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YIN Yanan,ZHEN Ran,WU Xiaojing,ZHANG Chunyue,WU Xueli.Research on UAV route planning based on adaptive multi heuristic ant colony algorithm[J].Journal of Hebei University of Science and Technology,2021,42(1):38-47
自適應多啟發蟻群算法的無人機路徑規劃
Research on UAV route planning based on adaptive multi heuristic ant colony algorithm
Received:September 06, 2020  Revised:November 06, 2020
DOI:10.7535/hbkd.2021yx01006
中文關鍵詞:  航空、航天科學技術基礎學科其他學科  無人機  蟻群算法  路徑規劃  啟發因素
英文關鍵詞:basic science and technology of aeronautics and astronautics other disciplines  UAV  ant colony algorithm  route planning  heuristic factors
基金項目:國防基礎計劃項目; 河北省重點研發計劃項目(19250801D); 河北省研究生創新資助項目(CXZZSS2020098); 河北省軍民融合發展研究課題(HB19JMRH009)
Author NameAffiliationE-mail
YIN Yanan School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang  
ZHEN Ran School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang  
WU Xiaojing School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang  
ZHANG Chunyue School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang  
WU Xueli School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang;Hebei Engineering Technology Research Center of Production Process Automation, Shijiazhuang xlwu0311@163.com 
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中文摘要:
      為了解決蟻群算法在無人機實現路徑規劃中容易陷入局部最優的問題,提出改進的蟻群算法。對信息素的揮發因子以及信息素進行上、下限設置,防止由于較短路徑上的信息素過高以及較長路徑上的信息素過低,使螞蟻陷入局部最優,同時在多啟發因素的影響下,將路徑的整體長度作為決定狀態轉移概率的一個自適應啟發函數因子,當路徑長度很大時,自適應啟發函數因子較小,使得蟻群選擇該路徑的概率減小。實驗結果表明,改進的算法在路徑長度上減少了6.4%,最優路徑長度方差降低了85.78%,增加了對環境整體性的考慮,縮短了路徑長度,降低了迭代次數,跳出局部最優。在環境復雜度加大的情況下,引入自適應啟發函數因子之后的算法可以有效地選擇較好的路徑,為無人機路徑規劃提供了理論依據。
英文摘要:
      In order to solve the problem that ant colony algorithm is easy to fall into local optimum in UAV route planning, an improved ant colony algorithm was proposed. The upper and lower limits of pheromone volatilization factor and pheromone were set to prevent ants from falling into local optimum because pheromone on short path was too high or pheromone on long path was too low. At the same time, under the influence of multiple heuristic factors, the overall length of the path was taken as an adaptive heuristic function factor to determine the state transition probability. When the path length was large, the adaptive heuristic function factor was small, which reduced the probability of choosing the path by the ant colony. The experimental results show that the improved algorithm reduces the path length by 6.4% and the variance of the optimal path length by 85.78%, which increases the consideration of environmental integrity, shortens the path length, reduces the number of iterations, and jumps out of the local optimum. In the case of increasing environmental complexity, the algorithm can effectively choose a better path and provide a theoretical basis for UAV route planning after introducing the adaptive heuristic function factor.
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