A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm

Quansheng Sun, Tianjun Liao, Haibo Du, Yinfeng Zhao, Chih Chiang Chen

研究成果: Article同行評審


The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging.

出版狀態Published - 2023 1月

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 資訊系統
  • 儀器
  • 原子與分子物理與光學
  • 電氣與電子工程
  • 生物化學


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