maximum fitness function value as the optimal planning path when the iteration stop conditions are met.
According to yet another aspect of the present invention, there is provided a unmanned aerial vehicle comprising: a processor and a memory arranged to store computer executable instructions that when executed cause the processor to perform the method of unmanned aerial vehicle path planning based on a modified sine and cosine algorithm according to any of the above embodiments.
According to a further aspect of the present invention, there is provided a computer readable storage medium storing one or more programs which, when executed by a processor, implement a method of unmanned aerial vehicle path planning based on a modified sine and cosine algorithm according to any of the above.
As can be seen from the above, according to the unmanned aerial vehicle path planning method based on the modified sine and cosine algorithm disclosed by the invention, firstly, the starting point, the intermediate node, the end point and the fitness function of the unmanned aerial vehicle are initialized or constructed, and the candidate solution of the unmanned aerial vehicle is initialized based on the chaotic logic diagram; then, carrying out iterative updating on the node positions of all unmanned aerial vehicles based on a sine and cosine algorithm, simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, comparing fitness function values before and after updating, and reserving larger fitness function values; and finally, judging whether an iteration stop condition is met, and determining the path corresponding to the maximum fitness function value as the optimal planning path when the iteration stop condition is met. According to the scheme, population initialization based on chaos is achieved, so that better consistency is obtained, an optimal path can be generated for the unmanned aerial vehicle more accurately, and convergence speed is high.
Further, the present invention also uses a step size that is non-linearly decreasing to balance the local and global searches; in addition, the convergence factor is increased, the convergence speed of the SCA is improved, and the optimal path can be generated for the unmanned aerial vehicle more accurately.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flow diagram of a method for unmanned aerial vehicle path planning based on a modified sine and cosine algorithm according to an embodiment of the present invention;
fig. 2 shows a schematic structural diagram of an unmanned aerial vehicle path planning device based on a modified sine and cosine algorithm according to an embodiment of the present invention;
fig. 3 shows a schematic structural view of a drone according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow diagram of a method for unmanned aerial vehicle path planning based on a modified sine and cosine algorithm, which can be implemented by unmanned aerial vehicles, according to an embodiment of the invention. The method comprises the following steps:
step S110, constructing or initializing a starting point, an intermediate node, an end point and a fitness function of the unmanned aerial vehicle, and initializing a candidate solution of the unmanned aerial vehicle based on a chaotic logic diagram;
step S120, carrying out iterative updating on the node positions of all unmanned aerial vehicles based on a sine and cosine algorithm, simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, comparing fitness function values before and after updating, and reserving larger fitness function values;
and step S130, judging whether an iteration stop condition is met, and determining the path corresponding to the maximum fitness function value as the optimal planning path when the iteration stop condition is met.
In iterative algorithms, population initialization plays a very important role in both the convergence speed and quality of the final solution. In general, random initialization is the most commonly used method of generating an initial population without any information about the candidate solution. The present invention uses a uniformly distributed random solution to initialize candidate solutions, and when the distribution is more uniform, the population remains rich in diversity, thereby increasing the chances of faster convergence and better solution quality. Therefore, the chaos-based initialization is favorable for maintaining better diversity among potential unmanned aerial vehicle group paths, has the advantage of being more uniformly distributed, can generate an optimal path for the unmanned aerial vehicle more accurately, and has high convergence speed.
In some embodiments, initializing the candidate solution for the drone based on the chaotic logic diagram includes:
generating a chaotic variable vector based on a chaotic logic diagram formula; let y be j And if the j-th chaotic variable is a bifurcation coefficient, the formula of the chaotic logic diagram is as follows:
if [ mu ] epsilon [3.57,4 ], a chaotic state occurs. When μ=4, the system generates a uniform version of the chaotic signal that can be used for initializing the candidate solution.
Repeating the steps to generate a corresponding chaotic variable vector for each candidate solution;
and initializing the candidate solution based on the chaos variable vector and the upper and lower boundaries of the corresponding variable.
Specifically, the initialization based on the chaotic logic diagram comprises the following steps:
first, step 1, set y 0 E (0, 1), and generates D chaotic variables (corresponding to the number of nodes) using the following formula, thereby forming a vector.
Wherein y is j Representing the j-th chaotic variable.
Then, step 2, step 1 is repeated to generate an initial chaotic variable for each candidate solution i, where i=1, 2, …, N (total number of candidate solutions).
Step 3, initializing the candidate solution based on the chaos variable vector and the upper and lower boundaries of the corresponding variable,
wherein x is max,j And x min,j The upper and lower bounds of the j-th node's location, respectively.
Through the steps, the i-th candidate solution is obtained as follows:
in some embodiments, the method further comprises:
the iteration formula of the sine and cosine algorithm comprises a control parameter r 1 Will be r as follows 1 The formula is replaced into an iterative formula of the sine and cosine algorithm:
wherein,is self-containedDefining parameters, t is the current iteration number, MAX iteration Maximum number of iterations>Is the maximum value of r 1.
In the t-th iteration formula of the sine and cosine algorithm,
r 1 for controlling parameters, the values of r2, r3 and r4 are random numbers of (0, 2 pi), (0, 1) and (0, 1) respectively,is the current optimal solution. Control parameter r in sine and cosine algorithm 1 Is a linear function that decreases linearly from β to 0, and because of its linearity, sometimes it may mutate when jumping from one iteration to the next, in some cases the mutation may result in skipping good solutions, and valuable information of the quality of the search area may be lost. Thus, the present invention proposes r 1 To exponentially decrease from γ to 0 to avoid the above-described problem.
In some embodiments, the method further comprises:
introducing a convergence factor CF into an iterative formula of the sine and cosine algorithm, wherein the convergence factor CF is multiplied by an initial position in the iterative formula:
the convergence factor CF is inversely proportional to the number of iterations, and the smaller the value thereof, the higher the value thereof, which corresponds to the smaller dependency on the current position, the more the current position is acted upon in determining the new position. The CF is larger at the beginning of the iteration, the search process is obviously guided by the current position, and in later iterations, when CF is smaller, its effect is smaller and the new position depends more on the global optimal solution.
The convergence factor CF described above thus contributes to rapid convergence.
According to the following formula, the convergence factor and the control parameter r are realized 1 Is combined with:
according to the formula, the step size r is nonlinearly decreased 1 The global exploration phase and the local development phase are well balanced, while the convergence factor contributes to a fast convergence. Therefore, the formula combines the two technologies, and cancels the absolute value term, so that better unmanned aerial vehicle group path planning performance is obtained in terms of solving quality, precision and convergence speed.
In some embodiments, the fitness function is the inverse of a composite cost comprising at least one of: path cost, obstacle threat cost, radar threat cost, missile threat cost, air defense threat cost, weather threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost, or unmanned aerial vehicle collision cost.
Specifically, to increase the iterative speed of the algorithm, one or both of the costs before and after the replacement node, such as the path cost, the obstacle threat cost, and the like, may also be calculated.
In some embodiments, determining whether the iteration stop condition is met comprises:
judging whether the iteration times are equal to the maximum iteration times or not, or judging whether the fitness function value is smaller than or equal to a preset threshold value, and stopping iteration if any one of the two judgment results is yes.
In some embodiments, the method further comprises:
and smoothing the optimal path by using a B-spline difference curve.
Specifically, a smooth path of a cubic B-spline interpolation curve can be adopted, a simple continuous analytical model is established for parameters by using known nodes through cubic spline interpolation, and the characteristics at non-observation points are estimated according to the model. B-splines are guided at nodes, with smoothness, as compared to piecewise linear interpolation. The purpose of applying the cubic B-spline interpolation curve to the smooth path is to reduce the dangerous coefficient in the turning process of the unmanned aerial vehicle, so that a safe and reliable path is obtained.
According to another aspect of the present invention, referring to fig. 2, there is provided an unmanned aerial vehicle path planning apparatus based on a modified sine and cosine algorithm, the apparatus 200 includes:
the initialization module 210 is adapted to construct a starting point, an intermediate node, an end point and an fitness function of the unmanned aerial vehicle, and initialize a candidate solution of the unmanned aerial vehicle based on the chaotic logic diagram;
the iteration module 220 is suitable for carrying out iteration update on the node positions of the unmanned aerial vehicles based on a sine and cosine algorithm, and simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, and reserving a larger fitness function value;
the planning module 230 is adapted to determine whether an iteration stop condition is satisfied, and determine that the path corresponding to the maximum fitness function value is the optimal planned path when the iteration stop condition is satisfied.
According to the scheme, population initialization based on chaos is achieved, so that better consistency is obtained, an optimal path can be generated for the unmanned aerial vehicle more accurately, and convergence speed is high.
In some embodiments, the initialization module 210 is further adapted to:
generating a chaotic variable vector based on a chaotic logic diagram formula; let y be j And if the j-th chaotic variable is a bifurcation coefficient, the formula of the chaotic logic diagram is as follows:
repeating the steps to generate a corresponding chaotic variable vector for each candidate solution;
and initializing the candidate solution based on the chaos variable vector and the upper and lower boundaries of the corresponding variable.
In some embodiments, the apparatus 200 is further adapted to:
in the iterative formula of the sine and cosine algorithmComprising a control parameter r 1 Will be r as follows 1 The formula is replaced into an iterative formula of the sine and cosine algorithm:
wherein,for the custom parameter, t is the current iteration number, MAX iteration Maximum number of iterations>Is r 1 Is a maximum value of (a).
In some embodiments, the apparatus 200 is further adapted to:
and introducing a convergence factor CF into an iteration formula of the sine and cosine algorithm, wherein the convergence factor CF is used for limiting an initial position in the iteration formula.
In some embodiments, the fitness function is the inverse of a composite cost comprising at least one of: path cost, obstacle threat cost, radar threat cost, missile threat cost, air defense threat cost, weather threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost, or unmanned aerial vehicle collision cost.
In some embodiments, the planning module 230 is adapted to:
judging whether the iteration times are equal to the maximum iteration times or not, or judging whether the fitness function value is smaller than or equal to a preset threshold value, and stopping iteration if any one of the two judgment results is yes.
In some embodiments, the apparatus 200 is further adapted to:
and smoothing the optimal path by using a B-spline difference curve.
It should be noted that, the specific implementation manner of each embodiment of the apparatus may be performed with reference to the specific implementation manner of the corresponding embodiment of the method, which is not described herein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may also be used with the teachings herein. The required structure for the construction of such devices is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a modified sine and cosine algorithm based unmanned aerial vehicle path planning apparatus according to an embodiment of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the unmanned aerial vehicle path planning method based on the modified sine and cosine algorithm in any of the method embodiments.
Fig. 3 shows a schematic structural diagram of an embodiment of the unmanned aerial vehicle according to the present invention, and the specific embodiment of the present invention is not limited to the specific structure of the unmanned aerial vehicle.
Preferably, the ZV10E vertical take-off and landing unmanned aerial vehicle is used for building a cluster, and the ZV10E vertical take-off and landing unmanned aerial vehicle is a high-end unmanned aerial vehicle product used for emergency department outburst, search and rescue, border monitoring, large-scale movable security inspection, oil pipeline inspection, electric power inspection, and other fields. The vertical take-off and landing unmanned aerial vehicle has the ultra-long endurance that current many rotor unmanned aerial vehicle does not possess. The unmanned aerial vehicle has the advantages of convenient use and maintenance, stable performance, low temperature resistance and other severe environments; compared with a fixed wing unmanned aerial vehicle, the product is convenient to operate, simple in take-off and landing, and can be widely applied to aspects of investigation and monitoring, traffic monitoring, power inspection, forest fire prevention and the like.
The ZV10E parameters are shown in the following table:
as shown in fig. 3, the unmanned aerial vehicle (particularly, the control section) may include: a processor (processor) 302, a communication interface (Communications Interface) 304, a memory (memory) 306, and a communication bus 308.
Wherein: processor 302, communication interface 304, and memory 306 perform communication with each other via communication bus 308. A communication interface 304 for communicating with network elements of other devices, such as clients or other servers. The processor 302 is configured to execute the program 310, and may specifically execute relevant steps in the embodiment of the method for planning a path of a drone based on the modified sine and cosine algorithm.
In particular, program 310 may include program code including computer-operating instructions.
The processor 302 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors comprised by the drone may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 306 for storing programs 310. Memory 306 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 310 may be specifically configured to cause the processor 302 to perform operations corresponding to the above-described embodiment of the unmanned aerial vehicle path planning method based on the modified sine and cosine algorithm.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (7)
1. An unmanned aerial vehicle path planning method based on a modified sine and cosine algorithm, the method comprising:
constructing a starting point, a middle node, an end point and an adaptability function of the unmanned aerial vehicle, and initializing a candidate solution of the unmanned aerial vehicle based on a chaotic logic diagram;
iteratively updating the node positions of each unmanned aerial vehicle based on a sine and cosine algorithm, simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, comparing the fitness function values before and after updating, and reserving a larger fitness function value;
judging whether iteration stop conditions are met, and determining a path corresponding to the maximum fitness function value as an optimal planning path when the iteration stop conditions are met;
initializing the candidate solution of the unmanned aerial vehicle based on the chaotic logic diagram comprises the following steps:
generating a chaotic variable vector based on a chaotic logic diagram formula; let y be j
As for the j-th chaotic variable, the [ mu ] is a bifurcation coefficient, and the formula of the chaotic logic diagram is as follows:
，
repeating the steps to generate a corresponding chaotic variable vector for each candidate solution;
initializing the candidate solution based on the chaotic variable vector and the upper and lower boundaries of the corresponding variable;
the method further comprises the steps of:
the iteration formula of the sine and cosine algorithm comprises a control parameter r 1 Will be r as follows 1 The formula is replaced into an iterative formula of the sine and cosine algorithm:
，
wherein,for the custom parameter, t is the current iteration number, MAX iteration Maximum number of iterations>Is r 1 Is the maximum value of (2);
the method further comprises the steps of:
introducing a convergence factor CF into an iteration formula of the sine and cosine algorithm, wherein the convergence factor CF is used for limiting an initial position in the iteration formula:
。
2. the method of claim 1, wherein the fitness function is the inverse of a composite cost, the composite cost comprising at least one of: path cost, obstacle threat cost, radar threat cost, missile threat cost, air defense threat cost, weather threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost, or unmanned aerial vehicle collision cost.
3. The method of claim 1, wherein determining whether an iteration stop condition is satisfied comprises:
judging whether the iteration times are equal to the maximum iteration times or not, or judging whether the fitness function value is smaller than or equal to a preset threshold value, and stopping iteration if any one of the two judgment results is yes.
4. A method according to any one of claims 1-3, characterized in that the method further comprises:
and smoothing the optimal planning path by using a B-spline interpolation curve.
5. An unmanned aerial vehicle path planning device based on a modified sine and cosine algorithm, the device comprising:
the initialization module is suitable for constructing a starting point, a middle node, an end point and a fitness function of the unmanned aerial vehicle, and initializing candidate solutions of the unmanned aerial vehicle based on the chaotic logic diagram;
the iteration module is suitable for carrying out iteration update on the node positions of the unmanned aerial vehicles based on a sine and cosine algorithm, and simultaneously updating and calculating the fitness function of each unmanned aerial vehicle, and reserving a larger fitness function value;
the planning module is suitable for judging whether iteration stop conditions are met, and determining a path corresponding to the maximum fitness function value as an optimal planning path when the iteration stop conditions are met;
the initialization module is further adapted to:
generating a chaotic variable vector based on a chaotic logic diagram formula; let y be j And if the j-th chaotic variable is a bifurcation coefficient, the formula of the chaotic logic diagram is as follows:
，
repeating the steps to generate a corresponding chaotic variable vector for each candidate solution;
initializing the candidate solution based on the chaotic variable vector and the upper and lower boundaries of the corresponding variable;
the device is further adapted to:
the iteration formula of the sine and cosine algorithm comprises a control parameter r 1 Will be r as follows 1 The formula is replaced into an iterative formula of the sine and cosine algorithm:
，
wherein,for the custom parameter, t is the current iteration number, MAX iteration Maximum number of iterations>Is r 1 Is the maximum value of (2);
introducing a convergence factor CF into an iteration formula of the sine and cosine algorithm, wherein the convergence factor CF is used for limiting an initial position in the iteration formula:
。
6. a drone comprising a processor and a memory arranged to store computer executable instructions which, when executed, cause the processor to perform a method of drone path planning based on a modified sine and cosine algorithm according to any one of claims 1 to 4.
7. A computer readable storage medium storing one or more programs which, when executed by a processor, implement the modified sine and cosine algorithm based unmanned aerial vehicle path planning method according to any of claims 1 to 4.
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