Skip to main content

Overview on Task Allocation Methods for Cooperative Multi-target Attack

  • Conference paper
  • First Online:
Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 934))

  • 2036 Accesses

Abstract

The cooperative multi-target attack is a challenging mission for the military action in modern complex combat environment, and task allocation system will play a key role. Both modeling and solving are important for the task allocation problem, and they have been studied by more and more experts . This paper summarizes the task allocation methods for cooperative multi-target attack. Firstly, it introduces the development status of typical task allocation projects at home and abroad, and combs the development context of the system. The mission planning is divided into task allocation and path planning, and the task allocation modeling and solving algorithm of multi-UAV cooperative attack on multi-target are analyzed respectively, and the advantages and disadvantages of various task allocation methods are compared and summarized. Finally, the challenges in the field of task allocation are described. A comprehensive grasp of task allocation will help us to engage in innovative research in related fields.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 469.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 599.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 599.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdel-Basset, M., et al.: Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications. IEEE Trans. Industr. Inf. 17(7), 5068–5076 (2020)

    Article  Google Scholar 

  2. Elaziz, M.A., et al.: Enhanced marine predators algorithm for identifying static and dynamic photovoltaic models parameters. Energy Convers. Manage. 236, 113971 (2021)

    Article  Google Scholar 

  3. Nilforoushan, Z., Mohades, A., Rezaii, M.M., Laleh, A.: 3D hyperbolic Voronoi diagrams. Comput. Aided Des. 42(9), 759–767 (2010)

    Article  Google Scholar 

  4. Baumann, M., Leonard, S., Croft, E.A., Little, J.J.: Path planning for improved visibility using a probabilistic road map. IEEE Trans. Rob. 26(1), 195–200 (2013)

    Article  Google Scholar 

  5. Nguyet, T., Hoai, T.V., Nguyen, A.T.: Some advanced techniques in reducing time for path planning based on visibility graph. In: Proceedings: 3rd International Conference on Knowledge and Systems Engineering, pp. 190–194 (2010)

    Google Scholar 

  6. Wen, N., Zhao, L., Su, X., Ma, P.: UAV online path planning algorithm in a low altitude dangerous environment. IEEE/CAA J. Autom. Sin. 2(2), 173–185 (2015)

    Article  MathSciNet  Google Scholar 

  7. Hyondong, O., et al.: Coordinated standoff tracking using path shaping for multiple UAVs. IEEE Trans. Aerosp. Electron. Syst. 50(1), 348–363 (2014)

    Article  Google Scholar 

  8. Lemaire, T., Alami, R., Lacroix, S.: A distributed tasks allocation scheme in multi-UAV context. In: Proceedings: IEEE International Conference on Robotics and Automation, pp. 3622–3627, Toulouse, France (2008)

    Google Scholar 

  9. Besada-Portas, E., Torre, L., Moreno, A., Risco-Martín, J.L.: On the performance comparison of multi-objective evolutionary UAV path planners. Inf. Sci. 238, 111–125 (2013)

    Article  Google Scholar 

  10. Rabbath, C.A., Gognon, E., Lauzon, M.: On the cooperative control of multiple unmanned aerial vehicles. IEEE Canadian Rev. 46, 15–19 (2004)

    Google Scholar 

  11. Amato, P., Farina, M.: An alift-inspired evolutionary algorithm for dynamic multiobjective optimization problems. In: Soft Computing: Methodologies and Applications, pp. 113–125 (2005)

    Google Scholar 

  12. Coello, C.: Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. Mag. 1(1), 28–36 (2006)

    Article  Google Scholar 

  13. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the Strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  14. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  15. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  16. Li, J., Fu, J., Yang, Y., Wang, X., Rong, X.: Research on crowd-sensing task assignment based on fuzzy inference PSO algorithm. In: Tan, Y., Shi, Y., Tuba, M. (eds.) ICSI 2020. LNCS, vol. 12145, pp. 189–201. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53956-6_17

    Chapter  Google Scholar 

  17. O’Rourke, K.P., et al.: Dynamic routing of unmanned aerial vehicles using reactive tabu-search. Mil. Oper. Res. J. 6(5), 5–30 (2001)

    Article  Google Scholar 

  18. Nygard, K.E., Chandler, P.R., Pachter, M.: Dynamic network flow optimization models for air vehicle resource allocation. In: Proceedings: American Control Conference Arlington, VA, pp. 1853–1858 (2001)

    Google Scholar 

  19. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Proceedings: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2002)

    Google Scholar 

  20. Coello, C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)

    Article  Google Scholar 

  21. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  22. Zhang, B., et al.: Cooperative and geometric learning algorithm (CGLA) for path planning of UAVs with limited information. Automatica 50, 809–820 (2014)

    Article  MathSciNet  Google Scholar 

  23. Zhou, Z., Duan, H., Li, P., Di, B.: Chaotic differential evolution approach for 3D trajectory planning of unmanned aerial vehicle. In: Proceedings: 10th IEEE International Conference on Control & Automation, pp. 368–372. IEEE, Hangzhou, China (2013)

    Google Scholar 

  24. Zhang, X., Duan, H.: An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl. Soft Comput. 26, 270–284 (2015)

    Article  Google Scholar 

  25. Kuoa, R.J., et al.: Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm. Appl. Math. Comput. 266, 1013–1026 (2015)

    MathSciNet  Google Scholar 

  26. Li, J., Liu, K., Wang, H.: Task assignment optimization of multi-logistics robot based on improved auction algorithm. In: Zhang, J., Dresner, M., Zhang, R., Hua, G., Shang, X. (eds.) LISS2019, pp. 41–54. Springer, Singapore (2020)

    Chapter  Google Scholar 

  27. Jia, Y.: Research on UAV task assignment method based on parental genetic algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11655, pp. 439–446. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26369-0_41

    Chapter  Google Scholar 

  28. Salimi, H.: Stochastic fractal search: a powerful metaheuristic algorithm. Knowl. Based Syst. 75, 1–18 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 62101590), Natural Science Foundation of Shaanxi Province, China (Grant No. 2020JQ-481).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huan Zhou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, H., Zhang, X., Tang, A. (2023). Overview on Task Allocation Methods for Cooperative Multi-target Attack. In: Ren, Z., Wang, M., Hua, Y. (eds) Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Lecture Notes in Electrical Engineering, vol 934. Springer, Singapore. https://doi.org/10.1007/978-981-19-3998-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-3998-3_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3997-6

  • Online ISBN: 978-981-19-3998-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics