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严炜,龙长江,李善军.基于差分量子退火算法的农用无人机路径规划方法[J].华中农业大学学报,2020,39(1):180-186
基于差分量子退火算法的农用无人机路径规划方法
A path planning method for agricultural UAV based on DEQA algorithm
投稿时间:2019-05-13  
DOI:
中文关键词:  农用无人机; 植保无人机; 路径规划; 差分量子退火算法; 植保机械; 飞行距离  覆盖率; 障碍物
英文关键词:agricultural UAV  plant protection UAV  path planning  DEQA algorithm  plant protection machinery  flight distance  coverage rate  obstacle
基金项目:国家重点研发计划项目(2017YFD0202001)
作者单位E-mail
严炜 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室武汉 430070 1367770761@qq.com 
龙长江 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室武汉 430070 lcjflow@163.com 
李善军 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室武汉 430070  
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中文摘要:
      为解决不规则区域内农用无人机植保作业问题,以农用无人机的总飞行距离和多余覆盖率为指标建立模型,将无人机的植保作业航向角作为优化目标,并考虑有障碍物下的情形,采用差分进化算法(different evolution algorithm,DE)与量子退火算法(quantum annealing algorithm,QA)融合的方法对应用模型进行求解,分析算法的执行过程并进行MATLAB仿真试验。结果显示:在设定的不含障碍物农田区域环境下,相较于未规划与差分进化算法规划情况,采用差分量子退火算法(differential evolution algorithm-quantum annealing,DEQA)时无人机总的飞行距离分别减少101.52、73.00 m,转弯路径分别减少43.02、43.10 m,多余覆盖率分别减少22.25%和12.79%;在设定的含障碍物农田区域环境下,相较于未规划与差分进化算法规划情况,采用差分量子退火算法时无人机总的飞行距离分别减少73.24、24.54 m,转弯路径分别减少52.50、12.72 m,多余覆盖率分别减少72.34%、23.52%,其余指标均有所下降。仿真结果表明,采用差分量子退火算法能够完成农田区域路径规划问题,可为农用无人机路径规划提供技术支持。
英文摘要:
      A model was established based on the total flight distance and redundant coverage of agricultural UAV to solve the problems of agricultural UAV’s plant protection operation in irregular area. Taking the heading angle of UAV’s plant protection operation as the optimization objective and considering the situation of obstacles,the application model was solved by the method of fusion of differential evolution algorithm and quantum annealing algorithm. The implementation of the algorithm was analyzed and MATLAB simulation test was conducted. The results showed that the total flight distance was reduced by 101.52 m and 73.00 m. The turning path was reduced by 43.02 m and 43.10 m. The redundant coverage was reduced by 22.35% and 12.79%,compared with that of the unplanned and differential evolution algorithm in the set farmland environment without obstacles according to the planning. The total flight distance was reduced by 73.24 m and 24.54 m. The turning path was reduced by 52.50 m and 12.72 m. The redundant coverage was reduced by 72.34% and 23.52%,and other indicators was reduced as well. The simulation results showed that DEQA algorithm can plan the path of farmland area. It will provide technical support for planning the path of agricultural UAV.
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