Cooperation-Based Gene Regulatory Network for Target Entrapment

Wu, Meng; Zhou, Yun; Zhu, Xiaomin; Ma, Li; Yuan, Yutong; Fang, Taosheng; Wang, Ji; Bao, Weidong; Fan, Zhun

doi:10.1007/978-3-030-26369-0_6

Cited by 9 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Wu et al [ 12 ] proposed a collaborative GRN (C-GRN) model that enables peer collaboration between agents. This model enables agents to discover and reinforce weak areas in the formed patterns.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network

Hong,

Chen,

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Quadrotor unmanned aerial vehicles (UAVs) often encounter intricate environmental and dynamic limitations in real-world applications, underscoring the significance of proficient trajectory planning for ensuring both safety and efficiency during flights. To tackle this challenge, we introduce an innovative approach that harmonizes sophisticated environmental insights with the dynamic state of a UAV within a potential field framework. Our proposition entails a quadrotor trajectory planner grounded in a kinodynamic gene regulation network potential field. The pivotal contribution of this study lies in the amalgamation of environmental perceptions and kinodynamic constraints within a newly devised gene regulation network (GRN) potential field. By enhancing the gene regulation network model, the potential field becomes adaptable to the UAV’s dynamic conditions and its surroundings, thereby extending the GRN into a kinodynamic GRN (K-GRN). The trajectory planner excels at charting courses that guide the quadrotor UAV through intricate environments while taking dynamic constraints into account. The amalgamation of environmental insights and kinodynamic constraints within the potential field framework bolsters the adaptability and stability of the generated trajectories. Empirical results substantiate the efficacy of our proposed methodology.

show abstract

Section: Related Workmentioning

confidence: 99%

Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network

Hong,

Chen,

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, Meng and Guo [21] introduced an evolving GRN method to adjust the structure and parameters of GRN. Lately, Wu et al [22] proposed a C-GRN model capable of peer collaboration, which allows agents to discover and reinforce the weak parts of the formed pattern. Yuan et al [23] combined tracking-based H-GRN and the leaderfollower model and introduced a TH-GRN model that can be used in dynamic and complex environments.…”

Section: Introductionmentioning

confidence: 99%

VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence

Cai¹,

Li²,

Fan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

UAVs (Unmanned Aerial Vehicles) dynamic encirclement is an emerging field with great potential. Researchers often get inspirations from biological systems, either from macro-world like fish schools or bird flocks etc, or from microworld like gene regulatory networks. However, most swarm control algorithms rely on centralized control, global information acquisition, or communication between neighboring agents. In this work, we propose a distributed swarm control method based purely on vision without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purly on their installed omnidirectional vision sensors. A finite-statemachine describing the behavior model of each individual drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments.

show abstract

Multi-robot target entrapment using cooperative hierarchical gene regulatory network

Wu¹,

Zhu²,

Ma³

et al. 2023

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Cooperation-Based Gene Regulatory Network for Target Entrapment

Cited by 9 publications

References 18 publications

Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network

Trajectory Planner for UAVs Based on Potential Field Obtained by a Kinodynamic Gene Regulation Network

VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence

Multi-robot target entrapment using cooperative hierarchical gene regulatory network

Contact Info

Product

Resources

About