[1]徐玉琼,娄柯,李志锟.基于变步长蚁群算法的移动机器人路径规划[J].智能系统学报,2021,16(2):330-337.[doi:10.11992/tis.202004011]
 XU Yuqiong,LOU Ke,LI Zhikun.Mobile robot path planning based on variable-step ant colony algorithm[J].CAAI Transactions on Intelligent Systems,2021,16(2):330-337.[doi:10.11992/tis.202004011]
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基于变步长蚁群算法的移动机器人路径规划

参考文献/References:
[1] CONFESSORE G, FABIANO M, LIOTTA G. A network flow based heuristic approach for optimising AGV movements[J]. Journal of intelligent manufacturing, 2013, 24(2):405-419.
[2] 张毅, 权浩, 文家富. 基于独狼蚁群混合算法的移动机器人路径规划[J]. 华中科技大学学报(自然科学版), 2020, 48(1):127-132
ZHANG Yi, QUAN Hai, WEN Jiafu. Mobile robot path planning based on the wolf ant colony hybrid algorithm[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2020, 48(1):127-132
[3] 刘可, 李可, 宿磊, 等. 基于蚁群算法与参数迁移的机器人三维路径规划方法[J]. 农业机械学报, 2020, 51(1):29-36
LIU Ke, LI Ke, SU Lei, et al. Robot 3D path planning method based on ant colony algorithm and parameter transfer[J]. Transactions of the Chinese society for agricultural machinery, 2020, 51(1):29-36
[4] ZHOU Zhiping, NIE Yunfeng, GAO Min. Enhanced ant colony optimization algorithm for global path planning of mobile robots[C]//Proceedings of 2013 International Conference on Computational and Information Sciences. Shiyang, China, 2013:698-701.
[5] HE Qiang, HU Xiangtao, REN Hong, et al. A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem[J]. ISA transactions, 2015, 59:105-113.
[6] WU Tao, JING Xiaojun. Exploration of multiple access interference suppression based on multi-user detection[J]. Chinese journal of electronics, 2019, 28(4):835-840.
[7] 陈军章. 改进人工鱼群算法的机器人路径规划及跟踪[J]. 机械设计与制造, 2019(04):251-255
CHEN Junzhang. Mobile Robot Path Planning and Tracking Based on Improved Artificial Fish Swarm Algorithm[J]. Machinery Design & Manufacture, 2019(04):251-255
[8] 许凯波, 鲁海燕, 黄洋, 等. 基于双层蚁群算法和动态环境的机器人路径规划方法[J]. 电子学报, 2019, 47(10):2166-2176
XU Kaibo, LU Haiyan, HUANG Yang, et al. Robot path planning based on double-layer ant colony optimization algorithm and dynamic environment[J]. Acta electronica sinica, 2019, 47(10):2166-2176
[9] 朱艳, 游晓明, 刘升. 基于启发式机制的改进蚁群算法[J]. 信息与控制, 2019, 48(3):265-271
ZHU Yan, YOU Xiaoming, LIU Sheng. Improved ant colony algorithm based on heuristic mechanism[J]. Information and control, 2019, 48(3):265-271
[10] LIU Jianhua, YANG Jianguo, LIU Huaping, et al. An improved ant colony algorithm for robot path planning[J]. Soft computing, 2017, 21(19):5829-5839.
[11] HWU T, WANG A Y, OROS N, et al. Adaptive robot path planning using a spiking neuron algorithm with axonal delays[J]. IEEE transactions on cognitive and developmental systems, 2018, 10(2):126-137.
[12] 封声飞, 雷琦, 吴文烈, 等. 自适应蚁群算法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55(17):35-43
FENG Shengfei, LEI Qi, WU Wenlie, et al. Mobile robot path planning based on adaptive ant colony algorithm[J]. Computer Engineering and Applications, 2019, 55(17):35-43
[13] 黄琰, 李岩, 俞建成, 等. AUV智能化现状与发展趋势[J]. 机器人, 2020, 42(2):215-231
HUANG Yan, LI Yan, YU Jiancheng, et al. State-of-the-art and development trends of AUV intelligence[J]. Robot, 2020, 42(2):215-231
[14] 江明, 王飞, 葛愿, 等. 基于改进蚁群算法的移动机器人路径规划研究[J]. 仪器仪表学报, 2019, 40(2):113-121
JIANG Ming, WANG Fei, GE Yuan, et al. Research on path planning of mobile robot based on improved ant colony algorithm[J]. Chinese journal of scientific instrument, 2019, 40(2):113-121
[15] CAO Jingang. Robot global path planning based on an improved ant colony algorithm[J]. Journal of computer and communications, 2016, 4(2):11-19.
[16] 徐玉琼, 娄柯, 李婷婷, 等. 改进自适应蚁群算法的移动机器人路径规划[J]. 电子测量与仪器学报, 2019, 33(10):89-95
XU Yuqiong, LOU Ke, LI Tingting, et al. Path planning of mobile robot based on improved adaptive ant colony algorithm[J]. Journal of electronic measurement and instrumentation, 2019, 33(10):89-95
[17] AJEIL F H, IBRAHEEM I K, AZAR A T, et al. Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments[J]. Sensors, 2020, 20(7):1880.
[18] 杨俊成, 李淑霞, 蔡增玉. 路径规划算法的研究与发展[J]. 控制工程, 2017, 24(7):1473-1480
YANG Juncheng, LI Shuxia, CAI Zengyu. Research and development of path planning algorithm[J]. Control engineering of China, 2017, 24(7):1473-1480
[19] 王培良, 张婷, 肖英杰. 蚁群元胞优化算法在人群疏散路径规划中的应用[J]. 物理学报, 2020, 69(8):234-242
WANG Peiliang, ZHANG Ting, XIAO Yingjie. Application research of ant colony cellular optimization algorithm in population evacuation path planning[J]. Acta physica sinica, 2020, 69(8):234-242
[20] 万方, 周风余, 尹磊, 等. 基于电势场法的移动机器人全局路径规划算法[J]. 机器人, 2019, 41(6):742-750
WAN Fang, ZHOU Fengyu, YIN Lei, et al. Global path planning algorithm of mobile robot based on electric potential field[J]. Robot, 2019, 41(6):742-750

备注/Memo

收稿日期:2020-04-10。
基金项目:国家自然科学基金项目(61572032);安徽省高校自然科学研究重点项目(KJ2019A0151,KJ2019A0150);2018年度皖江高端装备制造协同创新中心开放基金项目(GCKJ2018009)
作者简介:徐玉琼,硕士研究生,主要研究方向为移动机器人路径规划技术、图像处理;娄柯,副教授,博士,主要研究方向为多智能体协同控制、嵌入式系统及应用。主持及参与国家、省部级科学基金项目10余项。发表学术论文20余篇;李志锟,硕士研究生,主要研究方向为移动机器人路径规划技术、移动机器人地图构建技术、智能算法
通讯作者:徐玉琼.E-mail:xuyuqiong0104@163.com

更新日期/Last Update: 2021-04-25
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