基于改进人工势场-遗传算法的路径规划算法研究
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摘要
针对传统人工势场法解决移动机器人路径规划路问题时存在目标不可到达问题(GNRON)和局部最优的缺陷,提出一种建立在改进的人工势场模型之上结合遗传算法的并行搜索方法来寻求全局最优解的方法。通过引入填平势场使得势场函数能够跳出局部极小点,再将遗传算法和人工势场法两种方法相结合,利用人工势场法来优化采用遗传算法所得到的全局路径。仿真研究证明了所提出的改进算法的有效性,改进后算法能够在复杂的静态和动态环境中实现避障并找到最佳或接近最佳的移动机器人路径。
When the traditional artificial potential field method solves the path problem of mobile robot path planning,there are target unreachable problems(GNRON)and local optimal defects.This paper proposes a parallel search method to find a global optimal solution based on the improved artificial potential field model combined with the genetic algorithm method.By introducing the filling potential field,the potential field function can jump out of the local minimum point,and then use the artificial potential field method,combining the genetic algorithm and the artificial potential field method,to optimize the global path obtained by the genetic algorithm.Simulation studies demonstrate the effectiveness of the proposed improved algorithm.The improved algorithm can achieve obstacle avoidance and find the best or near-optimal mobile robot path in complex static and dynamic environments.
When the traditional artificial potential field method solves the path problem of mobile robot path planning,there are target unreachable problems(GNRON)and local optimal defects.This paper proposes a parallel search method to find a global optimal solution based on the improved artificial potential field model combined with the genetic algorithm method.By introducing the filling potential field,the potential field function can jump out of the local minimum point,and then use the artificial potential field method,combining the genetic algorithm and the artificial potential field method,to optimize the global path obtained by the genetic algorithm.Simulation studies demonstrate the effectiveness of the proposed improved algorithm.The improved algorithm can achieve obstacle avoidance and find the best or near-optimal mobile robot path in complex static and dynamic environments.
引文
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[8]席裕庚,柴天佑,恽为民.遗传算法综述[J].控制理论与应用,1996(6):697-708.
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[11]韩伟,孙凯彪.基于模糊人工势场法的智能全向车路径规划[J].计算机工程与应用,2018,54(6):105-109.
[12]邓学强.基于改进人工势场法的移动机器人路径规划[J].山东理工大学学报(自然科学版),2014(1):38-41.
[13]郜辉,吕志刚.人工势场法目标不可达的研究[J].国外电子测量技术,2018,37(1):29-33.
[14]朱毅,张涛,宋靖雁.未知环境下势场法路径规划的局部极小问题研究[J].自动化学报,2010,36(8):1122-1130.
[15]郭枭鹏.基于改进人工势场法的路径规划算法研究[D].哈尔滨:哈尔滨工业大学,2017.
[16]WU X T,SUN Z Q,Using genetic algorithm for path planning[J].Journal of Tsinghua University(Sci&Tech),1995(35):14-19.
[2]徐兆辉.移动机器人路径规划技术的现状与发展[J].科技创新与应用,2016(3):43-50.
[3]胡洋洋.移动机器人楼层内定位与导航研究[D].南京:南京理工大学,2017.
[4]ZHANG Q S,CHEN D D,CHEN T.An obstacle avoidance method of soccer robot based on evolutionary artificial potential field[J].Energy Procedia,2012(16):1792-1798.
[5]王钦钊,程金勇,李小龙.复杂环境下机器人路径规划方法研究[J].计算机仿真,2017,34(10):296-300.
[6]周郭许,唐西林.基于栅格模型的机器人路径规划快速算法[J].计算机工程与应用,2006,42(21):197-199.
[7]裴振兵,陈雪波.改进蚁群算法及其在机器人避障中的应用[J].智能系统学报,2015,10(1):90-96.
[8]席裕庚,柴天佑,恽为民.遗传算法综述[J].控制理论与应用,1996(6):697-708.
[9]KHATIB O.Real-time obstacle avoidance for manipulators and mobile robots[J].The International Journal of Robotics Research,1986,5(1):90-99.
[10]于振中,闫继宏,赵杰,等.改进人工势场法的移动机器人路径规划[J].哈尔滨工业大学学报,2011,43(1):50-55.
[11]韩伟,孙凯彪.基于模糊人工势场法的智能全向车路径规划[J].计算机工程与应用,2018,54(6):105-109.
[12]邓学强.基于改进人工势场法的移动机器人路径规划[J].山东理工大学学报(自然科学版),2014(1):38-41.
[13]郜辉,吕志刚.人工势场法目标不可达的研究[J].国外电子测量技术,2018,37(1):29-33.
[14]朱毅,张涛,宋靖雁.未知环境下势场法路径规划的局部极小问题研究[J].自动化学报,2010,36(8):1122-1130.
[15]郭枭鹏.基于改进人工势场法的路径规划算法研究[D].哈尔滨:哈尔滨工业大学,2017.
[16]WU X T,SUN Z Q,Using genetic algorithm for path planning[J].Journal of Tsinghua University(Sci&Tech),1995(35):14-19.