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With the advancement of technology, unmanned aerial vehicles (UAVs) are increasingly widely used in both military and civilian fields. Especially in the military field, the mode of UAVs performing tasks is constantly evolving in response to complex battlefield environments. This paper proposes an innovative mission planning method in view of the deficiencies existing in the traditional multi-UAV reconnaissance methods. This method fully considers the detection radius of the photoelectric payload and significantly improves the search efficiency and coverage quality through the optimization algorithm. This paper first proposes the minimum coverage circle algorithm to calculate the minimum coverage circle for the target point, and then takes the center of the minimum coverage circle as the reconnaissance point. And the ant colony algorithm is adopted to determine the optimal approach point sequence of the photoelectric payload. In the multi-UAV mission planning stage, tasks are allocated through the integer programming algorithm. Then, the ant colony algorithm is utilized in the context of a single UAV to determine the optimal landing point sequence of photoelectric payload. The research is based on the set detection radius of the photoelectric payload (150). Firstly, the minimum coverage circle algorithm is used for calculation. All the target points are included in the minimum coverage circle to ensure that all the target points can be effectively covered. Then, the center of the smallest covering circle is taken as the track point. Task planning was carried out using integer programming and ant colony algorithm, and the above-mentioned task planning methods were also calculated. The traditional methods and research methods for single and multiple unmanned aerial vehicles were respectively simulated and verified. The results show that in the case of 500 iterations of the ant colony algorithm, the new research method significantly shortens the total length of the path compared with the traditional method based on the target point. For example, the total path length of a single UAV decreased from 7510.1 to 7155.7, and the total path length of multi-UAV decreased from 11,613.4 to 8139.0. These results fully demonstrate the efficiency and superiority of the new method in multi-UAV mission planning, and show its great potential in practical applications.

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Research on UAV Mission Planning with Optimized Coverage of Photoelectric Payloads

  • 会议论文

  • 发表日期：

    2026年

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  • Jingzhi Bi,
  • Wei Huang,
  • Zhankui Qiu,
  • Chengzi Xia,
  • Shuo Zhang

摘要

With the advancement of technology, unmanned aerial vehicles (UAVs) are increasingly widely used in both military and civilian fields. Especially in the military field, the mode of UAVs performing tasks is constantly evolving in response to complex battlefield environments. This paper proposes an innovative mission planning method in view of the deficiencies existing in the traditional multi-UAV reconnaissance methods. This method fully considers the detection radius of the photoelectric payload and significantly improves the search efficiency and coverage quality through the optimization algorithm. This paper first proposes the minimum coverage circle algorithm to calculate the minimum coverage circle for the target point, and then takes the center of the minimum coverage circle as the reconnaissance point. And the ant colony algorithm is adopted to determine the optimal approach point sequence of the photoelectric payload. In the multi-UAV mission planning stage, tasks are allocated through the integer programming algorithm. Then, the ant colony algorithm is utilized in the context of a single UAV to determine the optimal landing point sequence of photoelectric payload. The research is based on the set detection radius of the photoelectric payload (150). Firstly, the minimum coverage circle algorithm is used for calculation. All the target points are included in the minimum coverage circle to ensure that all the target points can be effectively covered. Then, the center of the smallest covering circle is taken as the track point. Task planning was carried out using integer programming and ant colony algorithm, and the above-mentioned task planning methods were also calculated. The traditional methods and research methods for single and multiple unmanned aerial vehicles were respectively simulated and verified. The results show that in the case of 500 iterations of the ant colony algorithm, the new research method significantly shortens the total length of the path compared with the traditional method based on the target point. For example, the total path length of a single UAV decreased from 7510.1 to 7155.7, and the total path length of multi-UAV decreased from 11,613.4 to 8139.0. These results fully demonstrate the efficiency and superiority of the new method in multi-UAV mission planning, and show its great potential in practical applications.