A Low Latency Clustering Method for Large-Scale Drone Swarms

Zhu, Xiaopan; Bian, Chunjiang; Chen, Yu; Chen, Shi

doi:10.1109/access.2019.2960934

Cited by 21 publications

(9 citation statements)

References 30 publications

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“…By considering the network environment changes, the proposed architecture switches to MANET mode to guarantee UAVs’ carrier transmissions, so as to reduce the transmission delays of UAVs. Zhu et al proposed a low latency clustering method for large scale drone swarms [ 10 ]. The authors established a model which solves the relationship between the number of drones and the number of cluster heads.…”

Section: Related Workmentioning

confidence: 99%

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

Lee

2021

Sensors

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In the last ten years, supported by the advances in technologies for unmanned aerial vehicles (UAVs), UAVs have developed rapidly and are utilized for a wide range of applications. To operate UAVs safely, by exchanging control packets continuously, operators should be able to monitor UAVs in real-time and deal with any problems immediately. However, due to any networking problems or unstable wireless communications, control packets can be lost or transmissions can be delayed, which causes the unstable drone control. To overcome this limitation, in this paper, we propose MuTran for enabling reliable UAV control. MuTran considers the packet type and duplicates only control packets, not data packets. After that, MuTran transmits the original and duplicate packets through multiple protocols and paths to improve the reliability of control packet transmissions. We designed MuTran and conducted a lot of theoretical analyses to demonstrate the validity of MuTran and analyze it from various aspects. We implemented MuTran on real devices and evaluated MuTran using the devices. We conducted experiments to verify the limitations of the existing systems and demonstrate that control packets can be transmitted more stably by using MuTran. Through the analysis and experimental results, we confirmed that MuTran reduces the control packet transfer delay, which improves the reliability and stability of controlling UAVs.

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Section: Related Workmentioning

confidence: 99%

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

Lee

2021

Sensors

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“…. Similar to [22], the CM delay T m when the UAV is a CM should be the transmission delay of each CM multiplying the probability that the UAV is a CM, which can be formulated as…”

Section: Number Of Chsmentioning

confidence: 99%

“…. According to Lemma 3, nonconvex constraint (22) in (P1.5) is transformed to a convex one. Hence, we can obtain a lower bound of the optimal value of problem (P1.5) via solving the approximate problem…”

Section: Trajectory Optimization With Fixed Bandwidth Allocationmentioning

confidence: 99%

A large-scale clustering and 3D trajectory optimization approach for UAV swarms

Zhou

Qian

et al. 2021

Sci. China Inf. Sci.

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With the significant development of unmanned aerial vehicles (UAVs) technologies, a rapid increase on the use of UAV swarms in a wide range of civilian and emergency applications has been witnessed. However, how to efficiently network the large-scale UAVs and implement the swarms applications without infrastructure support in remote areas is challenging. In this paper, we investigate a hierarchal large-scale infrastructure-less UAV swarm scenario, where numerous UAVs surveil and collect data from the ground and a ferry UAV (Ferry UAV) is designated to carry back all their collected data. We can divide UAV swarms into different areas based on their geographic locations due to the wide range of surveillance. To improve data collection efficiency of Ferry UAV, we introduce a single super cluster head (Super-CH) UAV in each area which can be selected by the proposed modified k-means clustering algorithm with low latency. Then, we design an iterative approach to optimize the 3-dimensional (3D) trajectory of Ferry UAV such that its data collection mission completion time is minimized. Numerical results show the efficiency and low-latency of the proposed clustering algorithm, and the proposed 3D optimal trajectory design for large-scale UAV swarms data collection admits better performance than that with fixed altitude.

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“…Based on these observations, it is evident that multiple flapping wings in V-formation flight can result in significant performance benefits for the group compared to a single flapping wing. Several research studies investigated the feasibility of swarms or groups of drones flying in formation [20][21][22][23]. However, the arrangement of multiple pairs of flapping wings in a single vehicle based on the formation flight of birds has not been reported up to this point.…”

Section: Introductionmentioning

confidence: 99%

On the Aerodynamic Analysis and Conceptual Design of Bioinspired Multi-Flapping-Wing Drones

Billingsley

Ghommem

Vasconcellos

et al. 2021

Drones

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Many research studies have investigated the characteristics of bird flights as a source of bioinspiration for the design of flapping-wing micro air vehicles. However, to the best of the authors’ knowledge, no drone design targeted the exploitation of the aerodynamic benefits associated with avian group formation flight. Therefore, in this work, a conceptual design of a novel multi-flapping-wing drone that incorporates multiple pairs of wings arranged in a V-shape is proposed in order to simultaneously increase the propulsive efficiency and achieve superior performance. First, a mission plan is established, and a weight estimation is conducted for both 3-member and 5-member configurations of the proposed air vehicle. Several wing shapes and airfoils are considered, and aerodynamic simulations are conducted, to determine the optimal planform, airfoil, formation angle, and angle of attack. The simulation results reveal that the proposed bioinspired design can achieve a propulsive efficiency of 73.8%. A stability analysis and tail sizing procedure are performed for both 3-member and 5-member configurations. In addition, multiple flapping mechanisms are inspected for implementation in the proposed designs. Finally, the completed prototypes’ models of the proposed multi-flapping-wing air vehicles are presented, and their features are discussed. The aim of this research is to provide a framework for the conceptual design of bioinspired multi-flapping-wing drones and to demonstrate the sizing, weight estimation, and design procedures for this new type of air vehicles. This work establishes the first multi-flapping-wing drone design which exploits the aerodynamic features of the V-formation flight observed in birds to achieve superior performance in terms of payload and endurance.

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A Low Latency Clustering Method for Large-Scale Drone Swarms

Cited by 21 publications

References 30 publications

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

Enabling Reliable UAV Control by Utilizing Multiple Protocols and Paths for Transmitting Duplicated Control Packets

A large-scale clustering and 3D trajectory optimization approach for UAV swarms

On the Aerodynamic Analysis and Conceptual Design of Bioinspired Multi-Flapping-Wing Drones

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