Skip to main content
Springer Nature Link
Log in

A Non-Convex Optimization Approach to Dynamic Coverage Problem of Multi-agent Systems in an Environment with Obstacles

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

In this paper, a cooperative region reconnaissance problem is investigated where a group of agents are required to fly across and detect events occur in an environment with static obstacles until an effective coverage is achieved. First, the region reconnaissance is formulated as a non-convex optimization problem. A coverage performance index with additional collision and obstacle avoidance constraints is given. Since the optimization index is an implicit function of state variables and cannot be used to compute gradients on state variables directly, an approximate optimization index is selected. Then, a non-convex optimization-based coverage algorithm is proposed to find the optimal reconnaissance location for each agent and guarantee no collisions trajectories among agents and obstacles. Finally, simulation experiments are performed to verify the effectiveness of the proposed approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Atn G M, Stipanovi D M, and Voulgaris P G, Supervised coverage control of multi-agent systems, Automatica, 2014, 50(11): 2936–2942.

    Article  MathSciNet  Google Scholar 

  2. Zhong M and Cassandras C G, Distributed coverage control and data collection with mobile sensor networks, IEEE Transactions on Automatic Control, 2011, 56(10): 2445–2455.

    Article  MathSciNet  Google Scholar 

  3. Cortés J, Coverage control by multi-robot networks with limited-range anisotropic sensory, International Journal of Control, 2009, 82(6): 1113–1121.

    Article  MathSciNet  Google Scholar 

  4. Zhai C, He F, Hong Y, et al., Coverage-based interception algorithm of multiple interceptors against the target involving decoys, Journal of Guidance, Control, and Dynamics, 2016, 39(7): 1647–1653.

    Article  Google Scholar 

  5. Cortes J, Martinez S, and Bullo F, Spatially-distributed coverage optimization and control with limited-range interactions, ESAIM: Control, Optimisation and Calculus of Variations, 2005, 11(4): 691–719.

    MathSciNet  MATH  Google Scholar 

  6. Meguerdichian S, Koushanfar F, Potkonjak M, et al., Coverage problems in Wireless Ad Hoc Sensor Networks 20th IEEE INFOCOM, 2001, 1380–1387.

    Google Scholar 

  7. Hussein I I and Stipanovic D M, Effective coverage control for mobile sensor networks with guaranteed collision avoidance, IEEE Transactions on Control Systems Technology, 2007, 15(4): 642–657.

    Article  Google Scholar 

  8. Hussein I I, Stipanovic D M, and Wang Y, Reliable coverage control using heterogeneous vehicles, Proc. Int. Decision and Control, 2007, IEEE Conference on. IEEE, New Orleans, LA, USA, 2007, 6142–6147.

    Google Scholar 

  9. Song C, Feng G, andWang Y, Decentralized dynamic coverage control for mobile sensor networks in a non-convex environment, Asian Journal of Control, 2013, 15(2): 512–520.

    Article  MathSciNet  Google Scholar 

  10. Cortes J, Martnez S, Karatas T, et al., Coverage control for mobile sensing network, IEEE Trans. Robotics and Automation, 2004, 20(2): 243–255.

    Article  Google Scholar 

  11. Gusrialdi A, Hirche S, Asikin D, et al, Voronoi-based coverage control with anisotropic sensors and experimental case study, Intelligent Service Robotics, 2009, 2(4): 195–204.

    Article  Google Scholar 

  12. Zhai C, Sweep coverage of discrete time multi-robot networks with general topologies, Kybernetika, 2014, 1(1): 19–31.

    MathSciNet  MATH  Google Scholar 

  13. Wagner I, Lindenbaum M, and Bruckstein A, Distributed covering by ant-robots using evaporating traces, IEEE Trans. Robotics Automation, 1999, 15(5): 918–933.

    Article  Google Scholar 

  14. Breitenmoser A, Schwager M, Metzger J C, et al., Voronoi coverage of non-convex environments with a group of networked robots, Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 2010, 4982–4989.

    Google Scholar 

  15. Pimenta L C A, Kumar V, Mesquita R C, et al., Sensing and coverage for a network of heterogeneous robots, Proceedings of 47th IEEE Conference on Decision and Control, Cancun, Mexico, 2008, 3947–3952.

    Google Scholar 

  16. Caicedo-Nunez C H and Zefran M, A coverage algorithm for a class of non-convex regions, Proceedings of 47th IEEE Conference on Decision and Control, Cancun, Mexico, 2008, 4244–4249.

    Google Scholar 

  17. Ma L, He F, Wang L, et al., Multi-agent coverage control design with dynamic sensing regions, Control Theory and Technology, 2018, 16(3): 161–172.

    Article  MathSciNet  Google Scholar 

  18. Ma L, He F, Wang L, et al., Dynamic coverage control design of multi-agent systems under ellipse sensing regions, Kybernetika, 2018, 54(5): 991–1010.

    MathSciNet  MATH  Google Scholar 

  19. Frappier C, A repeated Leibniz integral rule, International Journal of Pure and Applied Mathematics, 2008, 44(2): 151–154.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Control and Simulation Center, Harbin Institute of Technology, Harbin, 150080, China

    Longbiao Ma, Fenghua He, Long Wang, Changxi Li & Yu Yao

Authors
  1. Longbiao Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fenghua He
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Long Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Changxi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yu Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Longbiao Ma.

Additional information

This paper was partially supported by the National Natural Science Foundation of China under Grant Nos. 61473099, 61333001.

This paper was recommended for publication by Editor HONG Yiguang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, L., He, F., Wang, L. et al. A Non-Convex Optimization Approach to Dynamic Coverage Problem of Multi-agent Systems in an Environment with Obstacles. J Syst Sci Complex 33, 426–445 (2020). https://doi.org/10.1007/s11424-020-8085-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-020-8085-4

Keywords