CN114485699B - Self-adaptive path optimization method and system for unmanned aerial vehicle self-organizing network - Google Patents
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Abstract
The disclosure provides a self-adaptive path optimization method and system for an unmanned aerial vehicle self-organizing network, comprising the following steps: constructing a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position; according to the constructed mathematical model, utilizing an improved intelligent water drop algorithm to carry out optimization solution on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, and generating an optimal search path; the improved intelligent water drop algorithm introduces a alliance game strategy to enable the unmanned aerial vehicle to achieve alliance, water drops in the same path of the unmanned aerial vehicle cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of water drops is determined through the soil quantity in the water drops. The scheme is based on an improved intelligent water drop algorithm, and by introducing a alliance game strategy and a soil updating mechanism, the optimization efficiency and accuracy of the unmanned aerial vehicle search path are effectively improved.
Description
Technical Field
The disclosure belongs to the technical field of mobile ad hoc networks (Mobile Ad Hoc Networks, MANET), and particularly relates to an unmanned aerial vehicle ad hoc network self-adaptive path optimization method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, unmanned Aerial Vehicles (UAVs) have found increasing use in military and civilian applications, including battlefield surveillance, airborne fueling, environmental monitoring, and mapping. Typical applications of unmanned aerial vehicles are target searches such as border patrol, mine clearance and perimeter surveillance. These tasks are often urgent, involve a wide range of collaboration, requiring a team of unmanned aerial vehicles, also known as multi-unmanned aerial vehicle (multi-UAV) collaborative search.
The target path planning technology is important to improving the task efficiency of unmanned plane teams. The method aims to find an optimal cooperation path for the unmanned aerial vehicle by utilizing an airborne sensor platform under certain resource and time constraint. The collaborative planning path of the unmanned aerial vehicles is an important content of the unmanned aerial vehicles for jointly carrying out tasks, under the conditions of heavy and time-tight tasks in a wide search area, a single unmanned aerial vehicle obviously cannot rapidly complete the search tasks, the unmanned aerial vehicles can simultaneously carry out search, the search tasks can be efficiently completed, and when the single unmanned aerial vehicle executes the tasks, the unmanned aerial vehicle breaks down and the whole search task is stopped when the energy or battery supply is insufficient. When a plurality of unmanned aerial vehicles execute tasks and one of the unmanned aerial vehicles has the phenomenon, the influence on the whole searching task is not great. However, the phenomena of path overlapping, blind searching and collision may occur during searching of multiple unmanned aerial vehicles, which seriously affects the searching efficiency and even causes the safety problem of the searching process.
The inventor finds that the existing target search algorithm has the following problems:
(1) In optimizing the path, a lot of resources and time are required;
(2) The updating of the path information is not timely, the path from the unmanned aerial vehicle group to the search target is about to be invalid, but no new path reaches the search target at the moment, the unmanned aerial vehicle group needs to wait for the reestablishment of the target path, and the data information can be delayed to be sent and even lost;
(3) Lack of cooperation between unmanned aerial vehicles, all unmanned aerial vehicles are independent. When the target is required to be searched, the target is searched only according to the condition of the target to establish a path, and the searching and the path overlapping can be repeated, so that the time for reaching the target position is seriously influenced.
Disclosure of Invention
In order to solve the problems, the disclosure provides an unmanned aerial vehicle self-organizing network self-adaptive path optimization method and system, and the scheme is based on an improved intelligent water drop algorithm, and by introducing a alliance game strategy and a soil updating mechanism, the optimization efficiency and accuracy of an unmanned aerial vehicle searching path are effectively improved.
According to a first aspect of an embodiment of the present disclosure, there is provided a method for optimizing an adaptive path of an unmanned aerial vehicle self-organizing network, including:
constructing a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
according to the constructed mathematical model, utilizing an improved intelligent water drop algorithm to carry out optimization solution on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, and generating an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable the unmanned aerial vehicle to achieve alliance, water drops in the same path of the unmanned aerial vehicle cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of water drops is determined through the soil quantity in the water drops.
Further, the mathematical model is constructed specifically as follows: and enabling the water drops in the intelligent water drop algorithm to correspond to single unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, enabling the lake/ocean to correspond to a known target position in a search area, and enabling the river to represent a path which is searched by the unmanned aerial vehicle group and reaches the target position.
Further, when evaluating whether the unmanned aerial vehicle with two points can achieve the alliance, the improved intelligent water drop algorithm judges by calculating the probability value that the current position of the water drop moves to other adjacent positions, and selects the adjacent position with the largest probability value to achieve the alliance with the current position of the water drop.
Further, the improved path optimization process of the intelligent water drop algorithm specifically comprises the following steps:
based on the alliance game strategy, the unmanned aerial vehicle in the unmanned aerial vehicle self-organizing network achieves alliance;
constructing a search path and continuously updating path information;
the path with the highest probability value is selected as the optimal path.
Further, the construction of the search path and the continuous updating of the path information are specifically as follows: the unmanned aerial vehicle is simulated to construct a path through water drops, and meanwhile, in order to ensure that the unmanned aerial vehicle continuously obtains updated path information, an information transmission mechanism is adopted to transmit relevant information.
According to a second aspect of the embodiments of the present disclosure, there is provided an adaptive path optimization system for an unmanned aerial vehicle self-organizing network, including:
the model building unit is used for building a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
the optimization solving unit is used for carrying out optimization solving on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network by utilizing an improved intelligent water drop algorithm according to the constructed mathematical model to generate an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable unmanned aerial vehicles to achieve alliance, water drops in the same path of the unmanned aerial vehicles cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of the water drops is determined through the soil quantity in the water drops.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements a method for adaptive path optimization of an unmanned aerial vehicle self-organizing network as described above.
According to a fourth aspect of the embodiment of the present invention, there is provided an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, the processor implementing a method for adaptive path optimization of an unmanned aerial vehicle self-organizing network as described above when executing the program.
Compared with the prior art, the beneficial effects of the present disclosure are:
(1) The invention provides a self-adaptive path optimization method and a self-adaptive path optimization system for an unmanned aerial vehicle self-organizing network, wherein the scheme is based on an improved intelligent water drop algorithm, and the optimization efficiency and the accuracy of an unmanned aerial vehicle searching path are effectively improved by introducing a alliance game strategy and a soil updating mechanism; compared with the existing algorithm, the scheme disclosed by the disclosure aims at encouraging cooperation among unmanned aerial vehicles, and forming an unmanned aerial vehicle alliance to optimize paths.
(2) According to the scheme, the alliance game strategy is introduced, cooperation and competition between unmanned aerial vehicles and interaction between unmanned aerial vehicles and environments are naturally simulated, so that the IWDs which are allied under the same path cooperate with each other and compete with the IWDs which are allied under different paths, meaningless searching work is reduced, an optimal path reaching a destination can be quickly searched, and the unmanned aerial vehicle can reach a target position in the shortest time.
Additional aspects of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
Fig. 1 is a flowchart of an adaptive path optimization method for an unmanned aerial vehicle self-organizing network according to an embodiment of the disclosure.
Detailed Description
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
Embodiment one:
the aim of the embodiment is to provide an adaptive path optimization method for an unmanned aerial vehicle self-organizing network.
An unmanned aerial vehicle self-organizing network self-adaptive path optimization method comprises the following steps:
constructing a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
according to the constructed mathematical model, utilizing an improved intelligent water drop algorithm to carry out optimization solution on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, and generating an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable the unmanned aerial vehicle to achieve alliance, water drops in the same path of the unmanned aerial vehicle cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of water drops is determined through the soil quantity in the water drops.
Further, the mathematical model is constructed specifically as follows: and enabling the water drops in the intelligent water drop algorithm to correspond to single unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, enabling the lake/ocean to correspond to a known target position in a search area, and enabling the river to represent a path which is searched by the unmanned aerial vehicle group and reaches the target position.
Further, when evaluating whether the unmanned aerial vehicle with two points can achieve the alliance, the improved intelligent water drop algorithm judges by calculating the probability value that the current position of the water drop moves to other adjacent positions, and selects the adjacent position with the largest probability value to achieve the alliance with the current position of the water drop.
Further, the probability value is calculated based on the same type of soil quantity at adjacent positions.
Further, the improved path optimization process of the intelligent water drop algorithm specifically comprises the following steps:
based on the alliance game strategy, the unmanned aerial vehicle in the unmanned aerial vehicle self-organizing network achieves alliance;
constructing a search path and continuously updating path information;
the path with the highest probability value is selected as the optimal path.
Further, the construction of the search path and the continuous updating of the path information are specifically as follows: the unmanned aerial vehicle is simulated to construct a path through water drops, and meanwhile, in order to ensure that the unmanned aerial vehicle continuously obtains updated path information, an information transmission mechanism is adopted to transmit relevant information.
Further, the related information includes the soil amount of the adjacent position, the water drop speed, and the time of arrival of the water drop.
In particular, for easy understanding, the following details of the embodiments of the present disclosure are described below:
an unmanned aerial vehicle self-organizing network self-adaptive path optimization method comprises the following steps:
step 1: introducing a alliance game strategy to enable the unmanned aerial vehicle to achieve alliance:
firstly, mapping an improved intelligent water drop algorithm into an unmanned aerial vehicle self-organizing network, wherein water drops correspond to single unmanned aerial vehicle, lakes or oceans correspond to known target positions, each river flowing into the lakes or oceans represents a path which is searched by an unmanned aerial vehicle group and reaches a target place, and the soil content in the river corresponds to the quality of each searched path. The drones evaluate whether a coalition is formed based on the improved smart drop algorithm, and can use two locations of soil to communicate information for simulating transmissions and responses between drones.
Step 2: establishing a search path and continuously updating path information:
simulating the unmanned aerial vehicle to construct a path by using water drops, an information transmission mechanism is required to transmit relevant information in order to ensure that the unmanned aerial vehicle continuously obtains updated path information: the amount of soil in adjacent locations, the water droplet velocity, and the time of arrival of the water droplet.
Step 3: an optimal path selection stage:
for all unmanned aerial vehicles in one search, when the unmanned aerial vehicles complete paths from the starting point to the target position, each path has a probability value, and the path with the largest probability value is selected as the optimal path and updated to global path information.
In the step 1 and the step 2, the evaluation mode of the unmanned aerial vehicle alliance formation is as follows:
thus, there are n heterogeneous rivers in the environment (each river maintains its own parameters.) IWD (n, k) is set to represent the kth IWD of the nth river, which carries a certain amount of soil that can be moved during the search.
The probability value of the IWD (n, k) moving from position i to position j is calculated to evaluate whether the two-point drone is able to achieve a coalition.
Wherein, soil n (j) Indicating the amount of soil of the nth type in position j. l represents other neighbor positions except j, min h The minimum function value g for all types of soil except the nth type in position j is represented. This information is used to reduce the probability that IWD search paths in different rivers pass through the same location, thereby reducing meaningless search effort by the unmanned team.
Further, a function f (oil n (j) Calculated as follows:
wherein the constant epsilon represents a small positive number.
Function g (soil) n (j) For use in a soil n (j) Converted to positive values, the calculation method is as follows:
when the IWD arrives at a position j from the position i with a position probability value greater than that of the IWD arrives at a neighboring position of i other than the position j, the IWD of the position i and the position j achieve the alliance. With the same evaluation method, a federation is formed between adjacent IWDs until reaching the target location, and then a path to reach the target location is found.
In the alliance evaluation and path establishment step, IWDs of alliances under the same path cooperate with each other and compete with IWDs of alliances under different paths to reduce meaningless search works. The alliance game strategy is introduced, so that the method has better performance than the traditional IWD algorithm. As it naturally simulates the cooperation and competition between the unmanned aerial vehicles, as well as the interaction between unmanned aerial vehicles and the environment.
The IWDs may communicate with each other. Meanwhile, in each time interval t, the IWD representing the unmanned aerial vehicle transmits information of each position to each other through soil, mainly including speed, soil amount and time to reach the position.
The IWD moves from the position i to the position j, and the speed increment calculating method is as follows:
wherein a is v ，b v ，c v Are all parameters greater than 0, soil n (j) Indicating the quantity of the nth type soil in position j, v nk Indicating the speed of movement of the kth IWD carrying the n-type soil.
In the IWD moving process, the calculation method of the increment of the soil quantity comprises the following steps:
wherein a is s ，b s ，c s Are all parameters greater than 0, t (i, j, v) nk ) Is the time the IWD arrives at position j from position i.
The calculation method of the time for each IWD to move from one location to the next:
wherein d (i, j) represents the distance between the two positions, v nk Indicating that the kth IWD carries the movement speed of the n-type soil, the time to reach the next position can be obtained by dividing the distance between two points by the movement speed.
And 3, selecting an optimal path by using the water drop simulation unmanned plane path through the formula (1), calculating the movement probability of the adjacent positions of the current water drops, continuously moving to the next position, and finally obtaining a path reaching the target position. And selecting one path with the largest probability value from the n groups of paths as an optimal path, and then enabling the unmanned aerial vehicle to quickly communicate with the target position.
Specifically, the following further describes the solution of the present disclosure in conjunction with specific examples:
as shown in fig. 1, in the method of the present disclosure, first, initializing parameters mainly includes: quantity of Intelligent Water drops N IWD Initial velocity IWD of intelligent water droplets vini Intelligent water drop initial soil content soil IWD Initial soil content (P) of the path from position i to position j i ，P j )。
Second, when the IWD (n, k) searches its path, it can only search for neighboring locations according to the characteristics of the ad hoc network. In the scheme disclosed in the disclosure, heterogeneity of different types of soil is considered. A new transition probability computation mechanism (i.e., soil update mechanism) is designed and a coalition game strategy is introduced. The probability of the current IWD to the neighboring location is determined by the same type of soil quantity in the neighboring location. In this mechanism, the probability of IWD (n, k) flowing to location j in location i is determined by the soil of all rivers (i.e., soil of all types), and the specific calculation uses equation (1).
Calculated according to formula (1): when the probability value of the IWD reaching the position j from the position i is larger than that of the IWD reaching the adjacent position of the i except the position j, the unmanned plane of the position i and the position j reach alliance. And forming alliances among different unmanned aerial vehicles by the same evaluation method until reaching the target position, and further finding a path reaching the target position.
When the IWD (n, k) calculates all the candidate path probabilities, each path information is updated thereafter: soil content, speed, time to next location; wherein the IWD (n, k) moves from position i to adjacent position j and the incremental calculation of its velocity is shown in equation (4).
When the IWD (n, k) reaches the position j, the increment of the nth soil is calculated as shown in the formula (5).
Each IWD simulates the UAV search for relevant paths process according to the initialization, path construction and local soil update described above, up to the target location, updating the path information when the IWD (n, k) completes its path.
Finally, the IWD selects the path with the greatest fitness, i.e., the greatest probability value, as the optimal path, calculated according to equation (1) and by comparison. And updating global information, and enabling the unmanned aerial vehicle to reach a target position by adopting an optimal path.
Therefore, according to the self-adaptive path optimization algorithm of the unmanned aerial vehicle self-organizing network based on natural elicitation, the simulation result of the embodiment of the invention shows that compared with the prior art, the path optimization method of the invention has higher stability when the alliance formed by the unmanned aerial vehicle in the mobile environment is used for path optimization, and the optimal path reaching the destination can be quickly and accurately searched, so that the unmanned aerial vehicle can be ensured to quickly reach the target position.
Embodiment two:
the embodiment aims to provide an adaptive path optimization system for an unmanned aerial vehicle self-organizing network.
An unmanned aerial vehicle self-organizing network self-adaptive path optimization system, comprising:
the model building unit is used for building a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
the optimization solving unit is used for carrying out optimization solving on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network by utilizing an improved intelligent water drop algorithm according to the constructed mathematical model to generate an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable unmanned aerial vehicles to achieve alliance, water drops in the same path of the unmanned aerial vehicles cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of the water drops is determined through the soil quantity in the water drops. In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of embodiment one. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented as a hardware processor executing or implemented by a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The self-adaptive path optimization method and the self-adaptive path optimization system for the unmanned aerial vehicle self-organizing network can be realized, and have wide application prospects.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. The self-adaptive path optimization method for the unmanned aerial vehicle self-organizing network is characterized by comprising the following steps of:
constructing a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
according to the constructed mathematical model, utilizing an improved intelligent water drop algorithm to carry out optimization solution on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, and generating an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable the unmanned aerial vehicle to achieve alliance, water drops in the same path of the unmanned aerial vehicle cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of water drops is determined according to the soil quantity in the water drops; the alliance game strategy and the soil updating mechanism are specifically as follows: the probability of the current water drop to the adjacent position is determined by the same type of soil quantity in the adjacent position, and the probability of the water drop flowing to the position j in the position i is determined by the soil of all rivers, namely when the probability value of the water drop reaching the position j from the position i is larger than the probability value of the water drop reaching the adjacent position of i except the position j, then the unmanned plane of the position i and the unmanned plane of the position j achieve alliance.
2. The self-adaptive path optimization method of the unmanned aerial vehicle self-organizing network according to claim 1, wherein the mathematical model is constructed specifically as follows: and enabling the water drops in the intelligent water drop algorithm to correspond to single unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network, enabling the lake/ocean to correspond to a known target position in a search area, and enabling the river to represent a path which is searched by the unmanned aerial vehicle group and reaches the target position.
3. The method for optimizing the self-adaptive path of the unmanned aerial vehicle self-organizing network according to claim 1, wherein when evaluating whether the unmanned aerial vehicle with two points can achieve the alliance, the improved intelligent water drop algorithm judges by calculating the probability value that the current position of the water drop moves to other adjacent positions, and selects the adjacent position with the largest probability value to achieve the alliance with the current position of the water drop.
4. A method of adaptive path optimization for an unmanned aerial vehicle ad hoc network as claimed in claim 3, wherein the probability value is calculated based on the same type of soil volume at adjacent locations.
5. The self-adaptive path optimization method of the unmanned aerial vehicle self-organizing network according to claim 1, wherein the path optimization process of the improved intelligent water drop algorithm is specifically as follows:
based on the alliance game strategy, the unmanned aerial vehicle in the unmanned aerial vehicle self-organizing network achieves alliance;
constructing a search path and continuously updating path information;
the path with the highest probability value is selected as the optimal path.
6. The self-adaptive path optimization method for the unmanned aerial vehicle self-organizing network according to claim 5, wherein the steps of constructing a search path and continuously updating path information are as follows: the unmanned aerial vehicle is simulated to construct a path through water drops, and meanwhile, in order to ensure that the unmanned aerial vehicle continuously obtains updated path information, an information transmission mechanism is adopted to transmit relevant information.
7. The unmanned aerial vehicle self-organizing network self-adaptive path optimization method of claim 6, wherein the related information includes soil amount, water drop velocity, and time of arrival of water drops at adjacent locations.
8. An unmanned aerial vehicle self-organizing network self-adaptive path optimization system, comprising:
the model building unit is used for building a mathematical model based on the determined unmanned aerial vehicle self-organizing network, the search area and the target position;
the optimization solving unit is used for carrying out optimization solving on the search paths of all unmanned aerial vehicles in the unmanned aerial vehicle self-organizing network by utilizing an improved intelligent water drop algorithm according to the constructed mathematical model to generate an optimal search path;
the improved intelligent water drop algorithm introduces a alliance game strategy to enable unmanned aerial vehicles to achieve alliance, water drops in the same path of the unmanned aerial vehicles cooperate with each other, and water drops in different paths compete with each other; meanwhile, a soil updating mechanism is adopted in the path updating process, and the transfer probability of water drops is determined according to the soil quantity in the water drops; the alliance game strategy and the soil updating mechanism are specifically as follows: the probability of the current water drop to the adjacent position is determined by the same type of soil quantity in the adjacent position, and the probability of the water drop flowing to the position j in the position i is determined by the soil of all rivers, namely when the probability value of the water drop reaching the position j from the position i is larger than the probability value of the water drop reaching the adjacent position of i except the position j, then the unmanned plane of the position i and the unmanned plane of the position j achieve alliance.
9. A computer readable storage medium having stored thereon a program which when executed by a processor implements a method of unmanned aerial vehicle self-organizing network adaptive path optimization as claimed in any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing a method of unmanned aerial vehicle self-organizing network adaptive path optimization as claimed in any one of claims 1 to 7 when the program is executed.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103301630A (en) * | 2013-06-16 | 2013-09-18 | 西安科技大学 | Coordination and cooperation control method for football robots and system thereof |
| CN110417588A (en) * | 2019-07-19 | 2019-11-05 | 北京科技大学 | A kind of aviation dynamic network paths planning method based on Game with Coalitions |
| CN110493844A (en) * | 2019-09-24 | 2019-11-22 | 广州大学 | The data fusion Game with Coalitions method and system of Wireless Sensor Networks |
| CN110703767A (en) * | 2019-11-08 | 2020-01-17 | 江苏理工学院 | Unmanned vehicle obstacle avoidance path planning method based on improved intelligent water drop algorithm |
| CN110906935A (en) * | 2019-12-13 | 2020-03-24 | 河海大学常州校区 | Unmanned ship path planning method |
| CN111115076A (en) * | 2019-12-10 | 2020-05-08 | 浙江工业大学 | Composite operation three-dimensional path planning method for dense warehousing system of primary and secondary shuttle vehicles |
| CN111712843A (en) * | 2017-10-13 | 2020-09-25 | 巴斯夫农化商标有限公司 | Personalized and customized plant management using autonomous clustered drones and artificial intelligence |
| CN112558601A (en) * | 2020-11-09 | 2021-03-26 | 广东电网有限责任公司广州供电局 | Robot real-time scheduling method and system based on Q-learning algorithm and water drop algorithm |
-
2021
- 2021-12-28 CN CN202111627908.1A patent/CN114485699B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103301630A (en) * | 2013-06-16 | 2013-09-18 | 西安科技大学 | Coordination and cooperation control method for football robots and system thereof |
| CN111712843A (en) * | 2017-10-13 | 2020-09-25 | 巴斯夫农化商标有限公司 | Personalized and customized plant management using autonomous clustered drones and artificial intelligence |
| CN110417588A (en) * | 2019-07-19 | 2019-11-05 | 北京科技大学 | A kind of aviation dynamic network paths planning method based on Game with Coalitions |
| CN110493844A (en) * | 2019-09-24 | 2019-11-22 | 广州大学 | The data fusion Game with Coalitions method and system of Wireless Sensor Networks |
| CN110703767A (en) * | 2019-11-08 | 2020-01-17 | 江苏理工学院 | Unmanned vehicle obstacle avoidance path planning method based on improved intelligent water drop algorithm |
| CN111115076A (en) * | 2019-12-10 | 2020-05-08 | 浙江工业大学 | Composite operation three-dimensional path planning method for dense warehousing system of primary and secondary shuttle vehicles |
| CN110906935A (en) * | 2019-12-13 | 2020-03-24 | 河海大学常州校区 | Unmanned ship path planning method |
| CN112558601A (en) * | 2020-11-09 | 2021-03-26 | 广东电网有限责任公司广州供电局 | Robot real-time scheduling method and system based on Q-learning algorithm and water drop algorithm |
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