CN-121977576-A - Unmanned plane path planning method and device, electronic equipment and readable storage medium

CN121977576ACN 121977576 ACN121977576 ACN 121977576ACN-121977576-A

Abstract

The application relates to an unmanned aerial vehicle path planning method, device, electronic equipment and readable storage medium, which comprise the steps of obtaining environment information of a task area and constraint parameters of an unmanned aerial vehicle, initializing a population for improving a beaver optimization algorithm, determining fitness values for each individual in the population based on a preset cost function, dynamically distributing roles for each individual in the population based on the fitness values, updating positions of the individuals according to the distributed roles by adopting a corresponding updating strategy, executing information exchange among the roles in the updating process, determining the individual with the lowest fitness value from the population after iteration updating based on a preset termination condition, and outputting a path point sequence represented by the individual as a planned unmanned aerial vehicle flight path. By the method provided by the application, the quality of the local optimal escape capability and resolution is enhanced, the flight safety and the terrain adaptability are improved, and the overall performance and the reliability of unmanned plane path planning are improved.

Inventors

GAO JIUZHOU
YUE JUNHUA
WANG YIGANG
HAN CHENGHAO
ZHANG LIHUI
SUN WEI
WANG XIANGRUI
JIA XUE
LI YULI
ZHANG YUHONG

Assignees

吉林建筑大学

Dates

Publication Date: 20260505
Application Date: 20260401

Claims (10)

1. A method for unmanned aerial vehicle path planning, comprising: Acquiring environment information of a task area and constraint parameters of an unmanned aerial vehicle, wherein the constraint parameters comprise a maximum safe flight distance, a minimum flight height and a maximum flight height; initializing a population of improved beaver optimization algorithms, wherein each individual in the population represents a candidate flight path consisting of a plurality of path point coordinates; Determining a fitness value for each individual in the population based on a preset cost function, wherein the preset cost function is determined based on path length, turning angle, threat avoidance, and altitude constraint; Dynamically assigning roles to each individual in the population based on the fitness value, updating the positions of the individuals by adopting a corresponding updating strategy according to the assigned roles, and executing information exchange among the roles in the updating process, wherein the roles comprise developer individuals, explorator individuals and guard individuals; And determining an individual with the lowest fitness value from the population after iteration updating based on a preset termination condition, and outputting a path point sequence represented by the individual as a planned unmanned aerial vehicle flight path.
2. The method of claim 1, wherein dynamically assigning roles to each individual in the population based on the fitness value comprises: normalizing the fitness value of each individual to obtain a normalized fitness value; based on the current iteration times and the normalized fitness value, determining preference probabilities of each individual becoming an explorator individual, a developer individual and a guard individual respectively; Determining a specific probability that each individual is allocated as an explorer individual, a developer individual or a defender individual through a preset role allocation function based on the temperature parameter attenuated with the iteration times and the preference probability; and determining the roles of the individuals in the round of iteration based on the specific probability and the target role proportion of the current iteration times.
3. The method of claim 1, wherein updating the location of the individual with the corresponding update policy based on the assigned persona comprises: for the developer individuals, determining elite individual learning parameters based on weighted summation of differences between the positions of a plurality of elite individuals and the positions of the current developer individuals, wherein the elite individuals are a preset number of individuals with fitness values in the current population ranked in a preset proportion range; Self-adaptively determining a covariance matrix of a multi-element Gaussian distribution based on an adaptation value of an individual of a current developer, a population optimal adaptation value, a population worst adaptation value and a current iteration number, and obtaining a random vector based on multi-element Gaussian distribution sampling corresponding to the covariance matrix so as to determine local fine random search parameters; Determining self historical optimal position learning parameters based on the difference value between the historical optimal position of the current developer individual and the current position; And determining a position updating amount based on the elite individual learning parameter, the local fine random search parameter and the self-history optimal position learning parameter.
4. The method of claim 1, wherein updating the location of the individual with the corresponding update policy based on the assigned persona comprises: For the guard individuals, determining a population diversity deficiency degree based on the relationship between the current population diversity and the initial population diversity, and determining a diversity disturbance item based on the population diversity deficiency degree and a random direction vector; determining a high diversity individual based on the deviation degree of the individual position and the population center position, and determining a suboptimal region exploration item based on the difference value between the current guard individual position and the high diversity individual position; determining an edge exploration term based on the position of the current guard individual and a vector of the nearest boundary of the search space; based on the preset probability selection, the combination of the diversity disturbance item and the suboptimal region exploration item, the edge exploration item or the random restarting operation is executed, and the position of the individual guard is updated.
5. The method of claim 1, wherein the information exchange comprises a exploratory information exchange between the exploratory individual and the developer individual, and a defending information exchange between the defender individual and the developer individual; Wherein the exploration information exchange is used for representing the individual explorer to transmit exploration information comprising a future exploration area to the individual developer, so that the individual developer develops the future exploration area based on the exploration information; The guard information exchange is used for indicating that the developer individual transmits local information comprising a local optimal position to the guard individual so that the guard individual moves away from the local optimal position based on the local information.
6. The method of claim 1, wherein the determining fitness values for each individual in the population based on a preset cost function comprises: determining a distance cost based on the total length of the candidate flight path; Determining a turn angle cost based on angles at each turn in the candidate flight path; determining threat cost based on minimum distance between each leg in the candidate flight path and the threat area; determining an altitude cost based on the relationship between the altitude of each leg in the candidate flight path and the minimum altitude and the maximum altitude; The fitness value is determined based on a weighted sum of the distance cost, the turn angle cost, the threat cost, and the altitude cost.
7. The method of claim 6, wherein determining the turn angle cost based on the angle at each turn in the candidate flight path comprises: For each turning point in the candidate flight path, acquiring coordinates of continuous preset turning number path points forming the turning point; Determining a front path segment vector and a rear path segment vector based on the coordinates of the path points with the preset turning number; determining a cosine value of the included angle at the turning position based on the previous path segment vector and the subsequent path segment vector; determining the corresponding angle cost value of the turning position based on the cosine value and a preset piecewise function; and summing the corresponding angle cost values of all the turning positions of the candidate flight path to obtain the turning angle cost.
8. An unmanned aerial vehicle path planning apparatus, comprising: The acquisition module is used for acquiring environment information of a task area and constraint parameters of the unmanned aerial vehicle, wherein the constraint parameters comprise a maximum safe flight distance, a minimum flight height and a maximum flight height; an initialization module for initializing a population of improved beaver optimization algorithms, wherein each individual in the population represents a candidate flight path consisting of a plurality of path point coordinates; the fitness determination module is used for determining fitness values for each individual in the population based on a preset cost function, wherein the preset cost function is determined based on path length, turning angle, threat avoidance and altitude constraint; The iterative optimization module is used for dynamically allocating roles for each individual in the population based on the fitness value, updating the positions of the individuals by adopting a corresponding updating strategy according to the allocated roles, and executing information exchange among the roles in the updating process, wherein the roles comprise an explorer individual, a developer individual and a defender individual; And the output module is used for determining an individual with the lowest fitness value from the population after iteration updating based on a preset termination condition, and outputting a path point sequence represented by the individual as a planned unmanned aerial vehicle flight path.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.

Description

Unmanned plane path planning method and device, electronic equipment and readable storage medium Technical Field The present application relates to the field of unmanned aerial vehicle control technologies, and in particular, to an unmanned aerial vehicle path planning method, an unmanned aerial vehicle path planning device, an electronic device, and a readable storage medium. Background In the field of unmanned aerial vehicle autonomous path planning, the prior art mainly relies on intelligent optimization algorithms such as genetic algorithms, particle swarm algorithms and the like, and searches for an optimal path by constructing a cost function containing basic factors such as distance, obstacle threat and the like. However, these methods typically consider only path length and static obstacles, ignoring various physical and safety constraints that must be observed in the actual flight of the unmanned aerial vehicle, such as energy, altitude, corners, etc., resulting in a path that may not be practical or present a safety risk. In the prior art, one solution idea is to take various constraints as post-verification conditions, but the method may cause planned paths to fail because of unsatisfied constraints, the other solution idea is to try to allocate weights for different constraints and integrate the weights into a cost function, but the improper weight setting easily causes the algorithm to deviate to a single target and sacrifice other key performances, and research is conducted on mechanisms for improving the optimization algorithm itself, such as adjusting role allocation strategies to balance exploration and development, but static or simple strategies are difficult to adapt to dynamic requirements of different stages of the optimization process, and information exchange among roles is weak, so that search efficiency and cooperativity are affected. Therefore, in the prior art, multiple practical constraints such as travel, height, rotation angle and threat avoidance are still difficult to adaptively and deeply fuse in the path planning process, and dynamic balance of algorithm exploration and development capability is realized, so that the problem that the safety, economy and reliability of the flight path of the unmanned aerial vehicle generated in the prior art are insufficient is solved. Disclosure of Invention In view of the above, the embodiments of the present application provide a method, an apparatus, an electronic device, and a readable storage medium for unmanned aerial vehicle path planning, so as to solve the problems of insufficient safety, economy, and reliability of unmanned aerial vehicle flight paths generated in the prior art. According to a first aspect of the embodiment of the application, an unmanned aerial vehicle path planning method is provided, which comprises the steps of obtaining environment information of a task area and constraint parameters of an unmanned aerial vehicle, wherein the constraint parameters comprise a maximum safe flight distance, a minimum flight altitude and a maximum flight altitude, initializing a population of an improved beaver optimization algorithm, wherein each individual in the population represents a candidate flight path formed by a plurality of path point coordinates, determining an fitness value for each individual in the population based on a preset cost function, wherein the preset cost function is determined based on path length, turning angle, threat avoidance and altitude constraint, dynamically distributing roles for each individual in the population based on the fitness value, updating positions of the individuals according to the distributed roles by adopting a corresponding updating strategy, executing information exchange among the roles in the updating process, wherein the roles comprise an explorer individual, a developer individual and a guard individual, determining the individual with the lowest fitness value from the iteratively updated population based on a preset termination condition, and outputting a path point sequence represented by the individual as the unmanned aerial vehicle flight path planning method. According to a second aspect of the embodiment of the application, an unmanned aerial vehicle path planning device is provided, which comprises an acquisition module, an initialization module and an adaptation degree determination module, wherein the acquisition module is used for acquiring environment information of a task area and constraint parameters of an unmanned aerial vehicle, the constraint parameters comprise a maximum safe flight distance, a minimum flight altitude and a maximum flight altitude, the initialization module is used for initializing a population for improving a beaver optimization algorithm, each individual in the population represents a candidate flight path formed by a plurality of path point coordinates, the adaptation degree determination module is used for determining an adaptation deg