CN117146833B - Unmanned aerial vehicle path planning method and device based on improved bat algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle path planning method and device based on an improved bat algorithm, wherein the method comprises the following steps: initializing bat algorithm model parameters of the unmanned aerial vehicle by combining an initial position, a target position and an adaptability function of the unmanned aerial vehicle; iterative updating is carried out based on the bat algorithm model, a manual bee colony algorithm is introduced to change the position of the unmanned aerial vehicle in time, and an updated fitness function value is determined; judging whether the iteration stopping condition is met, if so, stopping iteration, and planning an optimal path from the initial position to the target position. According to the technical scheme, the bats algorithm is improved by using the artificial bee colony algorithm, and when the bats algorithm is in local optimum, a new path is searched by using a mutation mechanism of the artificial bee colony algorithm to replace an old path, so that a feasible, safe and effective unmanned aerial vehicle flight path is planned.
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 an improved bat algorithm.
Background
Unmanned Aerial Vehicles (UAVs) are a modern type of avionics equipment in that they 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. Some intelligent algorithms have been applied in this problem, such as Bat Algorithm (BA), artificial bee colony Algorithm (ABC), genetic Algorithm (GA), ant colony Algorithm (ACO), artificial Neural Network (ANN), etc.
The bat algorithm was proposed by x.s. yang in 2010, and resulted from the simulation of searching and predating food processes by using the principle of echolocation for bats in nature. During food searching, the bat emits ultrasonic pulses, and the pulse intensity is the largest, so that the ultrasonic waves can be transmitted for a longer distance. During the process of flying to the prey, the pulse intensity is gradually reduced, and the pulse frequency is gradually increased, so that the bat can acquire the position of the food more accurately.
In the process of planning a flight path, the bat algorithm lacks a mutation mechanism and is easy to sink into local optimum, so that the population loses subsequent evolutionary capacity. Patent CN201910854519.9 introduces a reinforcement learning method into the bat algorithm, but still fails to address the problem of local optimality.
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 an unmanned aerial vehicle path planning method and apparatus based on an improved bat algorithm that overcomes or at least partially solves the above problems.
According to one aspect of the present invention, there is provided a method of unmanned aerial vehicle path planning based on an improved bat algorithm, the method comprising:
initializing bat algorithm model parameters of the unmanned aerial vehicle by combining an initial position, a target position and an adaptability function of the unmanned aerial vehicle;
iterative updating is carried out based on the bat algorithm model, a manual bee colony algorithm is introduced to change the position of the unmanned aerial vehicle in time, and an updated fitness function value is determined;
judging whether the iteration stopping condition is met, if so, stopping iteration, and planning an optimal path from the initial position to the target position.
In some embodiments, performing iterative update based on the bat algorithm model, introducing a manual bee colony algorithm to change the position of the unmanned aerial vehicle in time, and determining the updated fitness function value comprises:
selecting a first part of bat with the fitness lower than a preset value as employment bees, and selecting the rest of bat with the second part as sightseeing bees;
updating the position of the employing bees with speed, frequency and loudness values in a bat algorithm model;
selecting an object followed by the sightseeing bee from the employed bees in a roulette manner, and randomly selecting nodes needing to be changed;
and changing the position of the sightseeing bee by using the mutation parameters, and then updating the corresponding fitness function value according to the updated position.
In some embodiments, altering the location of the sightseeing bee using the mutation parameter, and further updating the corresponding fitness function value according to the location comprises:
determining the updated position of the sightseeing bee based on the node position to be changed, mutation parameters and the position of the selected employment bee;
and calculating an fitness function value based on the updated position, and if the fitness function value is smaller than the fitness function value before updating, reserving a path corresponding to the fitness function value after updating.
In some embodiments, the method further comprises:
if the position of the employed bees does not change within a preset time, a random position is generated and the corresponding speed, frequency and loudness are initialized.
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, climate threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost.
In some implementations, initializing the batout algorithm model parameters of the drone includes:
the speed, frequency, and loudness of each bat in the bat population is initialized and a location random solution is generated.
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 an improved bat algorithm, the apparatus comprising:
the initialization module is used for initializing bat algorithm model parameters of the unmanned aerial vehicle by combining the initial position, the target position and the fitness function of the unmanned aerial vehicle;
the updating module is suitable for carrying out iterative updating based on the bat algorithm model, introducing a manual bee colony algorithm to change the position of the unmanned aerial vehicle in time, and determining an updated fitness function value;
and the planning module is suitable for judging whether the iteration stopping condition is met, and stopping iteration if the iteration stopping condition is met, so that an optimal path from the initial position to the target position is planned.
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 improved bat algorithm based unmanned aerial vehicle path planning method according to any of the above embodiments.
According to yet another 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 the unmanned aerial vehicle path planning method according to any of the above based on an improved bat algorithm.
As can be seen from the above, the technical scheme disclosed by the invention utilizes the artificial bee colony algorithm to improve the bat algorithm, and when the bat is in local optimum, the artificial bee colony algorithm is utilized to find a new path to replace an old path, so that a feasible, safe and effective flight path is planned.
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 of unmanned aerial vehicle path planning based on an improved bat algorithm, according to one embodiment of the invention;
fig. 2 shows a schematic structural diagram of an unmanned aerial vehicle path planning apparatus based on an improved bat algorithm according to one embodiment of the present invention;
fig. 3 shows a schematic structural view of the unmanned aerial vehicle (control section) according to an embodiment of the present 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 an improved bat algorithm, according to one embodiment of the invention, the method comprising the steps of:
step S110, initializing bat algorithm model parameters of the unmanned aerial vehicle by combining the initial position, the target position and the fitness function of the unmanned aerial vehicle.
The present embodiment idealizes some of the echo characteristics of the bat when applying the bat algorithm, and follows the law: (1) All bats only use echo positioning to sense the distance and then find the target and avoid the obstacle; (2) Bats automatically adjust the wavelength and frequency of their emitted pulses when preying on prey on items, which are in position X i Randomly fly at speed V i Fixed frequency f min Loudness A 0 And continuously adjusting pulse transmission frequency r E [0,1 ] according to the distance from the target]The method comprises the steps of carrying out a first treatment on the surface of the (3) Bat loudness is from minimum constant value A min To A 0 And not equal. Model parameters include bat population number N P Maximum number of iterations T max Initializing loudness A 0 Initial velocity V 0 Frequency r, constants α, γ, and generate a counter t=1 for each bat, calculate fitness f (X) for each bat i ) The iterative process according to the bat algorithm can be found in CN201910854519.9 et al.
And step S120, carrying out iterative updating based on the bat algorithm model, introducing a manual bee colony algorithm to change the positions of the unmanned aerial vehicles in time, and then calculating corresponding fitness function values according to the positions of the unmanned aerial vehicles.
The step integrates the artificial bee colony algorithm into the bat algorithm, and particularly utilizes the idea of mutation or variation in the artificial bee colony algorithm. The employment bees in nature are mainly responsible for collecting honey source information, and the sightseeing bees judge the nectar information shared by the employment bees by watching the swing dances of the companion performances and decide which employment bees to follow to collect honey, and the scout bees randomly find new honey sources.
The artificial bee colony algorithm in the embodiment mainly comprises three steps, namely, bees randomly search for honey sources, bees with high honey source quality (low fitness function value) are called employment bees, and bees with low honey source quality are called sightseeing bees; then, looking for bees to select following employment bees, searching better honey sources nearby the honey sources together, and if so, replacing the original honey sources, otherwise, keeping the original honey sources unchanged; finally, if the honey source does not improve over a period of time, employment of bees becomes a scout bee, searching for the honey source randomly to replace the original honey source.
The step is mainly to change the local position of the bat by using the mutation characteristic of the artificial bee colony algorithm so as to avoid the bat from sinking into local optimum.
Step S130, judging whether the iteration stop condition is met, and stopping iteration if the iteration stop condition is met, so that an optimal path from the initial position to the target position is planned.
The iteration stop condition may be the maximum number of iterations, or may define an optimal value of the fitness.
According to the technical scheme of the embodiment, the bat algorithm is improved by using the artificial bee colony algorithm, and when the bat algorithm falls into local optimum, a new path is searched for to replace an old path by using a mutation step in the artificial bee colony algorithm, so that a feasible, safe and effective flight path is planned.
In some embodiments, iteratively updating based on the bat algorithm model and introducing the artificial bee colony algorithm to change the position of the unmanned aerial vehicle in time, and determining the updated fitness function value comprises:
selecting a first part of bat with the fitness lower than a preset value as employment bees, and selecting the rest of bat with the second part as sightseeing bees; updating the position of the employing bees with speed, frequency and loudness values in a bat algorithm model; selecting an object followed by the sightseeing bee from the employed bees in a roulette manner, and randomly selecting nodes needing to be changed; and changing the position of the sightseeing bee by using the mutation parameters, and further updating the corresponding fitness function value according to the position.
Specifically, a first portion of the less adaptable bat may be selected as the employment bee, with the remainder being the sightseeing bee. The information updating method of the position of the employment bees is based on the conventional bat algorithm, namely, the position of the employment bees is updated through speed, frequency, loudness, fitness and the like.
Regarding the update method of the sightseeing bees, firstly, the employment bees which the sightseeing bees follow are determined, for example, the employment bees can be selected through roulette:
in the above formula, p (i) is the probability of selecting the employment bee (position) with the sequence number of i for the sightseeing bee, N e To employ the total number of bees, k is the number of bees employed, f (x) is the fitness of the individual bees employed, the highest probability of employing a bee as the follow-up and placement can be selectedThe subject of the exchange, i.e. the location of the employment bee, is selected as the location of the sightseeing bee.
When updating the position, a mutation parameter F is introduced to enhance the local search capability of the algorithm, and only a certain node (other nodes than the starting position and the end position) of a certain sightseeing bee is changed at a time.
Wherein i represents the i th sightseeing bee, X i,j Is the j node position vector of the i sightseeing bee. The random numbers r1, r2, r3 are the different bee hiring serial numbers.
Then, the fitness function value is updated based on the positions of the sightseeing bees and the employment bees, and the path corresponding to the best fitness function value is selected to replace the original path.
Thus, in some embodiments, altering the location of the sightseeing bee using the mutation parameter, and further updating the corresponding fitness function value according to the location comprises:
determining the updated position of the sightseeing bee based on the node position to be changed, mutation parameters and the position of the selected employment bee;
and calculating an fitness function value based on the updated position, and if the fitness function value is smaller than the fitness function value before updating, reserving a path corresponding to the fitness function value after updating.
It should be noted that the updated locations include the location of the employment bees and the location of the sightseeing bees.
In some embodiments, the method further comprises:
if the position of the employment bee does not change within a preset time, a random position is generated and the speed, frequency and loudness of the corresponding employment bee is initialized.
The above operation corresponds to the third step of the artificial bee colony algorithm, namely, if the honey source is not improved for a period of time, the employment of bees becomes a scout bee, and the honey source is searched randomly to replace the original honey source. The method of randomly selecting the nodes may employ existing methods such as roulette.
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, climate threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost.
Specifically, in order to increase the iterative speed of the algorithm, particularly for sightseeing bees corresponding to abrupt nodes, only a certain cost, such as path cost, around the replacement point may be calculated.
In some embodiments, initializing the batout algorithm model parameters of the drone includes:
the speed, frequency, and loudness of each bat in the population are initialized and a location randomization solution is generated.
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.
In accordance with another aspect of the present invention, referring to fig. 2, there is provided an unmanned aerial vehicle path planning apparatus based on an improved bat algorithm, the apparatus 200 comprising:
an initialization module 210 for initializing bat algorithm model parameters of the unmanned aerial vehicle in combination with an initial position, a target position and an fitness function of the unmanned aerial vehicle;
the updating module 220 is adapted to perform iterative updating based on the bat algorithm model, introduce a manual bee colony algorithm to change the position of the unmanned aerial vehicle in time, and determine an updated fitness function value;
the planning module 230 is adapted to determine whether an iteration stop condition is satisfied, and if so, stop the iteration, thereby planning an optimal path from the initial position to the target position.
In some embodiments, the update module 220 is adapted to:
selecting a first part of bat with the fitness lower than a preset value as employment bees, and selecting the rest of bat with the second part as sightseeing bees;
updating the position of the employing bees with speed, frequency and loudness values in a bat algorithm model;
selecting an object followed by the sightseeing bee from the employed bees in a roulette manner, and randomly selecting nodes needing to be changed;
and changing the position of the sightseeing bee by using the mutation parameters, and then updating the corresponding fitness function value according to the updated position.
In some embodiments, the updating module 220 uses mutation parameters to change the position of the sightseeing bee, and further updates the corresponding fitness function value according to the position includes:
determining the updated position of the sightseeing bee based on the node position to be changed, mutation parameters and the position of the selected employment bee;
and calculating an fitness function value based on the updated position, and if the fitness function value is smaller than the fitness function value before updating, reserving a path corresponding to the fitness function value after updating.
In some embodiments, the apparatus 200 is further adapted to:
if the position of the employed bees does not change within a preset time, a random position is generated and the corresponding speed, frequency and loudness are initialized.
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, climate threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost.
In some embodiments, the bat algorithm model parameters of the initializing drone in the initializing device 210 are adapted to:
the speed, frequency, and loudness of each bat in the population are initialized and a location randomization solution is generated.
In some embodiments, the apparatus 200 is further adapted to:
and smoothing the optimal path by using a B-spline difference curve.
In summary, the invention provides a method and a device for solving unmanned aerial vehicle path planning, which mainly combine the characteristics of bat algorithm and artificial bee colony algorithm to achieve the purposes of improving local searching capability, obtaining collision-free, safer and shorter flight path, and provide a brand new view angle for solving unmanned aerial vehicle path planning.
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 a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in an unmanned aerial vehicle path planning apparatus based on the improved bat algorithm according to an embodiment of the present invention. 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 improved bat 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 perform relevant steps in the above-described unmanned aerial vehicle path planning method embodiment for an electronic device based on the improved bat 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 included in the electronic device 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 embodiments of the unmanned aerial vehicle path planning method based on the improved bat algorithm.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, 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 application 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 an improved bat algorithm, the method comprising:
initializing bat algorithm model parameters of the unmanned aerial vehicle by combining an initial position, a target position and an adaptability function of the unmanned aerial vehicle;
iterative updating is carried out based on the bat algorithm model, a manual bee colony algorithm is introduced to change the position of the unmanned aerial vehicle in time, and an updated fitness function value is determined;
judging whether an iteration stopping condition is met, if so, stopping iteration, and planning an optimal path from an initial position to a target position;
the method comprises the steps of carrying out iterative updating based on a bat algorithm model, introducing a manual bee colony algorithm to timely change the position of the unmanned aerial vehicle, and determining an updated fitness function value, wherein the steps comprise:
selecting a first part of bat with the fitness lower than a preset value as employment bees, and selecting the rest of bat with the second part as sightseeing bees;
updating the position of the employing bees with speed, frequency and loudness values in a bat algorithm model;
selecting an object followed by the sightseeing bee from the employed bees in a roulette manner, and randomly selecting nodes needing to be changed;
changing the position of the sightseeing bee by using mutation parameters, and then updating the corresponding fitness function value according to the updated position;
the above-mentioned changing the position of the sightseeing bee by using the mutation parameter, and further updating the corresponding fitness function value according to the position includes:
determining the updated position of the sightseeing bee based on the node position to be changed, mutation parameters and the position of the selected employment bee;
calculating an fitness function value based on the updated position, and if the fitness function value is smaller than the fitness function value before updating, reserving a path corresponding to the fitness function value after updating;
and, the method further comprises:
if the position of the employment bee does not change within the preset time, a random position is generated and the corresponding speed, frequency and loudness are reinitialized.
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, climate threat cost, terrain threat cost, maximum climb angle cost, maximum altitude cost.
3. The method of claim 1, wherein initializing bat algorithm model parameters of the unmanned aerial vehicle comprises:
the speed, frequency, and loudness of each bat in the bat population is initialized and a location random solution is generated.
4. A method according to any one of claims 1-3, characterized in that the method further comprises:
and smoothing the optimal path by using a B-spline difference curve.
5. An unmanned aerial vehicle path planning apparatus based on an improved bat algorithm, the apparatus comprising:
the initialization module is used for initializing bat algorithm model parameters of the unmanned aerial vehicle by combining the initial position, the target position and the fitness function of the unmanned aerial vehicle;
the updating module is suitable for carrying out iterative updating based on the bat algorithm model, introducing a manual bee colony algorithm to change the position of the unmanned aerial vehicle in time, and determining an updated fitness function value;
the planning module is suitable for judging whether the iteration stopping condition is met, and stopping iteration if the iteration stopping condition is met, so that an optimal path from the initial position to the target position is planned;
wherein the update module is adapted to:
selecting a first part of bat with the fitness lower than a preset value as employment bees, and selecting the rest of bat with the second part as sightseeing bees;
updating the position of the employing bees with speed, frequency and loudness values in a bat algorithm model;
selecting an object followed by the sightseeing bee from the employed bees in a roulette manner, and randomly selecting nodes needing to be changed;
changing the position of the sightseeing bee by using mutation parameters, and then updating the corresponding fitness function value according to the updated position;
the updating module changes the position of the sightseeing bee by using mutation parameters, and then updates the corresponding fitness function value according to the position specifically comprises the following steps:
determining the updated position of the sightseeing bee based on the node position to be changed, mutation parameters and the position of the selected employment bee;
calculating an fitness function value based on the updated position, and if the fitness function value is smaller than the fitness function value before updating, reserving a path corresponding to the fitness function value after updating;
and, the apparatus is further adapted to:
if the position of the employed bees does not change within a preset time, a random position is generated and the corresponding speed, frequency and loudness are initialized.
6. A drone comprising a processor and a memory arranged to store computer executable instructions which when executed cause the processor to perform the improved bat algorithm-based drone path planning method 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 improved bat algorithm-based unmanned aerial vehicle path planning method of any of claims 1-4.
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