Skip to main content
SpringerLink
Log in

An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.

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

Access this article

Log in via an institution

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. Bellingham, J., Tillerson, M., Richards, A., How, J.P.: Multi-task assignment and path planning for cooperating UAVs. Presented at the Conference on Cooperative Control and Optimization (2001)

  2. Chung, H., Oh, S., Shim., D.H, Sastry, S.S.: Toward robotic sensor webs: algorithms, systems, and experiments. In: Proceedings of IEEE, 99-9, pp. 1562–1586 (2011)

  3. Shim, D.H.: A dynamic path generation method for a UAV swarm in the urban environment. Presented at the AIAA Guidance, Navigation, and Control Conference (2008)

  4. Schouwenaars, T., Moor, B.D., Feron, E., How, J.: Mixed integer programming for multi-vehicle path planning. In: European Control Conference, pp. 2603–2608 (2001)

  5. Richards, A., Kuwata, Y., How, J.: Experimental demonstrations of real-time MILP control. Presented at the AIAA Guidance, Navigation, and Control Conference and Exhibit, Austin, TX (2003)

  6. Earl, M., D’Andrea, R.: Iterative MILP methods for vehicle control problems. IEEE Trans. Robot. 21, 1158–1167 (2005)

    Article  Google Scholar 

  7. Jain, V., Grossmann, I.E.: Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS J. Comput. 13, 258–276 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chang, D.E., Shadden, S.C., Marsden, J.E., Olfati-Saber, R.: Collision avoidance for multiple agent systems. In: 42nd IEEE Conference on Decision and Control, Maui, Hawaii, USA (2003)

  9. Pettersson, P., Doherty, P.: Probabilistic roadmap based path planning for an autonomous unmanned helicopter. J. Intell. Fuzzy Syst. 17(4), 395–405 (2006)

    Google Scholar 

  10. Maza, I., Caballero, F., Capitan, J., Martines-de-Dios, J.R., Ollero, A.: Experimental results in multi-UAV coordination for disaster management and civil security applications. Presented at International Symposium on Unmanned Aerial Vehicle (2010)

  11. Moon, S., Shim, D.H.: Development of an efficient path planning algorithm using UAVs in cluttered environment. Presented at Institute of Control, Robotics and Systems Conference (2010)

  12. Bertsekas, D.P.: An auction algorithm for shortest paths. SIAM J. Optim. 1, 425–447 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ren, W., Beard, R.W., Atkin, E.M.: Information consensus in multivehicle control. IEEE Control Syst. Mag. 27(2), 71–82 (2007)

    Article  Google Scholar 

  14. Choi, H.L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)

    Article  Google Scholar 

  15. Viguria, A., Maza, I., Ollero, A.: Distributed service-based cooperation in aerial/ground robot teams applied to fire detection and extinguishing missions. Adv. Robot. 24, 1–23 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Aerospace and Mechanical Engineering, Korea Air Force Academy, Cheongju, South Korea

    Sangwoo Moon

  2. CNS/ATM & Satellite Navigation Research Center, Korea Aerospace Research Institute, Daejeon, South Korea

    Eunmi Oh

  3. Department of Aerospace Engineering, KAIST, Daejeon, South Korea

    David Hyunchul Shim

Authors
  1. Sangwoo Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eunmi Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Hyunchul Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Hyunchul Shim.

Additional information

This work was supported in part by the Korea National Research Foundation Grant 20110015377.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moon, S., Oh, E. & Shim, D.H. An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments. J Intell Robot Syst 70, 303–313 (2013). https://doi.org/10.1007/s10846-012-9740-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-012-9740-3

Keywords

Search

Navigation