Multi-UAV Cooperative Air Combat Decision-Making Based on Multi-Agent Double-Soft Actor-Critic

Li, Shaowei; Wang, Yongchao; Zhou, Yaoming; Jia, Yingmin; Shi, Hanyue; Yang, Fan; Zhang, Chaoyue

doi:10.3390/aerospace10070574

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…In [25], Li et al presented a multi-agent decision-making algorithm in air combat situations involving multiple unmanned aerial vehicles in a cooperative game; they used a decentralized, partially observable Markov decision process and a centrally learned distributed control system structure.…”

Section: State Of Knowledgementioning

confidence: 99%

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Lisowski

2024

Electronics

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The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.

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Section: State Of Knowledgementioning

confidence: 99%

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Lisowski

2024

Electronics

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“…In addition, the intelligence and autonomy of UAVs is also important to consider for future development. With the development of computer algorithms, processing capabilities, and other technologies, researchers have made great progress in mission planning, strategy decisions, trajectory control, and other aspects with regards to UAVs [9,10]. The control mode of UAVs has also been improved from semi-autonomous forms, such as pre-programming and pre-planning, to fully autonomous and intelligent forms.…”

Section: Of 19mentioning

confidence: 99%

Study on the Glider Soaring Strategy in Random Location Thermal Updraft via Reinforcement Learning

Cui,

Yan,

Wan

2023

Aerospace

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Soaring birds can use thermal updrafts in natural environments to fly for long periods or distances. The flight strategy of soaring birds can be implemented to gliders to increase their flight time. Currently, studies on soaring flight strategies focus on the turbulent nature of updrafts while neglecting the random characteristics of its generation and disappearance. In addition, most flight strategies only focus on utilizing updrafts while neglecting how to explore it. Therefore, in this paper, a complete flight strategy that seeks and uses random location thermal updrafts is mainly emphasized and developed. Moreover, through the derivation of flight dynamics and related formulas, the principle of gliders acquiring energy from thermal updrafts is explained through energy concepts. This concept lays a theoretical foundation for research on soaring flight strategies. Furthermore, the method of reinforcement learning is adopted, and a perception strategy suitable for gliders that considers the vertical ground speed, vertical ground speed change rate, heading angle, and heading angle change as the main perception factors is developed. Meanwhile, an area exploring strategy was trained by reinforcement learning, and the two strategies were combined into a complete flight strategy that seeks and uses updrafts. Finally, based on the guidance of the soaring strategy, the flight of the glider in the simulation environment is tested. The soaring strategy is verified to significantly improve the flight time lengths of gliders.

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“…s a kind of flying machine, since its invention in the 1940s, unmanned aerial vehicle (UAV) has undergone decades of development, and now it has been integrated into all walks of national production and life [1]- [4], and has been widely used in many fields such as military, civil, and commercial. On the military side, it performs a variety of military tasks in war, including reconnaissance, target location, signal intelligence search, defense and control fire decoys and other real-time intelligence [5]- [9]. On the civilian side, UAVs can complete high-altitude and high-risk work such as courier transport, pesticide spraying, and antenna inspection [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

Design of LABVIEW-based UAV Online Monitoring and Security Situation Assessment System

Shi,

Zhang,

Shi

et al. 2024

Preprint

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In this paper, we design a LabVIEW-based UAV online monitoring and safety situation assessment system to address the deficiencies in the monitoring of UAV flight state parameters and safety situation assessment. The system includes a lower computer recorded by the UAV and an upper computer on the ground. The lower computer collects and detects the flight data and connects to the upper computer through the serial port via the wireless digital transmission module, and the upper computer receives the data and carries out the monitoring of the UAV and the estimation of the safety situation. The lower unit of the system adopts multi-sensors to collect UAV navigation information in real time to achieve flight detection, and the upper unit adopts LabVIEW to design UAV online monitoring and safety situation estimation system to achieve monitoring and safety situation estimation during UAV navigation. The test results show that the system is able to detect and comprehensively display the navigation information of the UAV in real time and realize the safety situation assessment and alarm function of the UAV, which can realize the functions of online monitoring and safety situation assessment during the navigation process of the UAV.

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Multi-UAV Cooperative Air Combat Decision-Making Based on Multi-Agent Double-Soft Actor-Critic

Cited by 9 publications

References 30 publications

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Study on the Glider Soaring Strategy in Random Location Thermal Updraft via Reinforcement Learning

Design of LABVIEW-based UAV Online Monitoring and Security Situation Assessment System

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