A Bio-Inspired Decision-Making Method of UAV Swarm for Attack-Defense Confrontation via Multi-Agent Reinforcement Learning

Author:

Chi Pei1,Wei Jiahong2,Wu Kun3,Di Bin4,Wang Yingxun1

Affiliation:

1. Institute of Unmanned System, Beihang University, Beijing 100191, China

2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

3. Flying College, Beihang University, Beijing 100191, China

4. Defense Innovation Institute, Academy of Military Sciences, Beijing 100071, China

Abstract

The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontation, such as multi-agent reinforcement learning (MARL), suffer from an exponential increase in training time as the size of the swarm increases. Inspired by group hunting behavior in nature, this paper presents a new bio-inspired decision-making method for UAV swarms for attack-defense confrontation via MARL. Firstly, a UAV swarm decision-making framework for confrontation based on grouping mechanisms is established. Secondly, a bio-inspired action space is designed, and a dense reward is added to the reward function to accelerate the convergence speed of training. Finally, numerical experiments are conducted to evaluate the performance of our method. The experiment results show that the proposed method can be applied to a swarm of 12 UAVs, and when the maximum acceleration of the enemy UAV is within 2.5 times ours, the swarm can well intercept the enemy, and the success rate is above 91%.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

Subject

Molecular Medicine,Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biotechnology

Reference20 articles.

1. Muchiri, N., and Kimathi, S. (2016, January 4). A Review of Applications and Potential Applications of UAV. Proceedings of the 2016 Annual Conference on Sustainable Research and Innovation, Milan, Italy.

2. Review on the Technological Development and Application of UAV Systems;Fan;Chin. J. Electron.,2020

3. Zhang, C., Liu, Y., and Hu, C. (2022). Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm. Biomimetics, 7.

4. Zhu, X. (2020, January 27). Analysis of Military Application of UAV Swarm Technology. Proceedings of the 2020 3rd International Conference on Unmanned Systems, Harbin, China.

5. Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets;Peng;Complex Syst. Model. Simul.,2021

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