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Cooperative confrontation decision-making method and system for unmanned aerial vehicle swarm

A decision-making method and UAV technology, applied in control/regulation systems, instruments, three-dimensional position/channel control, etc., can solve the problem of single UAVs not considering high-value targets on the ground, and improve the level of cooperative confrontation, The effect of increasing attack effectiveness

Active Publication Date: 2022-03-11
BEIJING UNION UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the decision-making and collaborative decision-making mechanism of the single UAV in this invention did not consider the high-value targets on the ground

Method used

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  • Cooperative confrontation decision-making method and system for unmanned aerial vehicle swarm
  • Cooperative confrontation decision-making method and system for unmanned aerial vehicle swarm
  • Cooperative confrontation decision-making method and system for unmanned aerial vehicle swarm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0103] Such as figure 1 As shown, a UAV swarm cooperative confrontation decision-making method, including:

[0104] Step 1: Establish a three-dimensional battlefield environment containing high-value targets on the ground;

[0105] Step 2: Establish the motion model of the UAV;

[0106] Step 3: Establish the attack model of the UAV;

[0107] Step 4: Find the optimal strategy set for each UAV (UAV, Unmanned Aerial Vehicle);

[0108] Step 5: Establish a dynamic game model;

[0109] Step 6: In the optimal strategy set, determine the optimal response strategy of the UAV according to the dynamic game model.

[0110] Preferably in this embodiment, step 1: establish a three-dimensional battlefield environment containing high-value targets on the ground, including: setting the spatial coordinate system of the three-dimensional battlefield, the spatial range of the battlefield, the number of unmanned aerial vehicles, HVT related information, unmanned aerial vehicles Parameters such ...

Embodiment 2

[0178] The difference from Example 1 is that, as Figure 8 As shown, preferably in the present embodiment, in step 4, utilize genetic algorithm (GA, Genetic Algorithm) to find out the optimal strategy set of each unmanned aerial vehicle, promptly for each in the unmanned aerial vehicle bee colony All UAVs execute GA to obtain the optimal strategy set of each UAV. For UAVs numbered i (i=1, 2, 3, ..., n, n is the swarm of UAVs of one’s own side the total number of drones), including:

[0179] Step 421: Obtain battlefield situation information;

[0180] Step 422: Generate an initialization population;

[0181] Step 423: Calculate fitness;

[0182] Step 424: According to fitness, use roulette rules to select parents;

[0183] Step 425: Parents generate offspring through crossover and mutation;

[0184] Step 426: Calculate the fitness of the offspring;

[0185] Step 427: replace individuals with low fitness with offspring to form a new population;

[0186] Step 428: If the n...

Embodiment 3

[0192] The difference from Example 1 is that, as Figure 9 As shown, preferably in the present embodiment, in step 4, use differential evolution (DE, Differential Evolution) algorithm to find out the optimal policy set of each drone, that is, for each drone colony All UAVs execute the DE algorithm to obtain the optimal strategy set of each UAV. For the UAV numbered i (i=1, 2, 3, ..., n, n is its own UAV total number of drones in the swarm), such as Figure 9 shown, including:

[0193] Step 431: Initialization, including initialization of population size, individual dimension, and maximum number of iterations;

[0194] Step 432: Calculate the fitness of each individual

[0195] Step 433: mutation operation;

[0196] Step 434: cross operation;

[0197] Step 435: select an operation;

[0198] Step 436: Terminate the iteration after the number of iterations reaches the maximum number of iterations, otherwise, go to step 432 after the number of iterations is increased by on...

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Abstract

The invention provides an unmanned aerial vehicle swarm collaborative confrontation decision-making method and system. The method comprises the steps of 1, establishing a three-dimensional battlefield environment containing a ground high-value target; 2, establishing a motion model of the unmanned aerial vehicle; 3, establishing an attack model of the unmanned aerial vehicle; 4, solving an optimal strategy set of each unmanned aerial vehicle; 5, establishing a dynamic game model; and 6, in the optimal strategy set, determining the optimal response strategy of the unmanned aerial vehicle according to the dynamic game model. The method comprehensively considers HVT, establishes a weight-based attack model which comprehensively considers an included angle between two unmanned aerial vehicles, an HVT exposure area and an attack distance, solves an optimal strategy set of the unmanned aerial vehicles, establishes a collaborative confrontation game model of three unmanned aerial vehicle swarms, and solves an optimal response strategy of the unmanned aerial vehicles. The cooperative confrontation level of the unmanned aerial vehicle swarm is effectively improved when the HVT of an enemy is attacked, so that the attack efficiency of the unmanned aerial vehicle swarm is greatly improved.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicles, in particular to a decision-making method and system for cooperative confrontation of unmanned aerial vehicles and swarms. Background technique [0002] Since the 21st century, small UAVs have continued to make progress in the miniaturization of payload, endurance, over-the-horizon communication, and low cost. Intelligent technologies such as cluster technology, autonomous technology, and collaborative technology are promoting the development of small UAVs. With the improvement of combat effectiveness, it is foreseeable that drone swarm operations will become an important combat method on the battlefield in the future. UAV swarms are composed of several low-cost small UAVs equipped with various mission loads. They refer to the collective action mode of creatures such as bees, and achieve the goal of completing specific combat tasks with high efficiency through the coordination be...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/104Y02T10/40
Inventor 宏晨陈艾东肖明明刘畅
Owner BEIJING UNION UNIVERSITY