Skip to main content
SpringerLink
Log in

Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The study on multiple unmanned aerial vehicles (UAVs) reconnaissance task allocation problem is an important research field, which is significant for both military and civilian applications. This problem has often been considered as a multiple traveling salesman problem where the targets are considered as points. In this paper, we present a novel mathematical model that classifies heterogeneous targets as point targets, line targets and area targets to improve the fidelity of the model. It is a complex combinatorial optimization problem, for which we can hardly get an optimal solution as the scale of the problem expands. A new heuristic algorithm called grouping ant colony optimization algorithm is proposed for this new model. Compared with traditional ant colony algorithm, pheromone is divided into membership pheromone and sequence pheromone corresponding to grouping and permutation characteristics of the model, respectively. Also, negative feedback mechanism is introduced to accelerate convergence speed of the algorithm. The simulation results demonstrate that the new algorithm can consider comprehensively the performance of different UAVs and the characteristic of heterogeneous targets. It outperforms existing methods reported in the literature in terms of optimality of the result, and the advantage gets more obvious with the scale of reconnaissance task allocation problem expanding.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

All data used during the study are available from the corresponding author by request.

Code availability

The raw code cannot be shared at this time as the code also forms part of an ongoing study.

References

  • Albornoz C, Giraldo LF (2017) Trajectory design for efficient crop irrigation with a UAV. In: 2017 IEEE 3rd Colombian conference on automatic control (CCAC). IEEE, pp 1–6

  • Alighanbari M (2004) Task assignment algorithms for teams of UAVs in dynamic environments. Massachusetts Institute of Technology

  • Deb K (2014) Multi-objective optimization. In: Search methodologies. Springer, Boston, MA, pp 403–449

  • Deng Q, Yu J, Wang N (2013) Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes. Chin J Aeronaut 26(5):1238–1250

    Article  Google Scholar 

  • Dokeroglu T, Sevinc E, Cosar A (2019) Artificial bee colony optimization for the quadratic assignment problem. Appl Soft Comput 76:595–606

    Article  Google Scholar 

  • Fei S, Yan C, Lin-Cheng S (2008) UAV cooperative multi-task assignment based on ant colony algorithm. Acta Aeronaut Astronaut Sin 29:188–199

    Google Scholar 

  • Fu Z, Mao Y, He D et al (2019) Secure multi-UAV collaborative task allocation. IEEE Access 7:35579–35587

    Article  Google Scholar 

  • Hadi GS, Varianto R, Trilaksono B et al (2014) Autonomous UAV system development for payload dropping mission. J Instrum Autom Syst 1(2):72–22

    Google Scholar 

  • Hoffmann G, Huang H, Waslander S et al (2007) Quadrotor helicopter flight dynamics and control: theory and experiment. In: AIAA guidance, navigation and control conference and exhibit. pp 6461–6481

  • Jiang C, Wan Z, Peng Z (2020) A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Syst Appl 139:112867

    Article  Google Scholar 

  • Junjie P, Dingwei W (2006) An ant colony optimization algorithm for multiple travelling salesman problem. In: First international conference on innovative computing, information and control-volume I (ICICIC'06), vol 1. IEEE, pp 210–213

  • Kona H, Burde A, Zanwar DR (2015) A review of traveling salesman problem with time window constraint. IJIRST Int J Innov Res Sci Technol 2:253–256

    Google Scholar 

  • Luo C, Miao W, Ullah H et al (2019) Unmanned aerial vehicles for disaster management. Geological disaster monitoring based on sensor networks. Springer, Singapore, pp 83–107

    Chapter  Google Scholar 

  • Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  • Merino L, Caballero F, Martínez-de Dios JR et al (2006) A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. J Field Robot 23(3–4):165–184

    Article  Google Scholar 

  • Nygard KE, Chandler PR, Pachter M (2001) Dynamic network flow optimization models for air vehicle resource allocation. In: Proceedings of the 2001 American control conference (Cat. No. 01CH37148), vol 3. IEEE, pp 1853–1858

  • O’Rourke KP, Carlton WB, Bailey TG et al (2001) Dynamic routing of unmanned aerial vehicles using reactive tabu search. Mil Oper Res 6:5–30

    Article  Google Scholar 

  • Pandiri V, Singh A (2018) A hyper-heuristic based artificial bee colony algorithm for k-Interconnected multi-depot multi-traveling salesman problem. Inf Sci 463:261–281

    Article  Google Scholar 

  • Pendharkar PC (2015) An ant colony optimization heuristic for constrained task allocation problem. J Comput Sci 7:37–47

    Article  MathSciNet  Google Scholar 

  • Ramirez-Atencia C, Bello-Orgaz G, R-Moreno MD et al (2017) Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput 21(17):4883–4900

    Article  Google Scholar 

  • Somhom S, Modares A, Enkawa T (1999) Competition-based neural network for the multiple travelling salesmen problem with minmax objective. Comput Oper Res 26(4):395–407

    Article  MathSciNet  Google Scholar 

  • Srikanth GU, Geetha R (2018) Task scheduling using ant colony optimization in multicore architectures: a survey. Soft Comput 22(15):5179–5196

    Article  Google Scholar 

  • Vincent T, Seipp F, Ruzika S et al (2013) Multiple objective branch and bound for mixed 0–1 linear programming: corrections and improvements for the biobjective case. Comput Oper Res 40(1):498–509

    Article  MathSciNet  Google Scholar 

  • Wang J, Jiang C, Han Z et al (2017) Taking drones to the next level: cooperative distributed unmanned-aerial-vehicular networks for small and mini drones. IEEE Veh Technol Mag 12(3):73–82

    Article  Google Scholar 

  • Wang J, Jiang C, Zhang H et al (2020) Thirty years of machine learning: the road to pareto-optimal wireless networks. IEEE Commun Surv Tutor 22:1472–1514

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yang T, Jiang Z, Sun R et al (2020) Maritime search and rescue based on group mobile computing for UAVs and USVs. In: IEEE transactions on industrial informatics. pp 1

  • Yousefikhoshbakht M, Didehvar F, Rahmati F (2013) Modification of the ant colony optimization for solving the multiple traveling salesman problem. Rom J Inf Sci Technol 16(1):65–80

    Google Scholar 

  • Yuan S, Skinner B, Huang S et al (2013) A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. Eur J Oper Res 228(1):72–82

    Article  MathSciNet  Google Scholar 

  • Zhang W, Hu Y, He H et al (2019) Linear and dynamic programming algorithms for real-time task scheduling with task duplication. J Supercomput 75(2):494–509

    Article  Google Scholar 

  • Zhu W, Li LIU, Teng L et al (2018) Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding. Chin J Aeronaut 31(2):339–350

    Article  Google Scholar 

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Department of Aeronautics and Astronautics, Fudan University, Shanghai, 200433, China

    Sheng Gao, Jiazheng Wu & Jianliang Ai

Authors
  1. Sheng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiazheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianliang Ai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design, and all authors read and approved the final manuscript.

Corresponding author

Correspondence to Sheng Gao.

Ethics declarations

Conflict of interest

The authors, Sheng GAO, Jiazheng WU, and Jianliang AI, declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, S., Wu, J. & Ai, J. Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm. Soft Comput 25, 7155–7167 (2021). https://doi.org/10.1007/s00500-021-05675-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05675-8

Keywords

Search

Navigation