LAPSE:2020.0512
Published Article
LAPSE:2020.0512
A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network
Xi Wang, Guan-zheng Tan, Fan-Lei Lu, Jian Zhao, Yu-si Dai
May 22, 2020
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.
Keywords
coverage maximization, deployment algorithm, molecular force, MWSN, UAV swarm
Suggested Citation
Wang X, Tan GZ, Lu FL, Zhao J, Dai YS. A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network. (2020). LAPSE:2020.0512
Author Affiliations
Wang X: School of Automation, Central South University, Changsha 410083, China [ORCID]
Tan GZ: School of Automation, Central South University, Changsha 410083, China
Lu FL: School of Automation, Central South University, Changsha 410083, China
Zhao J: School of Automation, Central South University, Changsha 410083, China
Dai YS: School of Automation, Central South University, Changsha 410083, China [ORCID]
Journal Name
Processes
Volume
8
Issue
3
Article Number
E369
Year
2020
Publication Date
2020-03-22
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8030369, Publication Type: Journal Article
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LAPSE:2020.0512
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doi:10.3390/pr8030369
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May 22, 2020
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CC BY 4.0
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[v1] (Original Submission)
May 22, 2020
 
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May 22, 2020
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Original Submitter
Calvin Tsay
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