Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Wu, Weihuan; Zhang, Xiangyin; Miao, Yuqing

doi:10.3390/biomimetics7040214

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…When considering the effect of the obstacle, the visibility may not be full for the pursuer and evader. Wu et al [ 24 ] proposed a flock control algorithm that can guide swarming aerial vehicles with obstacle avoidance in a dynamic and unknown 3D environment. Dong et al [ 25 ] incorporated the artificial potential field method to design a collision-free evasion model.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

Li,

Yang

2024

Biomimetics

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In complex and dynamic environments, traditional pursuit–evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit–evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

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Section: Introductionmentioning

confidence: 99%

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

Li,

Yang

2024

Biomimetics

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“…Therefore, the UAV is widely used in the military field and has become more and more significant in modern warfare [ 1 , 2 , 3 ]. Through collaboration among UAVs, the UAV swarm consisting of multiple UAVs can overcome the limitations of a single UAV in perception and execution and complete complex tasks [ 4 , 5 , 6 , 7 , 8 , 9 ], such as dynamic task allocation, collaborative reconnaissance, and attack-defense confrontation. Among these tasks, the method for attack-defense confrontation is highly valued as an emerging military technique that requires that the UAV make proper decisions autonomously according to the situation.…”

Section: Introductionmentioning

confidence: 99%

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

Chi

Wei

et al. 2023

Biomimetics

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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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Revolutionizing swarm dynamics: the role of receptive fields in enhancing convergence and stability

Liu,

He,

et al. 2024

Phys. Scr.

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The classic Vicsek model, while influential in understanding swarm behavior, has limitations in achieving motion consensus and convergence speed, especially under varying conditions of density and noise. This study aims to introduce a novel receptive field mechanism to theVicsek model to enhance its performance in terms of motion consensus and convergence speed within swarms. The modified model divides a particle's surrounding area into excitation and inhibition zones based on distinct functions. This structural modification is designed to enrich evolutionary behavior and improve consensus convergence capabilities. Experimental outcomes indicate that the proposed model achieves faster convergence rates towards motion consensus under various density and noise conditions compared to traditional models.Specifically, while classic Vicsek models fail to converge to an overall polarization state under high noise levels and exhibit quasi-periodic oscillations, the enhanced model demonstrates stable convergence without oscillatory behavior across both low- and high-noise environments. The findings highlight the superior evolutionary consistency characteristics of the improved model, offering new theoretical and practical insights into the stability and controllability of swarms. This advancement presents significant implications for the development of more robust swarm systems.

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Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Cited by 6 publications

References 38 publications

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

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

Revolutionizing swarm dynamics: the role of receptive fields in enhancing convergence and stability

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