引用本文:薛裕颖,张祥银,张国梁,贾松敏.基于量子行为烟花算法的移动机器人路径规划及平滑[J].控制理论与应用,2019,36(9):1398~1408.[点击复制]
XUE Yu-ying,ZHANG Xiang-yin,ZHANG Guo-liang,JIA Song-min.Path planning and smoothing based on quantum-behaved fireworks algorithm for mobile robot[J].Control Theory and Technology,2019,36(9):1398~1408.[点击复制]
基于量子行为烟花算法的移动机器人路径规划及平滑
Path planning and smoothing based on quantum-behaved fireworks algorithm for mobile robot
摘要点击 2915  全文点击 1170  投稿时间：2018-06-26  修订日期：2019-03-04
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/CTA.2018.80473
  2019,36(9):1398-1408
中文关键词  路径规划  量子行为烟花算法  人工势场法  均值滤波  路径平滑
英文关键词  Path planning  Quantum-behaved fireworks algorithm  Artificial potentialSfield  Average filter  Path smoothing
基金项目  国家自然科学基金(61703012,81471770)；北京市自然科学基金(4182010)
作者单位邮编
薛裕颖 北京工业大学信息学部 100124
张祥银* 北京工业大学信息学部 100124
张国梁 北京工业大学信息学部 
贾松敏 北京工业大学信息学部 
中文摘要
      针对移动机器人全局路径规划问题，提出一种基于量子行为烟花算法(Quantum-behaved fireworks algorithm, QFWA)的路径规划方法。改进算法在基本烟花算法(Fireworks algorithm, FWA)的基础上增加了基于量子行为的烟花爆炸策略。该策略使得种群在接近全局最优时具有较强的局部搜索能力，同时在种群远离全局最优位置时具有较强的全局搜索能力。改进算法提高了烟花爆炸产生火花的多样性和算法的收敛速度。在Benchmark测试函数上将改进算法与其他几种优化算法进行了对比，结果表明改进算法的性能优于其他算法。将QFWA应用于求解移动机器人路径规划问题，并采用均值滤波结合人工势场法对规划出的路径进行平滑处理。仿真实验结果表明改进方法在移动机器人路径规划问题上的可行性和有效性。
英文摘要
      In view of the global path planning problem of mobile robot, a path planning method based on quantum-behaved fireworks algorithm (QFWA) is proposed. A quantum-behavior fireworks explosion method is added to the fireworks algorithm (FWA). The method has strong local search ability when the fitness value is close to the global optimal fitness value, and has strong global search capability when the fitness value is relatively bigger. The algorithm has improved the diversity of fireworks explosion and the algorithm convergence speed. Test the improved algorithm with Benchmark test function, and contrast the result with other algorithm, the results shows that the improved algorithm has the best optimization effect. The QFWA algorithm is applied to the path planning of mobile robot, and using a method of the artificial potential field algorithm based on the average filter to smooth the planned path. The effectiveness and feasibility of the improved algorithm is verified by simulation results.