In recent years, UAV technology has achieved rapid development and has been more and more widely used in both military and civilian fields. In the military, UAV applications gradually present characteristics such as clustering, intelligence and self-organization, which naturally gives rise to the need for research on UAV swarm collaborative intelligent decision-making technology. The UAV swarm cooperative intelligent decision-making technology is a key technical issue that determines whether the UAV swarm with self-organized intelligent cooperation can be put into actual combat application, including "intelligent communication networking", "intelligent command and control", "intelligent Situation Awareness and Assessment", "Intelligent Target Assignment Decision", "Intelligent Track Planning Decision", "Intelligent Flight Control Decision", "Intelligent Weapon Assignment Decision", "Intelligent Damage Assessment", and a series of other fields.
Focusing on swarm intelligence algorithms, this paper briefly investigates the UAV swarm collaborative intelligent decision making technology by selecting three aspects of intelligent decision making techniques, namely "intelligent situational awareness and assessment", "intelligent collaborative target assignment" and "intelligent collaborative path planning":
Firstly, to address the problem that the existing mainstream situational assessment methods ranked by target threat values are not applicable to homogeneous UAV swarm air combat missions, a new form of situational representation centered on strike hit ratio and counterattack hit ratio is introduced, and a situational assessment model combining fuzzy set theory and multi-attribute analysis and based on the strike gain-loss ratio matrix is studied;
Secondly, based on the situation assessment and the swarm cooperative target allocation, this research proposes an improved hybrid particle swarm algorithm, which uses the “blockage detection” mechanism and “forced scattering” operation. The proposed algorithm can address partly the problem of “premature”, which often occurs when the swarm intelligence algorithm is prone to local extremes for the application of UAV swarm cooperative target allocation;
Then, assuming that the final assignment result of UAV swarm cooperative target assignment is already known, the modeling of single aircraft online path planning is completed, and for the problem that the existing path planning algorithm cannot be applied to the online path planning of moving mission targets in the transparent dynamic mission environment, take the Wolf Pack algorithm as the research object, a single aircraft online path planning algorithm based on the improved Wolf Pack algorithm and combining the rolling time domain optimization method and the two-dimensional equal step length trajectory generation method is proposed. The simulation application of this algorithm on the modeled online path planning of a single aircraft is studied, and the difference of optimization performance is compared with the original Wolf Pack algorithm;
Finally, the above-mentioned research on single aircraft online path planning is extended to UAV swarm collaborative online path planning, and a collaborative UAV swarm collaborative online path planning model is reconstructed based on the modeling of single aircraft online path planning problem by introducing synergy constraints and requirements. Aiming to solve the UAV swarm collaborative online path planning of moving mission targets in the transparent dynamic mission environment, take the Firefly algorithm as the research object, an algorithm is proposed based on the improved Firefly algorithm and combines the rolling time domain optimization method and the two-dimensional equal step length trajectory generation method. It is verified that the proposed algorithm can be used to optimize the UAV swarm collaborative online path planning problem modeled in this previous, and the difference of optimization performance is compared with the original Firefly algorithm.
|Keyword||无人机集群 态势评估 协同目标分配 协同航迹规划 群智能算法|
|MOST Discipline Catalogue||工学::计算机科学与技术（可授工学、理学学位）|
|IS Representative Paper||是|
|Sub direction classification||人工智能基础理论|
|planning direction of the national heavy laboratory||复杂系统建模与推演|
|Paper associated data||否|
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