UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG

Luo, Yuxie; Zhao, Kai; Liu, Yang

doi:10.3390/app12031618

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…(2) Based on the reinforcement learning network. For example, Yuxie Luo [12] established an intelligent UAV model using the reinforcement learning network and built a reward function using the cooperative parameters of multiple UAVs to guide UAVs to conduct collaborative penetration; Yue Li [13] used reinforcement learning algorithms for training in four scenarios, frontal attack, escape, pursuit, and energy storage, thereby improving the intelligent decision-making level of air confrontation; Kaifang Wan [14] proposed a motion control method based on deep reinforcement learning (DRL), which provides additional flexibility for UAV penetration within the DRL framework; Liang Li [15] mainly focused on the winning region of three players in the reconnaissance penetration game, proposed an explicit policy method for analyzing and constructing barriers, and provided a complete solution by integrating the games of kind and degree.…”

Section: Related Workmentioning

confidence: 99%

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A Reinforcement Learning Method Based on an Improved Sampling Mechanism for Unmanned Aerial Vehicle Penetration

Wang

Zhuang

et al. 2023

Aerospace

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The penetration of unmanned aerial vehicles (UAVs) is an important aspect of UAV games. In recent years, UAV penetration has generally been solved using artificial intelligence methods such as reinforcement learning. However, the high sample demand of the reinforcement learning method poses a significant challenge specifically in the context of UAV games. To improve the sample utilization in UAV penetration, this paper innovatively proposes an improved sampling mechanism called task completion division (TCD) and combines this method with the soft actor critic (SAC) algorithm to form the TCD-SAC algorithm. To compare the performance of the TCD-SAC algorithm with other related baseline algorithms, this study builds a dynamic environment, a UAV game, and conducts training and testing experiments in this environment. The results show that among all the algorithms, the TCD-SAC algorithm has the highest sample utilization rate and the best actual penetration results, and the algorithm has a good adaptability and robustness in dynamic environments.

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Section: Related Workmentioning

confidence: 99%

“…When the UAV hits the target successfully, it is considered to have completed hit subtask m hit , so the corresponding subtask m t is shown as Formula (12).…”

Section: Task Completion Divisionmentioning

confidence: 99%

A Reinforcement Learning Method Based on an Improved Sampling Mechanism for Unmanned Aerial Vehicle Penetration

Wang

Zhuang

et al. 2023

Aerospace

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“…Li et al [7] adopted the Gaussian pseudo-spectral method to realize the time coordination strategy of reusable launch vehicles (RLV) and satisfy the no-fly constraints at the same time. However, the Due to the huge benefits of time cooperation, time cooperation technology has received worldwide attention from many studies [1][2][3]. Yu et al [4] designed a two-stage strategy that the attack angle cooperation of multi-vehicles is realized in the first stage, and the attack time cooperation of multi-vehicles is achieved in the final stage.…”

Section: Introductionmentioning

confidence: 99%

A Time Cooperation Guidance for Multi-Hypersonic Vehicles Based on LSTM Network and Improved Artificial Potential Field Method

Tong

Zhao

2022

Aerospace

Self Cite

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Time cooperation guidance is a key technology which can greatly increase the success rate of flight missions. However, it is difficult to satisfy all the strict constraints when designing the guidance system for multiple hypersonic vehicles. To solve these problems, a time cooperation framework is proposed. In this paper, the longitudinal predictor–corrector guidance law is firstly applied to meet the terminal and path constraints simultaneously. To settle the inaccurate estimation problem of residual flight time, a long short-term memory network (LSTM network) is trained and adopted in a time decision module, whose inputs are selected as six-dimensional feature vectors combined with the features of the sequential ballistics. In the time control module, the traditional artificial potential field method is modified to handle the no-fly zone constraints problem. Furthermore, the time potential field as a new type of potential field is added to indirectly control the flight time of hypersonic vehicles. The final simulation results show that the novel time potential field is compatible with the traditional potential field, which can satisfy the no-fly zone and flight time constraints at the same time. Meanwhile, compared with other time cooperative guidance, the algorithm proposed in this paper performs better in terms of time adjustable range.

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Defense penetration strategy for unmanned surface vehicle based on modified soft actor–critic

Zhang,

Liang,

Chen

et al. 2024

Ocean Engineering

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UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG

Cited by 7 publications

References 19 publications

A Reinforcement Learning Method Based on an Improved Sampling Mechanism for Unmanned Aerial Vehicle Penetration

A Reinforcement Learning Method Based on an Improved Sampling Mechanism for Unmanned Aerial Vehicle Penetration

A Time Cooperation Guidance for Multi-Hypersonic Vehicles Based on LSTM Network and Improved Artificial Potential Field Method

Defense penetration strategy for unmanned surface vehicle based on modified soft actor–critic

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