A fast coordination approach for large-scale drone swarm

Chen, Wu; Zhu, Jiayi; Liu, Jiajia; Guo, Hongzhi

doi:10.1016/j.jnca.2023.103769

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Remark 1. Since N is fixed after the deployment of the drone swarms, i.e., the number of elements in the antenna array is fixed after the deployments, and followed by the Proposition 2, the RSwarm algorithm has just less than the quadratic complexity, i.e., O(M p ), where p < 2, while comparing with [1,20,[62][63][64][65][66]. Furthermore, the RSwarm algorithm allows one to route a drone swarm with over 100 members, which is an improvement compared to the limitations of the existing AODV-based swarm members [15,59,60].…”

Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 99%

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

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The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

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Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 99%

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…From the perspective of graph theory, the investigation into time synchronization within UANETs inherently revolves around the consensus problem [16]. Furthermore, insights from research conducted in domains such as sensor network time synchronization, the collaborative control of multi-agent systems, and UAV swarm flight can provide valuable guidance and references for addressing the consensus problem [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Wu,

Bai,

2024

Drones

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Time synchronization is a critical prerequisite for unmanned aerial vehicle ad hoc networks (UANETs) to facilitate navigation and positioning, formation control, and data fusion. However, given the dynamic changes in UANETs, improving the convergence speeds of distributed consensus time synchronization algorithms with only local information poses a major challenge. To address this challenge, this study first establishes a convex model on the basis of graph theory and relevant theories of random matrices to approximate the original problem. Subsequently, three acceleration schemes for consensus algorithms are derived by minimizing the Frobenius norm of the iteration matrix. Additionally, this study provides a new upper bound for constant communication weights and discusses the limitations of existing metrics used to measure the convergence speeds of consensus algorithms. Finally, the proposed schemes are compared with existing ones through simulation. Our results indicate that the three proposed schemes can achieve faster convergence while maintaining high-precision synchronization in scenarios with static or known topological structures of networks. In scenarios where the topological structure of a UANET is time-varying and unknown, the scheme proposed in this paper achieves the fastest convergence speed.

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“…In the implementation of relative positioning technology in denied environments, some researchers have attempted to equip a single platform with multiple heterogeneous sensors, such as cameras [21], barometers [22], inertial navigation systems [23,24], and range and angle measurement devices [25,26]. These sensors, combined with multi-sensor data fusion algorithms [27,28], aim to achieve positioning. However, multi-sensor systems inevitably affect the flexibility of drones and are not suitable for large-scale, low-cost swarm systems [29].…”

Section: Related Workmentioning

confidence: 99%

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Di,

Guo

2024

Aerospace

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In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

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A fast coordination approach for large-scale drone swarm

Cited by 7 publications

References 33 publications

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

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