Study on Multi-UAV Task Clustering and Task Planning in Cooperative Reconnaissance

Zhao, Junwei; Zhao, Jianjun

doi:10.1109/ihmsc.2014.196

Cited by 17 publications

(9 citation statements)

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“…Zhao et al [32] and Li et al [33] introduced the ant algorithm to solve the task allocation problem. Zhao et al [34] designed an improved K-means clustering algorithm of simulated annealing algorithm, which makes the task assignments of drones swarm balanced. Chen et al [35] introduced the genetic algorithm into the task allocation problem of multiple drones.…”

Section: Related Workmentioning

confidence: 99%

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

et al. 2020

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Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme. INDEX TERMS Swarm of drones, distributed fog computing, latency, reliability, energy consumption.

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Section: Related Workmentioning

confidence: 99%

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

et al. 2020

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“…The unmanned aerial vehicle (UAV) swarm, first introduced by Schiller et al in 1993 [1], is an intelligent cooperative system that provides the advantages of multi-functional integration, flexible adaptation to dynamic scenarios and increased mission efficiency compared with traditional single UAV platforms [2,3]. The UAV swarm has been extensively employed in the civil and military mission scenarios such as remote sensing [4][5][6][7], precision agriculture [8], emergency rescue [9,10], cooperative reconnaissance [11], confrontation [12], and emergency wireless communication [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity

et al. 2022

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Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94% in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.

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“…Unmanned aerial vehicles (UAVs) have a wide range of applications such as surveying and mapping [1], search and rescue [2], reconnaissance [3], and many others [4][5][6][7][8]. Recently, using several UAVs became a hot topic, since these systems have several advantages comparing to systems containing only one vehicle.…”

Section: Introductionmentioning

confidence: 99%

Multiple UAV Systems: A Survey

2020

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Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.

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Study on Multi-UAV Task Clustering and Task Planning in Cooperative Reconnaissance

Cited by 17 publications

References 1 publication

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity

Multiple UAV Systems: A Survey

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