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This paper investigates the task planning methods for multiple UAVs executing multiple tasks. Initially, a clustering algorithm is used to cluster the task targets, transforming the UAV cooperative task planning problem into a Traveling Salesman Problem (TSP). Subsequently, considering the cooperative characteristics of UAVs and addressing issues such as the slow convergence and susceptibility to local optima of traditional genetic algorithms, improvements are made to the encoding and mutation steps of the genetic algorithm. Finally, numerical simulation experiments validate the effectiveness of the proposed task planning method and demonstrate the superiority of the improved genetic algorithm in achieving faster convergence.

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A Multi-UAV Mission Planning Method Based on Improved Genetic Algorithm and Traveling Salesman Model

  • 会议论文

  • 发表日期：

    2025年

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  • Yunfei Wang,
  • Wei Yang,
  • Hongzhong Ma,
  • Xiang Song,
  • Peng Yang

摘要

This paper investigates the task planning methods for multiple UAVs executing multiple tasks. Initially, a clustering algorithm is used to cluster the task targets, transforming the UAV cooperative task planning problem into a Traveling Salesman Problem (TSP). Subsequently, considering the cooperative characteristics of UAVs and addressing issues such as the slow convergence and susceptibility to local optima of traditional genetic algorithms, improvements are made to the encoding and mutation steps of the genetic algorithm. Finally, numerical simulation experiments validate the effectiveness of the proposed task planning method and demonstrate the superiority of the improved genetic algorithm in achieving faster convergence.