基于改进混合蛙跳算法的移动机器人路径规划

doi:10.11772/j.issn.1001-9081.2014.10.2850

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2850-2853.DOI: 10.11772/j.issn.1001-9081.2014.10.2850

基于改进混合蛙跳算法的移动机器人路径规划

潘桂彬,潘丰,刘国栋

江南大学物联网工程学院，江苏无锡 214122

收稿日期:2014-04-28 修回日期:2014-06-20 出版日期:2014-10-01 发布日期:2014-10-30
通讯作者: 潘桂彬
作者简介:潘桂彬(1990-)，男，江苏泰州人，硕士研究生，主要研究方向：机器人路径规划、智能控制；潘丰(1963-)，男，江苏苏州人，教授，博士，主要研究方向：工业优化控制；刘国栋(1950-)，男，辽宁沈阳人，教授，博士，主要研究方向：人工智能、机器人系统。
基金资助:
国家自然科学基金资助项目

Path planning for mobile robots based on improved shuffled frog leaping algorithm

PAN Guibing,PANFeng ,LIU Guodong

School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2014-04-28 Revised:2014-06-20 Online:2014-10-01 Published:2014-10-30
Contact: PAN Guibing

摘要/Abstract

摘要：

针对混合蛙跳算法(SFLA)进行路径规划时易陷入局部最优且寻优效果较差的问题，提出一种改进的SFLA。改进算法在原算法的更新策略中引入欧氏距离和种群最优蛙，并提出一种带可调控制参数的产生新个体的方法代替原本的随机更新操作。把路径规划问题转换为最小化问题，基于环境中目标和障碍物的位置定义青蛙的适应度，机器人依次到达每次迭代中最好蛙的位置，从而实现最优路径规划。移动机器人仿真实验中，与其他算法相比，改进后的算法成功次数由82提高到98，规划时间由9.7s减少到5.3s。实验结果表明，改进算法具有较强的安全性和寻优性能。

Abstract:

To solve the problems that the Shuffled Frog Leaping Algorithm (SFLA) path planning is easy to fall into local optima and optimization effect is poor, an improved SFLA was proposed. Euclidean distance and the best frog of the population were added into the original algorithms update strategy, and a method to generate a new individual with a adjustable control parameter instead of the original random update operation was proposed. This paper firstly transformed the problem of robot path planning into a minimization one, and then defined the fitness of a frog based on the position of target and obstacles in the environment. The robot selected and reached the position of the globally best frog in each iteration successively to realize the optimal path planning. In the mobile robots simulation experiment, compared with other algorithms, the successes number of the improved algorithm was increased from 82 to 98, and the path planning time was decreased from 9.7s to 5.3s. The experimental results show that the improved algorithm has stronger security and better optimization performance.

中图分类号:

TP242.6

潘桂彬潘丰刘国栋. 基于改进混合蛙跳算法的移动机器人路径规划[J]. 计算机应用, 2014, 34(10): 2850-2853.

PAN Guibing PANFeng LIU Guodong. Path planning for mobile robots based on improved shuffled frog leaping algorithm[J]. Journal of Computer Applications, 2014, 34(10): 2850-2853.

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基于改进混合蛙跳算法的移动机器人路径规划

Path planning for mobile robots based on improved shuffled frog leaping algorithm

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