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

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Abstract

This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmanned Aerial Vehicles (UAV). With the help of this algorithm, a multi-rotor UAV can reach anywhere in the specific area by charging at the charging station several times. In this algorithm, a number of equal circles are used to cover the specific area (in a two-dimensional plane), and the center of each circle denotes a charging station. The radius of these circles is equal to the radius of action of the UAV. The number of the circles is set by the users. Under the combined effect of three virtual forces, the centers of the circles, called nodes, keep moving within the specific area, and multiple iterations are performed to adjust the location of each node. Finally, a proper deployment scheme for the charging stations is generated, which can achieve the working area maximization of the UAV by a certain number of charging stations. Simulation experiments were executed, and the results under different conditions show that the proposed algorithm can meet the expected requirements and has an advantage over three other algorithms in terms of coverage ratio. The experiment results also indicate that in the case of dense node density, the proposed algorithm has a better coverage performance than the case of sparse node density. The experimental data are available at https://figshare.com/projects/MFA/24064. The codes will be published later.

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References

  1. Kemper, F.P., Suzuki, K.A.O., Morrison, J.R.: UAV consumable replenishment: design concepts for automated service stations. In: Journal of Intelligent and Robotic Systems: Theory and Applications, pp 369–397 (2011)

  2. Wang, G., Cao, G., Porta, T.L.: Movement-assisted sensor deployment. Proc. - IEEE INFOCOM. 4, 2469–2479 (2004). https://doi.org/10.1109/INFCOM.2004.1354668

    Google Scholar 

  3. Lee, H.J., Kim, Y.H., Han, Y.H., Park, C.Y.: Centroid-based movement assisted sensor deployment schemes in wireless sensor networks. IEEE Veh. Technol. Conf. 1, 1 (2009). https://doi.org/10.1109/VETECF.2009.5379087

    Google Scholar 

  4. Wei, F., Xin-Hong, S.: A deployment strategy for coverage control in wireless sensor networks based on the blind-zone of Voronoi diagram. Acta Phys. Sin. 63, 220701 (2014)

    Google Scholar 

  5. Aziz, N.A.B.A., Mohemmed, A.W., Alias, M.Y.: A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In: 2009 International Conference of Networking, Sensory Control. https://doi.org/10.1109/ICNSC.2009.4919346, pp 602–607 (2009)

  6. Ab Aziz, N.A.: Wireless sensor networks coverage-energy algorithms based on particle swarm optimization. Emirates J. Eng. Res. 18(2), 41.52 (2013)

    Google Scholar 

  7. Qu, Y.: Wireless Sensor Network Deployment. Faculty of Engineering, Electrical Engineering Department, Florida International University (2013)

  8. Zou, Y., Chakrabarty, K.: Sensor deployment and target localization based on virtual forces. In: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, vol. 2, pp 1293–1303 (2003)

  9. Qu, Y., Georgakopoulos, S.: Relocation of wireless sensor network nodes using a genetic algorithm. In: 2011 IEEE 12th Annual Wireless and Microwave Technology Conference (WAMICON), pp 1–5. IEEE (2011)

  10. Computing, E., Jin, L., Jia, J., Sun, D.: Node distribution optimization in mobile sensor network based on multi-objective differential evolution algorithm. In: 2010 Fourth International Conference on Genetics Evolutionary Computing. https://doi.org/10.1109/ICGEC.2010.21, pp 51–54 (2010)

  11. Abo-Zahhad, M., Ahmed, S.M., Sabor, N., Sasaki, S.: Coverage maximization in mobile wireless sensor networks utilizing immune node deployment algorithm. In: Canadian Conference on Electrical and Computer Engineering (2014)

  12. Jia, J., Chen, J., Chang, G., Wen, Y., Song, J.: Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Comput. Math. Appl. 57, 1767–1775 (2009). https://doi.org/10.1016/j.camwa.2008.10.037

    Article  MathSciNet  MATH  Google Scholar 

  13. Costanzo, C., Loscrí, V., Natalizio, E., Razafindralambo, T.: Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network. Comput. Commun. 35, 1047–1055 (2012). https://doi.org/10.1016/j.comcom.2011.09.004

    Article  Google Scholar 

  14. Guo, Y., Liu, D., Liu, Y., Chen, M.: The coverage optimization for wireless sensor networks based on quantum-inspired cultural algorithm. In: Proceedings of 2013 Chinese Intelligent Automation Conference, pp 87–96. Springer (2013)

  15. Zou, Y., Chakrabarty, K.: Sensor deployment and target localization in distributed sensor networks. ACM Trans. Embed. Comput. Syst. 3, 61–91 (2004)

    Article  Google Scholar 

  16. Loscrí, V., Natalizio, E., Mitton, N.: Performance evaluation of novel distributed coverage techniques for swarms of flying robots. In: IEEE Wireless Communications and Networking Conference, WCNC, pp 3278–3283 (2014)

  17. Abo-Zahhad, M., Sabor, N., Sasaki, S., Ahmed, S.M.: A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks. Inf. Fusion 30, 36–51 (2016)

    Article  Google Scholar 

  18. Zhang, K, Collins, EG, Shi, D: Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction[J]. ACM Trans. Auton. Adapt. Syst. 7(2), 1–22 (2012)

    Article  Google Scholar 

  19. Zhang, K., Collins, E.G., Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Robot. Syst. Theory Appl. 72, 541–558 (2013). https://doi.org/10.1007/s10846-012-9800-8

    Article  Google Scholar 

  20. Zhang, K., Collins, E.G., Barbu, A.: A novel stochastic clustering auction for task allocation in multi-robot teams. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, pp 3300–3307 (2010)

  21. Zhang, K., Collins, E.G., Shi, D., Liu, X., Chuy, O.: A Stochastic Clustering Auction (SCA) for centralized and distributed task allocation in multi-agent teams. In: Distributed Autonomous Robotic Systems 8, pp 345–354 (2009)

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant No. 61403422.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Xi Wang, Guanzheng Tan, Xiya Liu & Zhixiang Zhao

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  1. Xi Wang
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  2. Guanzheng Tan
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  3. Xiya Liu
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  4. Zhixiang Zhao
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Correspondence to Guanzheng Tan.

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Wang, X., Tan, G., Liu, X. et al. A Molecular Force-Based Deployment Algorithm for Flight Coverage Maximization of Multi-Rotor UAV. J Intell Robot Syst 95, 1063–1078 (2019). https://doi.org/10.1007/s10846-018-0938-x

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