CN-116136944-B - Unmanned aerial vehicle non-missing area coverage task parameter optimization method

CN116136944BCN 116136944 BCN116136944 BCN 116136944BCN-116136944-B

Abstract

The invention provides an unmanned aerial vehicle non-missing area coverage task parameter optimization method, which comprehensively considers the discovery problem and the coverage search problem, integrates multiple factors such as environment, load, unmanned aerial vehicle, task personnel and the like, introduces relatively perfect influencing variables, builds an optimization target model and a constraint condition model, designs task parameter optimization algorithm flow and steps based on an immune algorithm frame, finally obtains excellent task parameter groups, ensures high discovery probability, maximizes the unmanned aerial vehicle search efficiency, and realizes quick non-missing coverage search of a target area.

Inventors

JING XIANYONG
HOU MANYI
ZHANG QINGJIE
JI YIGUO
YANG LIN
CHEN CUI
MA ZONGCHENG

Assignees

中国人民解放军空军航空大学

Dates

Publication Date: 20260512
Application Date: 20230222

Claims (6)

1. The unmanned aerial vehicle non-missing area coverage task parameter optimization method is characterized by comprising the following steps of: s1, establishing a coverage width model: s11, determining a scanning mode of the photoelectric device; s12, establishing a static view field model of the photoelectric equipment; s13, establishing an exhaustive coverage width model: s2, establishing a limit model; S21, determining a speed-height ratio limiting condition; S22, determining an omission-free search limiting condition; s23, limiting conditions of rotation angular speed of a visual field optical axis of the photoelectric device in a horizontal plane, such as a visual field depression angle limit, a flying height limit, a cruising speed limit and a visual field depression angle limit are determined; s3, searching parameter planning based on an immune algorithm: S31, establishing an optimization index model; s32, optimizing a target model based on an immune algorithm: The specific steps of the immune algorithm are as follows: Speed of the speed Flying height Azimuth angle Angular velocity of scan Combining as one group of parameters to be optimized, each group of parameters as one antibody in algorithm population with population scale of 50, and objective function value J based on one group of task parameters as adaptability of antibody Cloning selection probability Probability of variation Updating probability ; Step 1, initializing algorithm related parameters, initializing search area coordinates, initializing unmanned aerial vehicle platform performance parameters, initializing photoelectric equipment performance parameters and initializing ; Step 2, randomly generating 50 groups of task parameter groups according to the threshold range of each parameter to form an initial antibody population, wherein each group of parameters comprises 4 parameters; ; Step 3, calculating the objective function value J of each antibody in the population as the fitness of the antibody ; Step 4, calculating whether each antibody in the population meets a speed-height ratio limiting condition and a missing sweeping limiting condition, if any one of the conditions is not met, setting the adaptability of the antibody to 0, and if both limiting conditions are met, keeping the adaptability value of the antibody unchanged; Step 5, judging whether the optimal antibody meets the task requirement, if so, ending the algorithm, otherwise, carrying out the next step; Step 6, calculating the vector moment concentration of each antibody in the population based on the fitness, and calculating the selection probability of the antibody based on the vector moment concentration; step 7, selecting antibodies based on a concentration regulation mechanism of an immune algorithm, performing clone amplification, and cloning selection probability ; Step 8, carrying out mutation operation on each antibody in the population after clone amplification, wherein the mutation probability is that Each parameter of the antibody obtained after mutation needs to meet the threshold range limit of the parameter, and if the parameter does not meet the threshold range limit, the mutation is performed again; step 9, judging whether the number of the antibodies in the current population reaches 50, if not, randomly generating antibodies to complement the population to 50; And 10, judging whether the set condition of the algorithm operation is reached, if so, ending the algorithm, otherwise, taking the obtained new population as an initial population, turning to step 3, and starting a new round of calculation.
2. The unmanned aerial vehicle non-missing area coverage task parameter optimization method according to claim 1, wherein the step S12 of establishing a static field of view model of the photovoltaic device is specifically as follows: for the maximum recognition distance of the target to be searched under the current weather condition, the position of the photoelectric device is that Its projection position on the ground is The flying height is h, For the projection direction of the central axis of the field of view on the ground, For the vertically upward direction, right hand rule determines A shaft; In order to be able to measure the angle of view, The region formed by ABCD is the coverage area of the static view field of the photoelectric equipment on the ground, and the lengths of the view field edges are respectively 、、 And the formula is shown in the specification, ; ; 。
3. The unmanned aerial vehicle non-missing region coverage task parameter optimization method according to claim 1, wherein the step S13 of establishing the non-missing coverage width model is specifically as follows: To be used for The origin is the flying direction The axis of the shaft is provided with a plurality of grooves, The axis being perpendicular to The shaft is positive to the right, and a ground horizontal two-dimensional coordinate system is established; ; ; Wherein the method comprises the steps of 、 Is the inner radius and the outer radius of the static footprint, In order to achieve a speed of flight, Is the rotation angular velocity of the optical axis of the field of view of the photoelectric device in the horizontal plane, and the photoelectric device is radially arranged along the Y axis The position is moved to In position, the scanning optical axis is axially rotated 2 from the leftmost side To the far right, when the optoelectronic device is from The position is moved to In position, the scanning optical axis will rotate 2 in the axial direction from the rightmost side The process of reaching the original position is one period, the time for one scanning period is T, and the scanning of two arc areas is completed in one scanning period, In order to dynamically search for the azimuth angle, For the innermost point of the "missed scan region A representation; the width of the container is the width of the container for no missing search.
4. The unmanned aerial vehicle non-missing area coverage task parameter optimization method according to claim 1, wherein the speed ratio limiting condition in S21 is specifically as follows: ; The angle deviating from the flying course is , Is the maximum speed to high ratio.
5. The unmanned aerial vehicle non-missing area coverage task parameter optimization method of claim 4, wherein the non-missing search constraint in S22 is: The region A 1 B 1 C 1 D 1 is that the optoelectronic device is located The coverage area of static view field on the ground, the area formed by A 2 B 2 C 2 D 2 is that the photoelectric equipment is positioned The coverage area of static view field on the ground, the area formed by A 3 B 3 C 3 D 3 is that the photoelectric equipment is positioned The coverage area of the static field of view on the ground for the trajectory Sum trace The functional expression of (2) is in interval Any of the inside All have: ; Wherein the track The functional expression of (2) is , ; Track The functional expression of (2) is , 。
6. The unmanned aerial vehicle non-missing region coverage task parameter optimization method according to claim 1, wherein the optimization index model in S31 specifically comprises: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; , ; Wherein, the To cover efficiency Weight coefficient of (2), Angular velocity of photoelectric equipment Weight coefficient of (2), At optimum cruising speed Weight coefficient of (2), Is the optimal cruising altitude Is used for the weight coefficient of the (c), 、 Fly heights for mission schemes, respectively Speed of flight Deviation from the optimum value, and > > = , In order to comprehensively optimize the objective function, Is the depression angle of the outer edge of the cup, For the lowest depression-angle of view, For the highest safe flight level, For the lowest safe flight level of the aircraft, Is the maximum rotational angular velocity of the optical axis of the field of view of the optoelectronic device in the horizontal plane.

Description

Unmanned aerial vehicle non-missing area coverage task parameter optimization method Technical Field The invention belongs to the technical field of unmanned aerial vehicle searching, and particularly relates to a method for optimizing unmanned aerial vehicle photoelectric load non-missing area coverage task parameters. Background Many of the conventional search theories are developed based on conventional search units, such as fixed wing aircraft, helicopters, ships, vehicles, personnel, and the like. In recent years, along with the maturity and perfection of unmanned aerial vehicles and task loads thereof, the unmanned aerial vehicle has the advantages of long voyage, large voyage, unmanned casualties, various task loads and the like, the application of searching for civil or military targets by using the unmanned aerial vehicles is gradually increased, a larger searching application prospect is shown, and a target searching method based on the unmanned aerial vehicles also becomes an important research direction. According to the search theory, a certain target in a certain area is usually found, so that complete and complete coverage search of the area where the target is located is ensured, and effective induction and discovery of the target by a search field of a search unit are ensured. The area coverage search is a relatively common target search mode of the unmanned aerial vehicle, and has wide application in various scenes such as marine rescue, disaster area rescue, area investigation, patrol monitoring and the like. In unmanned aerial vehicle area coverage search, photoelectric detection equipment is the most common imaging equipment, has advantages such as resolution ratio is high, discovery distance is far away, but static visual field is limited generally, need adopt the mode of on-line control scanning of mission personnel to accomplish the search process. However, due to complex limitations of environmental factors, detection load, unmanned aerial vehicle, physiology of task operators and other factors, how to determine a set of proper task parameters to efficiently and completely complete coverage search and find targets with high probability is a practical, challenging and concurrent complex nonlinear optimization problem. The existing task parameter planning method generally does not consider the influence of detector resolution, environment visibility and the like on target discovery, and focuses attention on the area segmentation and track planning method. The regional division method and the track planning method for complex regional multiple UAV coverage reconnaissance are researched in Dagaku university of aviation, beijing, etc. (2015,41 (1): 167-173), wu Qingpo, etc. (tactical missile technology, 2016 (1): 50-55) establish a geometric relation model between static detection width and flight height, pitch angle and search azimuth angle of a UAV detector, but the geometrical relation model does not consider the limitation conditions and influencing factors in aspects of target discovery, speed-height ratio, scanning omission, etc., and has a larger difference with the actual condition when the load works. Tan Lezu et al (naval vessel electronic engineering, 2019,39 (06): 146-150) studied the relation between indexes such as discovery probability, coverage rate and the like from the meaning of the scanning width, and obtained the scanning width calculation method under various detection modes by analyzing the working mode of the detection equipment and the limitation principle of target identification pixels, but without considering the influence of the aspects of target discovery, missing scanning and visual fatigue of mission personnel. Overall, modeling of the photoelectric load scanning range of the unmanned aerial vehicle in the existing method is too simple, the regional scanning leakage problem and the target finding problem are not considered, and influences such as physiology of task personnel are not considered during task parameter planning. Disclosure of Invention In order to solve the technical problems, the invention provides a method for optimizing the parameters of an unmanned aerial vehicle non-missing area coverage task, which comprises the following steps: S1, establishing a coverage width model; s2, establishing a limit model; s3, searching parameter planning based on an immune algorithm. Specifically, the specific steps for establishing the coverage width model are as follows: s11, determining a scanning mode of the photoelectric device; s12, establishing a static view field model of the photoelectric equipment; s13, establishing an exhaustive coverage width model. Further, the establishment of the static view field model of the photoelectric device is specifically as follows: For the maximum recognition distance of a target to be searched under the current weather condition, the position of the photoelectric device is O 1, the projection position of the photoele