A Molecular Force-Based Deployment Algorithm for Flight Coverage Maximization of Multi-Rotor UAV

Wang, Xi; Tan, Guozhen; Liu, Xiya; Zhao, Zhixiang

doi:10.1007/s10846-018-0938-x

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In [23], an estimation of the control force for network connectivity preservation and collision avoidance is proposed. In [24 ] [25 ], an enhanced virtual force algorithm is proposed, which improves the accuracy of the virtual force model. In [26], the repulsive forces between vehicles or objects and the environment can be obtained according to the potential function, and an application of distance-based collision avoidance is implemented.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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confidence: 99%

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

et al. 2020

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“…In [18], The enhanced deployment algorithms (EDA-I and EDA-II) were proposed to achieve a high sensing coverage ratio in the monitored field. In [19], a virtual force-based deployment algorithm-the virtual force algorithm (VFA)-was proposed, which was then improved in [20][21][22]. The Voronoi diagram is a famous computational geometric structure [23], which has been widely used for sensor deployment problems [24][25][26][27][28][29].…”

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

Wang

Tan

et al. 2020

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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.

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A Multiarea On-Demand Classification Constellation Design for Satellite IoT

Tang,

Xu,

Huang

et al. 2024

IEEE Internet Things J.

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A Molecular Force-Based Deployment Algorithm for Flight Coverage Maximization of Multi-Rotor UAV

Cited by 5 publications

References 18 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

A Multiarea On-Demand Classification Constellation Design for Satellite IoT

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