An improved ant colony algorithm for robot path planning

被引：404

作者：

Liu, Jianhua ^{[1
,2
]}

Yang, Jianguo ^{[1
]}

Liu, Huaping ^{[3
]}

Tian, Xingjun ^{[2
]}

Gao, Meng ^{[2
]}

机构：

[1] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China

[2] Shijiazhuang Tiedao Univ, Coll Elect & Elect Engn, Shijiazhuang 050043, Hebei, Peoples R China

[3] Tsinghua Univ, Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

来源：

SOFT COMPUTING | 2017年 / 21卷 / 19期

关键词：

Mobile robot; Ant colony algorithm; Pheromone diffusion; Local path optimization; GENETIC ALGORITHMS; OPTIMIZATION; NETWORK; SYSTEM;

D O I：

10.1007/s00500-016-2161-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.

引用

页码：5829 / 5839

页数：11

共 29 条

[11] Incremental Support Vector Learning for Ordinal Regression [J].

Gu, Bin ;

Sheng, Victor S. ;

Tay, Keng Yeow ;

Romano, Walter ;

Li, Shuo .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (07) :1403-1416

[12] 基于改进蚁群算法的搜索机器人路径规划 [J].

康冰 ;

王曦辉 ;

刘富 .

吉林大学学报(工学版) , 2014, (04) :1062-1068

[13] REAL-TIME OBSTACLE AVOIDANCE FOR MANIPULATORS AND MOBILE ROBOTS [J].

KHATIB, O .

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 1986, 5 (01) :90-98

[14] Hybrid ant colony algorithms for path planning in sparse graphs [J].

Lim, Kwee Kim ;

Ong, Yew-Soon ;

Lim, Meng Hiot ;

Chen, Xianshun ;

Agarwal, Amit .

SOFT COMPUTING, 2008, 12 (10) :981-994

[15]

Liu Zhao-hua, 2010, Control and Decision, V25, P695

[16] Ant colony optimization with potential field heuristic for robot path planning [J].

Luo D.-L. ;

Wu S.-X. .

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2010, 32 (06) :1277-1280

[17] Social Network and Tag Sources Based Augmenting Collaborative Recommender System [J].

Ma, Tinghuai ;

Zhou, Jinjuan ;

Tang, Meili ;

Tian, Yuan ;

Al-Dhelaan, Abdullah ;

Al-Rodhaan, Mznah ;

Lee, Sungyoung .

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2015, E98D (04) :902-910

[18] A memetic ant colony optimization algorithm for the dynamic travelling salesman problem [J].

Mavrovouniotis, Michalis ;

Yang, Shengxiang .

SOFT COMPUTING, 2011, 15 (07) :1405-1425

[19] A computational ecosystem for optimization: review and perspectives for future research [J].

Parpinelli, Rafael Stubs ;

Lopes, Heitor Silverio .

MEMETIC COMPUTING, 2015, 7 (01) :29-41

[20] Image fusion by combining multiwavelet with nonsubsampled direction filter bank [J].

Peng, Geng ;

Wang, Zhengyou ;

Liu, Shuaiqi ;

Zhuang, Shanna .

SOFT COMPUTING, 2017, 21 (08) :1977-1989

← 1 2 3 →