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Search Planning and Analysis for Mobile Targets with Robots

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Quality, Reliability, Security and Robustness in Heterogeneous Systems (QShine 2019)

Abstract

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.

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Notes

  1. 1.

    https://www.hongkongfp.com/2018/07/27/hong-kong-paraglider-missing-since-sunday-found-dead-lantau-island/.

  2. 2.

    https://www.usatoday.com/story/news/world/2014/03/07/malaysia-airlines-beijing-flight-missing/6187779/.

  3. 3.

    The actual sensing range could be a circle containing the square.

References

  1. DARPA Announces “Gremlins” UAS Program (2015). http://www.unmannedsystemstechnology.com/2015/09/darpa-announces-gremlins-uas-program/

  2. Department of Defense Announces Successful Micro-Drone Demonstration, January 2017. https://www.defense.gov/News/News-Releases/News-Release-View/Article/1044811/department-of-defense-announces-successful-micro-drone-demonstration/

  3. Celikkanat, H., Sahin, E.: Steering self-organized robot flocks through externally guided individuals. Neural Comput. Appl. 19(6), 849–865 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. Cucker, F., Dong, J.G.: Avoiding collisions in flocks. IEEE Trans. Autom. Control 55(5), 1238–1243 (2010)

    Article  MathSciNet  Google Scholar 

  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. https://doi.org/10.1109/ICRA.2016.7487742

  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)

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-44427-7_16

    Chapter  Google Scholar 

  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). https://doi.org/10.1109/TCYB.2016.2537307

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1109/TITS.2016.2619266

    Article  Google Scholar 

  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). https://doi.org/10.1109/TRO.2016.2559511

    Article  Google Scholar 

  13. Han, T., Ge, S.S.: Styled-velocity flocking of autonomous vehicles: a systematic design. IEEE Trans. Autom. Control 60(8), 2015–2030 (2015). https://doi.org/10.1109/TAC.2015.2400664

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  15. Michael, R., Alejandro, C., Radhika, N.: Robotics. Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–9 (2014)

    Article  Google Scholar 

  16. Olfati-Saber, R., Jalalkamali, P.: Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Autom. Control 57(10), 2609–2614 (2012). https://doi.org/10.1109/TAC.2012.2190184

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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). https://doi.org/10.1109/TCYB.2014.2328659

    Article  Google Scholar 

  22. Szwaykowska, K., Romero, L.M., Schwartz, I.B.: Collective motions of heterogeneous swarms. IEEE Trans. Autom. Sci. Eng. 12(3), 810–818 (2015). https://doi.org/10.1109/TASE.2015.2403253

    Article  Google Scholar 

  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. https://doi.org/10.1109/IROS.2014.6943105

  24. Virágh, C., et al.: Flocking algorithm for autonomous flying robots. Bioinspir. Biomimet. 9(2), 025012 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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). https://doi.org/10.1109/TCST.2015.2487864

    Article  Google Scholar 

  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). https://doi.org/10.1109/JSAC.2018.2864376

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Acknowledgements

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.

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Correspondence to Shujin Ye .

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Ye, S., Wong, W.K., Liu, H. (2020). Search Planning and Analysis for Mobile Targets with Robots. In: Chu, X., Jiang, H., Li, B., Wang, D., Wang, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-38819-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-38819-5_1

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