Skip to main content
Log in

Survivor searching in a dynamically changing flood zone by multiple unmanned aerial vehicles

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

This paper presents a control strategy for survivor searching in a dynamically changing flood zone using a group of unmanned aerial vehicles (UAVs). Assuming that there are multiple groups of the survivors, the positions which are time-varying and cannot be accurately located, the control strategy requires the UAVs to optimally cover possible locations of survivors in the flood zone. A robust adaptive controller has been proposed to implement the strategy, the feasibility of which is verified under simulations in the presence of time-varying uncertainties.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Tadokoro S (2019) Disaster robotics: results from the ImPACT tough robotics challenge. Springer, New York

    Book  Google Scholar 

  2. Murphy R (2014) Disaster robotics. The MIT Press, New York

    Book  Google Scholar 

  3. Bai Y, Asami K, Svinin M, Magid E (2020) Cooperative multi-robot control for monitoring an expanding flood area. In: Proceedings of 17th International Conference on Ubiquitous Robots

  4. Norouzi Ghazbi S, Aghli Y, Alimohammadi M, Akbari AA (2016) Quadrotors unmanned aerial vehicles: a review. Int J Smart Sens Intell Syst 9:309–333

    Google Scholar 

  5. Miyashita R, Sunada S, Tanabe Y, Aoyama T (2014) Comparison between a single rotor helicopter and a quadrotor uav. J Jpn Soc Aeronaut Space Sci 62(6):193–197

    Google Scholar 

  6. Meyer J, Sendobry A, Kohlbrecher S, Klingauf U, von Stryk O (2012) Comprehensive simulation of quadrotor UAVs using ROS and Gazebo. Springer, Berlin

    Book  Google Scholar 

  7. Chung S-J, Paranjape AA, Dames P, Shen S, Kumar V (2018) A survey on aerial swarm robotics. IEEE Trans Robot 34(4):837–855

    Article  Google Scholar 

  8. Cortes J, Martinez S, Karatas T, Bullo F (2004) Coverage control for mobile sensing networks. IEEE Trans Robot Autom 20(2):243–255

    Article  Google Scholar 

  9. Michael N, Zavlanos MM, Kumar V, Pappas GJ (2008) Distributed multi-robot task assignment and formation control. In: IEEE international conference on robotics and automation, pp 128–133

  10. Miah S, Panah AY, Fallah MMH, Spinello D (2017) Generalized non-autonomous metric optimization for area coverage problems with mobile autonomous agents. Automatica 80:295–299

    Article  MathSciNet  Google Scholar 

  11. Lee SG, Diaz-Mercado Y, Egerstedt M (2015) Multirobot control using time-varying density functions. IEEE Trans Rob 31(2):489–493

    Article  Google Scholar 

  12. Chevet T, Maniu CS, Vlad C, Zhang Y (2019) Guaranteed voronoi-based deployment for multi-agent systems under uncertain measurements. In: 2019 18th European Control Conference (ECC), pp 4016–4021

  13. Li S, Li B, Yu J, Zhang L, Zhang A, Cai K (2020) Probabilistic threshold k-ann query method based on uncertain voronoi diagram in internet of vehicles. IEEE transactions on intelligent transportation systems, pp 1–11

  14. Xiao J, Wang G, Zhang Y, Cheng L (2020) A distributed multi-agent dynamic area coverage algorithm based on reinforcement learning. IEEE Access 8:33 511-33 521

    Article  Google Scholar 

  15. Miah S, Fallah MMH, Spinello D (2017) Non-autonomous coverage control with diffusive evolving density. IEEE Trans Autom Control 62(10):5262–5268

    Article  Google Scholar 

  16. Bai Y, Svinin M, Magid E (2020) Multi-robot control for adaptive caging and tracking of a flood area. In: Proceedings of SICE Annual Conference 2020 (SICE 2020)

  17. Bai Y, Wang Y, Svinin M, Magid E, Sun R (2020) Function approximation technique based immersion and invariance control for unknown nonlinear systems. IEEE Control Syst Lett 4(4):934–939

    Article  MathSciNet  Google Scholar 

  18. Huang A, Chen Y (2004) Adaptive sliding control for single-link flexible-joint robot with mismatched uncertainties. Control Syst Technol IEEE Trans 12:770–775

    Article  Google Scholar 

  19. Bai Y, Svinin M, Yamamoto M (2017) Adaptive trajectory tracking control for the ball-pendulum system with time-varying uncertainties. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2083–2090

  20. Bai Y, Svinin M, Yamamoto M (2018) Function approximation based control for non-square systems. SICE J Control Measur Syst Integr 11(6):477–485

    Article  Google Scholar 

  21. Chien M, Huang A (2007) Adaptive control for flexible-joint electrically driven robot with time-varying uncertainties. IEEE Trans Industr Electron 54(2):1032–1038

    Article  Google Scholar 

  22. Khorashadizadeh S, Fateh MM (2017) Uncertainty estimation in robust tracking control of robot manipulators using the fourier series expansion. Robotica 35(2):310–336

    Article  Google Scholar 

  23. Zirkohi MM (2017) Direct adaptive function approximation techniques based control of robot manipulators. J Dyn Syst Measur Control 140(1)

  24. Liang J-W, Chen H-Y, Wu Q-W (2015) Active suppression of pneumatic vibration isolators using adaptive sliding controller with self-tuning fuzzy compensation. J Vib Control 21(2):246–259

    Article  Google Scholar 

  25. Ebeigbe D, Nguyen T, Richter H, Simon D (2020) Robust regressor-free control of rigid robots using function approximations. IEEE Trans Control Syst Technol 28(4):1433–1446

    Article  Google Scholar 

  26. Schwager M, Rus D, Slotine J-J (2009) Decentralized, adaptive coverage control for networked robots. Int J Robot Res 28(3):357–375

    Article  Google Scholar 

  27. Wang BH, Wang DB, Ali ZA, Ting BT, Wang H (2019) An overview of various kinds of wind effects on unmanned aerial vehicle. Measur Control 52(7–8):731–739

    Article  Google Scholar 

  28. Bai Y, Wang Y, Svinin M, Magid E, Sun R (2022) Adaptive multi-agent coverage control with obstacle avoidance. IEEE Control Syst Lett 6:944–949

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported, in part, by the Japan Science and Technology Agency, the JST Strategic International Collaborative Research Program, Grant No. JPMJSC18E4.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Koki Asami.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was presented in part at the joint symposium with the 15th International Symposium on Distributed Autonomous Robotic Systems 2021 and the 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics 2021 (Online, June 1–4, 2021).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asami, K., Bai, Y., Svinin, M. et al. Survivor searching in a dynamically changing flood zone by multiple unmanned aerial vehicles. Artif Life Robotics 27, 292–299 (2022). https://doi.org/10.1007/s10015-022-00755-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-022-00755-w

Keywords

Navigation