Application of Machine Learning in UAV Combat

Ren, Yan; Cao, Xiaoqun; Guo, Yanan; Peng, Kecheng; Xiao, Chaohao; Tian, Wenlong

doi:10.1007/978-981-16-9492-9_290

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…In recent years, an increasing number of scholars have applied AI methods to swarm confrontation research [30][31][32][33][34]. Compared to traditional game theory and evolutionary computation strategies, AI methods offer advantages in handling complex, high-dimensional environments and adaptively learning optimal strategies from raw observational data.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

Cai,

Luo,

Gao

et al. 2024

Electronics

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This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices.

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