Swarm of Public Unmanned Aerial Vehicles as a Queuing Network

Kirichek, Ruslan; Paramonov, Alexander; Koucheryavy, Andrey

doi:10.1007/978-3-319-30843-2_12

Cited by 24 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the proposed optimal matching algorithm, we introduced a control experiment (Experiment 2) for comparison. In Experiment 2, the optimal matching algorithm is not adopted, and the UAVs fly according to the initial matching sequence, which is set to link= [1,2,3,4,5,6]. The rest of the settings of Experiment 2 remain consistent with those of Experiment 1.…”

Section: B Results Analysis Of the Comparative Experimentsmentioning

confidence: 99%

“…Angles  and  are the absolute flight path angle and the absolute course angle of a UAV relative to the ground coordinate system. The variation values of them between the current sampling period and the previous sampling period are considered as the guidance inputs, which are denoted as   and   as shown in Equation (2). By controlling these two angles and another guidance input U V , the trajectory of this UAV can be determined according to the proposed guidance law.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Therefore, many scholars have conducted research on cooperative tasks of UAVs, and related research has attained great development in recent years. For example, in [2 ], a UAV swarm is used to build a queuing network, and the length distribution of the shortest paths for cube and sphere formations of UAV swarms is determined. In [3], nPSO is applied to solve the problem of finding the optimal positions of UAVs that form a swarm in the presence of large obstacles such as buildings in an urban environment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

et al. 2020

View full text Add to dashboard Cite

To solve the problem of intercepting a moving target by a multirotor unmanned aerial vehicle (UAV) swarm, an optimal guidance strategy is proposed. The proposed guidance law is based on the integration of the classic pure pursuit guidance law and Kuhn-Munkres (KM) optimal matching algorithm, and virtual force potential functions are used to avoid collision. The proposed optimal guidance strategy is demonstrated by simulation experiments. The simulation results indicate that with the proposed optimal guidance strategy, a UAV swarm can intercept a moving target while maintaining the predetermined formation, and during the formation flight, the collisions between UAVs or the target can be avoided. Through a comparative experiment, the proposed optimal matching algorithm is proven to significantly reduce the average per-sampling-period total flight distance of all the UAVs and accelerate the interception process, and the formation completion degree is improved. INDEX TERMS Optimal matching; Unmanned aerial vehicles; Pursuit algorithms; Three-dimensional guidance law; Target interception; Collision avoidance. Xi WANG received his B.E. degree in electrical engineering in 2011 and MA.Eng. degree in control science and engineering in 2014, from Hunan University of Science and Technology. He is currently a doctoral student in the School of Automation, Central South University, China. His research areas are unmanned aerial vehicles, swarm intelligence, and related applications.

show abstract

Section: B Results Analysis Of the Comparative Experimentsmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…, M + 1, which are sorted (either clockwise or counterclockwise), and (xv 1 , yv 1 ) is equal to (xv M+1 , yv M+1 ). The centroid (c x , c y ) is the average position of all the points of the polygon, which can be obtained by Equations (5)- (7).…”

Section: Centroid Of the Convex Polygonmentioning

confidence: 99%

A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network

Wang

Tan

et al. 2020

Processes

View full text Add to dashboard Cite

In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved.

show abstract

“…In most applications of robots and robotic systems the organization of communication between its elements is required. To ensure mobility of the system elements, various technology of wireless communication is usually used [6][7][8][9][10][11]. Communication between elements should be capable of transmitting a control signal, as the monitoring data, including images and video.…”

Section: Introductionmentioning

confidence: 99%

Using the IEEE 802.11 Family of Standards for Communication between Robotic Systems

Vladyko

Paramonov

Kirichek

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

View full text Add to dashboard Cite

show abstract

Swarm of Public Unmanned Aerial Vehicles as a Queuing Network

Cited by 24 publications

References 12 publications

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm

A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network

Using the IEEE 802.11 Family of Standards for Communication between Robotic Systems

Contact Info

Product

Resources

About