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Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm

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Abstract

Describing cooperative reconnaissance is crucial for air traffic relating to multiple unmanned aerial vehicles (UAVs) loaded in different bases in an increasingly complex battlefield environment. Compared with the traditional problem that all UAVs took off from just one base, this paper is to address reconnaissance missions, which must be done in partnership among multiple UAVs in different bases. To improve missions’ reliability, residence time in effective detection of enemy radars should be mitigated under the premise of missions completed by UAVs. This paper transforms the minimum residence time into the shortest path combinatorial optimization, and discretizes heading angles. Graph theory is applied to analyze path problems and a global model with numerous constraint conditions can be built. Finally, a valuable reconnaissance path planning can be generated through solving the model with genetic algorithm. Also an application example that eight UAVs in four bases finish reconnaissance missions involving sixty-eight targets is established, and then an optimal solution is got to explain both the feasibility and efficiency of the proposed modularization and algorithm.

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Acknowledgements

The paper was supported by Key Problem Tackling Project of Shaanxi Scientific and Technological Office (2016GY-024), and National Natural Science Foundation of China (Grant No. 51705392), Xi’an Technological University President Foundation (Grant No. XAGDXJJ16004).

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Authors and Affiliations

  1. School of Mechatronic Engineering, Xi’an Technological University, Xi’an, 710021, China

    Yan Cao, Wanyu Wei, Yu Bai & Hu Qiao

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  1. Yan Cao
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  2. Wanyu Wei
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  3. Yu Bai
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  4. Hu Qiao
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Correspondence to Wanyu Wei.

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Cao, Y., Wei, W., Bai, Y. et al. Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm. Cluster Comput 22 (Suppl 3), 5175–5184 (2019). https://doi.org/10.1007/s10586-017-1132-9

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  • DOI: https://doi.org/10.1007/s10586-017-1132-9

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