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

Li, Huanlin; Cai, Yao; Hong, Juncao; Xu, Peng; Cheng, Hui; Zhu, Xiaomin; Hu, Bingliang; Hao, Zhifeng; Fan, Zhun

doi:10.1109/lra.2023.3236565

Cited by 5 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed GRN structure is based on VG-Swarm [ 1 ] improvement, which expands the dimensions of the GRN potential field and incorporates the state space of drones. We designed a real-time trajectory-planning method specifically tailored to this framework.…”

Section: Related Workmentioning

confidence: 99%

“…Our method improves upon the framework of a GRN model in a VG-Swarm [1], as shown in Figure 1. Within this enhanced framework, the method for computing the GRN concentration in each cell is calculated through Equations ( 1)- (6).…”

Section: Kinodynamic Grn Potential Fieldmentioning

confidence: 99%

“…This approach has found extensive application in swarm robot morphology generation and serves as a foundation for the development of autonomous systems. Based on the previous work by Li [ 1 ], who utilized the GRN potential field for shape generation and cooperative motion of a swarm of drones without communication, this study further enhanced the potential field by incorporating the kinematic state of the robots. This enhancement enabled the design of a local trajectory planning framework using the proposed potential field, thereby ensuring improved navigation capabilities.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Kinodynamic Grn Potential Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…R ECENTLY, the research of swarm unmanned aerial vehicles (UAV) has attracted increasing attention from researchers because of its wide applicability, such as environment exploration [1], autonomous search and rescue [2], target tracking and entrapping [3], flocking [4], task allocation [5], etc. To achieve an effective collaboration of swarm UAVs and avoid collisions, a reliable and accurate distance estimation of surrounding UAVs plays an important role.…”

Section: Introductionmentioning

confidence: 99%

An integrated approach towards coastal zone management in Shantou, China

Zhang¹,

張映璇²

View full text Add to dashboard Cite

UAV distance estimation plays an important role for path planning of swarm UAVs and collision avoidance. However, the lack of annotated data seriously hinder the related studies. In this paper, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the commonly used stereo triangulation can not stand for UAV scenes. The core reason is the position deviation issue of UAVs due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module (PCM), which can directly predict the offset between the image positions and the actual ones of UAVs and perform calculation compensation in stereo triangulation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. Consequently, the position deviation issue can be effectively alleviated. We conduct extensive experiments on UAVDE, and our proposed method can achieve a 38.84% performance improvement, which demonstrates its effectiveness and superiority. The code and dataset would be released.

show abstract

Self-organized swarm robot for multi-target trapping based on self-regulated density interaction

Zhou,

Tao,

Lei

et al. 2024

Information Sciences

View full text Add to dashboard Cite

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

Cited by 5 publications

References 20 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

An integrated approach towards coastal zone management in Shantou, China

Self-organized swarm robot for multi-target trapping based on self-regulated density interaction

Contact Info

Product

Resources

About