Search Planning and Analysis for Mobile Targets with Robots

  • Shujin YeEmail author
  • Wai Kit Wong
  • Hai Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 300)


With robotics technologies advancing rapidly, there are many new robotics applications such as surveillance, mining tasks, search and rescue, and autonomous armies. In this work, we focus on use of robots for target searching. For example, a collection of Unmanned Aerial Vehicle (UAV) could be sent to search for survivor targets in disaster rescue missions. We assume that there are multiple targets. The moving speeds and directions of the targets are unknown. Our objective is to minimize the searching latency which is critical in search and rescue applications. Our basic idea is to partition the search area into grid cells and apply the divide-and-conquer approach. We propose two searching strategies, namely, the circuit strategy and the rebound strategy. The robots search the cells in a Hamiltonian circuit in the circuit strategy while they backtrack in the rebound strategy. We prove that the expected searching latency of the circuit strategy for a moving target is upper bounded by \(\frac{3n^2-4n+3}{2n}\) where n is the number of grid cells of the search region. In case of a static or suerfast target, we derive the expected searching latency of the two strategies. Simulations are conducted and the results show that the circuit strategy outperforms the rebound strategy.


Robot search Mobile target Search planning and analysis 



This work is partially supported by the Faculty Development Scheme (Ref. No. UGC/FDS14/E03/17 and UGC/FDS14/E01/17), The Deep Learning Research & Application Centre, and The Big Data & Artificial Intelligence Group in The Hang Seng University of Hong Kong.


  1. 1.
  2. 2.
  3. 3.
    Celikkanat, H., Sahin, E.: Steering self-organized robot flocks through externally guided individuals. Neural Comput. Appl. 19(6), 849–865 (2010)CrossRefGoogle Scholar
  4. 4.
    Couzin, I.D., Jens, K., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433(7025), 513–6 (2005)CrossRefGoogle Scholar
  5. 5.
    Cucker, F., Dong, J.G.: Avoiding collisions in flocks. IEEE Trans. Autom. Control 55(5), 1238–1243 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Delight, M., Ramakrishnan, S., Zambrano, T., MacCready, T.: Developing robotic swarms for ocean surface mapping. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5309–5315, May 2016.
  7. 7.
    Dell’Ariccia, G., Dell’Omo, G., Wolfer, D.P., Lipp, H.P.: Flock flying improves pigeons’ homing: GPS track analysis of individual flyers versus small groups. Anim. Behav. 76(4), 1165–1172 (2008)CrossRefGoogle Scholar
  8. 8.
    Dimidov, C., Oriolo, G., Trianni, V.: Random walks in swarm robotics: an experiment with kilobots. In: Dorigo, M., et al. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 185–196. Springer, Cham (2016). Scholar
  9. 9.
    Fang, H., Wei, Y., Chen, J., Xin, B.: Flocking of second-order multiagent systems with connectivity preservation based on algebraic connectivity estimation. IEEE Trans. Cybern. 47(4), 1067–1077 (2017). Scholar
  10. 10.
    Ferrante, E., Turgut, A.E., Stranieri, A., Pinciroli, C., Birattari, M., Dorigo, M.: A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat. Comput. 13(2), 225–245 (2014)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Fredette, D., Őzguner, U.: Swarm-inspired modeling of a highway system with stability analysis. IEEE Trans. Intell. Transp. Syst. 18(6), 1371–1379 (2017). Scholar
  12. 12.
    de Marina, H.G., Jayawardhana, B., Cao, M.: Distributed rotational and translational maneuvering of rigid formations and their applications. IEEE Trans. Robot. 32(3), 684–697 (2016). Scholar
  13. 13.
    Han, T., Ge, S.S.: Styled-velocity flocking of autonomous vehicles: a systematic design. IEEE Trans. Autom. Control 60(8), 2015–2030 (2015). Scholar
  14. 14.
    Liu, H., Chu, X., Leung, Y.W., Du, R.: Simple movement control algorithm for bi-connectivity in robotic sensor networks. IEEE J. Sel. Areas Commun. 28(7), 994–1005 (2010)CrossRefGoogle Scholar
  15. 15.
    Michael, R., Alejandro, C., Radhika, N.: Robotics. Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–9 (2014)CrossRefGoogle Scholar
  16. 16.
    Olfati-Saber, R., Jalalkamali, P.: Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Autom. Control 57(10), 2609–2614 (2012). Scholar
  17. 17.
    Qiang, W., Li, W., Cao, X., Meng, Y.: Distributed flocking with biconnected topology for multi-agent systems. In: International Conference on Human System Interactions (2016)Google Scholar
  18. 18.
    Rango, F.D., Palmieri, N., Yang, X., Marano, S.: Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks. Soft. Comput. 22(13), 4251–4266 (2018)CrossRefGoogle Scholar
  19. 19.
    Sabattini, L., Chopra, N., Secchi, C.: Decentralized connectivity maintenance for cooperative control of mobile robotic systems. Int. J. Robot. Res. 32(12), 1411–1423 (2013)CrossRefGoogle Scholar
  20. 20.
    Sakthivelmurugan, E., Senthilkumar, G., Prithiviraj, K., Devraj, K.T.: Foraging behavior analysis of swarm robotics system. In: MATEC Web of Conferences, vol. 144, p. 01013. EDP Sciences (2018)Google Scholar
  21. 21.
    Semnani, S.H., Basir, O.A.: Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems. IEEE Trans. Cybern. 45(1), 129–137 (2015). Scholar
  22. 22.
    Szwaykowska, K., Romero, L.M., Schwartz, I.B.: Collective motions of heterogeneous swarms. IEEE Trans. Autom. Sci. Eng. 12(3), 810–818 (2015). Scholar
  23. 23.
    Vásárhelyi, G., et al.: Outdoor flocking and formation flight with autonomous aerial robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3866–3873, September 2014.
  24. 24.
    Virágh, C., et al.: Flocking algorithm for autonomous flying robots. Bioinspir. Biomimet. 9(2), 025012 (2013)CrossRefGoogle Scholar
  25. 25.
    Ward, A.J.W., Herbert-Read, J.E., Sumpter, D.J.T., Jens, K.: Fast and accurate decisions through collective vigilance in fish shoals. Proc. Natl. Acad. Sci. U.S.A. 108(6), 2312–2315 (2011)CrossRefGoogle Scholar
  26. 26.
    Zhang, H., Chen, Z., Fan, M.: Collaborative control of multivehicle systems in diverse motion patterns. IEEE Trans. Control Syst. Technol. 24(4), 1488–1494 (2016). Scholar
  27. 27.
    Zhao, H., Wang, H., Wu, W., Wei, J.: Deployment algorithms for uav airborne networks toward on-demand coverage. IEEE J. Sel. Areas Commun. 36(9), 2015–2031 (2018). Scholar
  28. 28.
    Zhao, H., Liu, H., Leung, Y.W., Chu, X.: Self-adaptive collective motion of swarm robots. IEEE Trans. Autom. Sci. Eng. 15(4), 1533–1545 (2018)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  1. 1.Department of ComputingThe Hang Seng University of Hong KongSiu Lek YuenHong Kong

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