Collective Dynamics and Visual Interaction Topology of Bionic Flapping-wing Robot Flock

Tang, Qirong; Cui, Ying; Zhang, Jingtao; Xu, Pengjie; Zhang, Kun

doi:10.1109/icus52573.2021.9641193

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The development of self-organized and cue-based aggregation behaviors that can be deployed on physical robots is an active line of research (Garnier et al, 2008; Misir & Gökrem, 2021; Na et al, 2021; Schmickl & Hamann, 2011; Sion et al, 2022; Tang et al, 2021). Garnier et al (2008) modeled a self-organized aggregation behavior of cockroaches and then transferred to a swarm of micro-robots.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based aggregation for robot swarms

Sadeghi Amjadi,

Bilaloğlu,

Turgut

et al. 2023

Adaptive Behavior

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Aggregation, the gathering of individuals into a single group as observed in animals such as birds, bees, and amoeba, is known to provide protection against predators or resistance to adverse environmental conditions for the whole. Cue-based aggregation, where environmental cues determine the location of aggregation, is known to be challenging when the swarm density is low. Here, we propose a novel aggregation method applicable to real robots in low-density swarms. Previously, Landmark-Based Aggregation (LBA) method had used odometric dead-reckoning coupled with visual landmarks and yielded better aggregation in low-density swarms. However, the method’s performance was affected adversely by odometry drift, jeopardizing its application in real-world scenarios. In this article, a novel Reinforcement Learning-based Aggregation method, RLA, is proposed to increase aggregation robustness, thus making aggregation possible for real robots in low-density swarm settings. Systematic experiments conducted in a kinematic-based simulator and on real robots have shown that the RLA method yielded larger aggregates, is more robust to odometry noise than the LBA method, and adapts better to environmental changes while not being sensitive to parameter tuning, making it better deployable under real-world conditions.

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Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based aggregation for robot swarms

Sadeghi Amjadi,

Bilaloğlu,

Turgut

et al. 2023

Adaptive Behavior

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“…Furthermore, Shang and Bouffanais (2014) combine the above two methods and propose a hybrid neighbor selection method. The Delaunay triangulation rule is used to achieve the purpose of visually selecting neighbors (Tang et al , 2021; Yu et al , 2021). However, this rule has to master positions of all individuals without the limited interaction, which is equivalent to centralized control.…”

Section: Introductionmentioning

confidence: 99%

Distributed UAV swarm control framework with limited interaction for obstacle avoidance

Zhu

Deng

2022

AEAT

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Purpose The purpose of this paper is to propose a distributed unmanned aerial vehicle (UAV) swarm control method to ensure safety and obstacle avoidance during swarm flight and realize effective guidance. Design/methodology/approach This paper proposes a distributed UAV swarm control framework with limited interaction. UAVs in the swarm realize the selection of limited interactive neighbors according to the random line of sight and limited field of view. The designed interaction force and obstacle avoidance mechanism are combined to ensure the safety of UAVs and avoid collisions and obstacles. Informed UAVs are deployed to guide the swarm to move in the desired direction. Findings The proposed distributed swarm control framework achieves high safety of swarm motion and the participation of informed UAVs is conducive to the guidance of the UAV swarm. Simulation results demonstrate the feasibility and effectiveness of the proposed approach. Practical implications The UAV swarm control method developed in this paper can be applied to the practice of UAV swarm control. Originality/value A distributed UAV swarm control method is proposed to ensure the effective control of the consistency and safety of swarm motion.

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Collective Dynamics and Visual Interaction Topology of Bionic Flapping-wing Robot Flock

Cited by 2 publications

References 12 publications

Reinforcement learning-based aggregation for robot swarms

Reinforcement learning-based aggregation for robot swarms

Distributed UAV swarm control framework with limited interaction for obstacle avoidance

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