Advertisement

Adaptive Data Sharing Algorithm for Aerial Swarm Coordination in Heterogeneous Network Environments (Short Paper)

  • Yanqi Zhang
  • Bo ZhangEmail author
  • Xiaodong Yi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

With the development of unmanned aerial vehicle (UAV) systems, multi-UAV cooperation has attracted noticeable attention. In response to the communication constraints faced in UAV swarm coordination, both the lazy and the eager strategies were proposed to enable swarm-wide reliable information exchange to further behavior coordination for UAV swarms. However, these two algorithms are only evaluated in a fixed and homogeneous network scenario. Hence, how to choose the proper information exchange strategy for a UAV swarm in realistic dynamic and heterogeneous network environments remains an open while interesting problem. Therefore, in this paper, we first evaluate the convergence and payload cost of both strategies for robotic swarms in realistic network scenarios. Then we propose a novel online adaptive information exchange strategy by adopting single relay selection schemes to ensure low payload and fast convergence in various network environments. Numerical results reveal our novel strategy performs well across different network scenarios in terms of convergence and payload cost, showing its robustness, adaptive capability and potential applications in UAV swarms.

Keywords

Multi-UAV Single relay selection Heterogeneous network environments 

References

  1. 1.
    Gazebo official website. http://www.gazebosim.org
  2. 2.
    Ros official website. http://www.ros.org
  3. 3.
    Bekmezci, I., Sahingoz, O.K., Temel, S.: Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)CrossRefGoogle Scholar
  4. 4.
    Bletsas, A.A.: Intelligent antenna sharing in cooperative diversity wireless networks. Ph.D. thesis, Massachusetts Institute of Technology (2005)Google Scholar
  5. 5.
    Davis, D.T., Chung, T.H., Clement, M.R., Day, M.A.: Consensus-based data sharing for large-scale aerial swarm coordination in lossy communications environments. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3801–3808. IEEE (2016)Google Scholar
  6. 6.
    Day, M.A., Clement, M.R., Russo, J.D., Davis, D., Chung, T.H.: Multi-UAV software systems and simulation architecture. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 426–435. IEEE (2015)Google Scholar
  7. 7.
    Hauert, S., et al.: Reynolds flocking in reality with fixed-wing robots: communication range vs. maximum turning rate. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5015–5020. IEEE (2011)Google Scholar
  8. 8.
    Jing, Y., Jafarkhani, H.: Single and multiple relay selection schemes and their achievable diversity orders. IEEE Trans. Wirel. Commun. 8(3), 1414–1423 (2009)CrossRefGoogle Scholar
  9. 9.
    Ren, W., Beard, R.W., Atkins, E.M.: A survey of consensus problems in multi-agent coordination. In: 2005 Proceedings of the American Control Conference, pp. 1859–1864. IEEE (2005)Google Scholar
  10. 10.
    Sadek, A.K., Han, Z., Liu, K.R.: A distributed relay-assignment algorithm for cooperative communications in wireless networks. In: 2006 IEEE International Conference on Communications, ICC 2006, vol. 4, pp. 1592–1597. IEEE (2006)Google Scholar
  11. 11.
    Sreng, V., Yanikomeroglu, H., Falconer, D.D.: Relayer selection strategies in cellular networks with peer-to-peer relaying. In: 2003 IEEE 58th Vehicular Technology Conference, VTC 2003-Fall, vol. 3, pp. 1949–1953. IEEE (2003)Google Scholar
  12. 12.
    Wei, X., Fengyang, D., Qingjie, Z., Bing, Z., Hongchang, S.: A new fast consensus algorithm applied in rendezvous of multi-UAV. In: 2015 27th Chinese Control and Decision Conference (CCDC), pp. 55–60. IEEE (2015)Google Scholar
  13. 13.
    Zhang, Y., Zhang, B., Yi, X.: The design and implementation of swarm-robot communication analysis tool. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds.) GSKI 2017. CCIS, vol. 849, pp. 631–640. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-13-0896-3_62CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.State Key Laboratory of High Performance Computing (HPCL)National University of Defense Technology (NUDT)ChangshaChina
  2. 2.Artificial Intelligence Research Center (AIRC)National Innovation Institute of Defense Technology (NIIDT)BeijingChina

Personalised recommendations