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%.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296010 | PMC |
| http://dx.doi.org/10.3390/biomimetics8020222 | DOI Listing |
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Article Synopsis
- Vision-based UAV swarming is emerging as a solution that addresses the issues of adaptability and scalability in traditional UAV communication systems.
- The study introduces a new finite perception visual (FPV) neighbor selection model, inspired by bird vision, which accounts for visual occlusion and sensor limitations and allows for flexible visual field widths.
- Through Monte Carlo simulations, the FPV model combined with the Acute Angle Test (AAT) method demonstrates a significant improvement in swarm performance by reducing communication load and enhancing safety and connectivity compared to traditional methods.
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