[1]林晓玲,王志强,等.基于多约束场景的BFO-ACO漫游路径规划[J].深圳大学学报理工版,2022,39(4):463-471.[doi:10.3724/SP.J.1249.2022.04463]
 LIN Xiaoling,WANG Zhiqiang,GUO Yanyan,et al.BFO-ACO roaming path planning based on multi-constraint scenarios[J].Journal of Shenzhen University Science and Engineering,2022,39(4):463-471.[doi:10.3724/SP.J.1249.2022.04463]
点击复制

基于多约束场景的BFO-ACO漫游路径规划()
分享到:

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第4期
页码:
463-471
栏目:
电子与信息科学
出版日期:
2022-07-12

文章信息/Info

Title:
BFO-ACO roaming path planning based on multi-constraint scenarios
文章编号:
202204014
作者:
林晓玲1 王志强1 2 郭岩岩2 朱泽轩1
1)深圳大学计算机与软件学院,广东深圳 518060
2)深圳大学信息中心,广东深圳 518060
Author(s):
LIN Xiaoling1 WANG Zhiqiang1 2 GUO Yanyan2 and ZHU Zexuan1
1) College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P. R. China
2) Information Center, Shenzhen University, Shenzhen 518060, Guangdong Province, P. R. China
关键词:
计算机应用路径规划多约束漫游死锁蚁群算法细菌觅食算法虚拟环境
Keywords:
computer application path planning multiple constraints roaming deadlock ant colony algorithm bacterial foraging algorithm virtual environment
分类号:
P315.69
DOI:
10.3724/SP.J.1249.2022.04463
文献标志码:
A
摘要:
目前基于蚁群算法的路径规划用于多约束条件下寻找最优路径时,容易陷入局部最优解并导致收敛速度慢.为此,在路径长度、有效景点区域数量、路径平滑性和路径障碍距离等约束条件下,构造一种适应度函数模型,以评价漫游路径的质量.提出混合细菌觅食优化思想的改进蚁群优化(bacterial foraging optimization and ant colony optimization, BFO-ACO)算法,采用禁忌表优化策略解决传统蚁群算法的死锁问题,提高算法初期的路径多样性,通过引入细菌觅食算法的复制和驱散机制,提高收敛速度,跳出局部最优值.实验结果表明,BFO-ACO算法可在多约束环境下以较少的迭代次数获得高质量的漫游路径,为漫游路径设计提供了参考.
Abstract:
In recent years, when the path planning based on ant colony algorithm is used to find the optimal path under multiple constraints, it is easy to fall into the local optimal solution and lead to slow convergence. Under the constraints of path length, number of effective scenic spots, path smoothness and path obstacle distance, this paper constructs a fitness function model to evaluate the quality of roaming path. A hybrid algorithm of bacterial foraging optimization and ant colony optimization (BFO-ACO) is proposed by using the taboo table optimization strategy to solve the deadlock problem in the traditional ant colony algorithm, which improves the path diversity in the early stage of the algorithm. The replication and dispersion mechanism of bacterial foraging algorithm is introduced to improve the convergence speed as well as jump out of the local optimum. Experimental results show that the BFO-ACO algorithm obtains a higher-quality roaming path with the fewer iterations under multi-constrained environments, which provides the foundation for designing roaming path.

参考文献/References:

[1] 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.
[2] ALI H, GONG Dawei, WANG Meng, et al. Path planning of mobile robot with improved ant colony algorithm and MDP to produce smooth trajectory in grid-based environment [J]. Frontiers in Neurorobotics, 2020, 14: 44.
[3] 张松灿,普杰信,司彦娜,等.蚁群算法在移动机器人路径规划中的应用综述[J].计算机工程与应用,2020,56(8):10-19.
ZHANG Songcan, PU Jiexin, SI Yanna, et al. Survey on application of ant colony algorithm in path planning of mobile robot [J]. Computer Engineering and Applications, 2020, 56(8): 10-19.(in Chinese)
[4] HAO Kun, ZHAO Jiale, WANG Beibei, et al. The application of an adaptive genetic algorithm based on collision detection in path planning of mobile robots [J]. Computational Intelligence and Neuroscience,2021, 2021: 5536574.
[5] DAMOS M A, ZHU Jun, LI Weilian, et al. A novel urban tourism path planning approach based on a multiobjective genetic algorithm [J]. ISPRS International Journal of Geo-Information, 2021, 10(8): 530.
[6] SHAO Shikai, PENG Yu, HE Chenglong, et al. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization [J]. ISA Transactions, 2020, 97: 415-430.
[7] DAS P K, JENA P K. Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators [J]. Applied Soft Computing, 2020, 92: 106312.
[8] 王洪斌,尹鹏衡,郑维,等.基于改进的A*算法与动态窗口法的移动机器人路径规划[J].机器人,2020,42(3):346-353.
WANG Hongbin, YIN Pengheng, ZHENG Wei, et al. Mobile robot path planning based on improved A* algorithm and dynamic window method [J]. Robot, 2020, 42(3): 346-353.(in Chinese)
[9] ZHANG Zhe, WU Jian, DAI Jiyang, et al. A novel real-time penetration path planning algorithm for stealth UAV in 3D complex dynamic environment [J]. IEEE Access, 2020, 8: 122757-122771.
[10] DORIGO M, MANIEZZO V, COLORNI A. Ant system: optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1): 29-41.
[11] JIAO Zhuqing, MA Kai, RONG Yiling, et al. A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs [J]. Journal of Computational Science, 2018, 25: 50-57.
[12] 辛建霖,左家亮,岳龙飞,等.基于改进启发式蚁群算法的无人机自主航迹规划[J].航空工程进展,2022,13(1):60-67.
XIN Jianlin, ZUO Jialiang, YUE Fielong, et al. Autonomous path planning for unmanned aerial vehicle (UAV) based on improved heuristic ant colony algorithm [J]. Advances in Aeronautical Science and Engineering, 2022, 13(1): 60-67.(in Chinese)
[13] MIAO Changwei, CHEN Guangzhu, YAN Chengliang, et al. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm [J]. Computers & Industrial Engineering, 2021, 156: 107230.
[14] CHEN Lan, SU Yixin, ZHANG Danhong, et al. Research on path planning for mobile robots based on improved ACO [C]// The 36th Youth Academic Annual Conference of Chinese Association of Automation. Nanchang, China: IEEE, 2021: 379-383.
[15] 陈志,韩兴国.改进蚁群算法在移动机器人路径规划上的应用[J].计算机工程与设计,2020,41(8):2388-2395.
CHEN Zhi, HAN Xinguo. Application of improved ant colony algorithm in mobile robot path planning [J]. Computer Engineering and Design, 2020, 41(8): 2388-2395.(in Chinese)
[16] 张恒,何丽,袁亮,等.基于改进双层蚁群算法的移动机器人路径规划[J].控制与决策,2022,37(2):303-313.
ZHANG Heng, HE Li, YUAN Liang, et al. Mobile robot path planning using improved double-layer ant colony algorithm [J]. Control and Decision, 2022, 37(2): 303-313.(in Chinese)
[17] 孙功武,苏义鑫,顾轶超,等.基于改进蚁群算法的水面无人艇路径规划[J].控制与决策,2021,36(4):847-856.
SUN Gongwu, SU Yixin, GU Yichao, et al. Path planning for unmanned surface vehicle based on improved ant colony algorithm [J]. Control and Decision, 2021, 36(4): 847-856.(in Chinese)
[18] PASSINO K M. Biomimicry of bacterial foraging for distributed optimization and control [J]. IEEE Control Systems Magazine, 2002, 22(3): 52-67.
[19] 李二超,齐款款.改进双向蚁群算法的移动机器人路径规划[J].计算机工程与应用,2021,57(18):281-288.
LI Erchao, QI Kuankuan. Improved bidirectional ant colony algorithm mobile robot path planning [J]. Computer Engineering and Applications, 2022, 57(18): 281-288.(in Chinese)

相似文献/References:

[1]蔡华利,刘鲁,樊坤,等.基于BPSO的web服务推荐策略[J].深圳大学学报理工版,2010,27(1):49.
 CAI Hua-li,LIU Lu,FAN Kun,et al.Web services recommendation based on BPSO[J].Journal of Shenzhen University Science and Engineering,2010,27(4):49.
[2]朱泽轩,张永朋,尤著宏,等.高通量DNA测序数据压缩研究进展[J].深圳大学学报理工版,2013,30(No.4(331-440)):409.[doi:10.3724/SP.J.1249.2013.04409]
 Zhu Zexuan,Zhang Yongpeng,You Zhuhong,et al.Advances in the compression of high-throughput DNA sequencing data[J].Journal of Shenzhen University Science and Engineering,2013,30(4):409.[doi:10.3724/SP.J.1249.2013.04409]
[3]张滇,明仲,刘刚,等.基于传感器节点的无线接收信号强度研究(英文)[J].深圳大学学报理工版,2014,31(1):63.[doi:10.3724/SP.J.1249.2014.01063]
 Zhang Dian,Ming Zhong,Liu Gang,et al.An empirical study of radio signal strength in sensor networks using MICA2 nodes[J].Journal of Shenzhen University Science and Engineering,2014,31(4):63.[doi:10.3724/SP.J.1249.2014.01063]
[4]廖日军,李雄军,徐健杰,等.Arnold变换在二值图像置乱应用中若干问题讨论[J].深圳大学学报理工版,2015,32(4):428.[doi:10.3724/SP.J.1249.2015.04428]
 Liao Rijun,Li Xiongjun,Xu Jianjie,et al.Discussions on applications of Arnold transformation in binary image scrambling[J].Journal of Shenzhen University Science and Engineering,2015,32(4):428.[doi:10.3724/SP.J.1249.2015.04428]
[5]李雄军,廖日军,李金龙,等.图像Arnold变换中的准对称性问题与半周期现象[J].深圳大学学报理工版,2015,32(6):551.[doi:10.3724/SP.J.1249.2015.06551]
 Li Xiongjun,Liao Rijun,Li Jinlong,et al.Quasi-symmetry and the half-cycle phenomenon in scrambling degrees for images with pixel locations scrambled by Arnold transformation[J].Journal of Shenzhen University Science and Engineering,2015,32(4):551.[doi:10.3724/SP.J.1249.2015.06551]
[6]柴变芳,曹欣雨,魏春丽,等.一种主动半监督大规模网络结构发现算法[J].深圳大学学报理工版,2020,37(3):243.[doi:10.3724/SP.J.1249.2020.03243]
 CHAI Bianfang,CAO Xinyu,WEI Chunli,et al.An active semi-supervised structure exploring algorithm for large networks[J].Journal of Shenzhen University Science and Engineering,2020,37(4):243.[doi:10.3724/SP.J.1249.2020.03243]
[7]刘朝斌,孙雪,刘剑,等.基于物联网的高校校园智能安防建设探索[J].深圳大学学报理工版,2020,37(增刊1):128.[doi:10.3724/SP.J.1249.2020.99128]
 LIU Chaobin,SUN Xue,LIU Jian,et al.Campus intelligent security construction based on internet of things[J].Journal of Shenzhen University Science and Engineering,2020,37(4):128.[doi:10.3724/SP.J.1249.2020.99128]
[8]杨阳.高校大数据平台的规划设计与实现[J].深圳大学学报理工版,2020,37(增刊1):146.[doi:10.3724/SP.J.1249.2020.99146]
 YANG Yang.Design and implementation of big data platform in colleges[J].Journal of Shenzhen University Science and Engineering,2020,37(4):146.[doi:10.3724/SP.J.1249.2020.99146]
[9]龚黎旰,顾坤,明心铭,等.基于校园一卡通大数据的高校学生消费行为分析[J].深圳大学学报理工版,2020,37(增刊1):150.[doi:10.3724/SP.J.1249.2020.99150]
 GONG Ligan,GU Kun,MING Xinming,et al.Analysis of college students’ consumption behavior based on campus card data[J].Journal of Shenzhen University Science and Engineering,2020,37(4):150.[doi:10.3724/SP.J.1249.2020.99150]
[10]胡迪,靳文舟.基于站点优化的需求响应公交调度研究[J].深圳大学学报理工版,2022,39(2):209.[doi:10.3724/SP.J.1249.2022.02209]
 HU Di and JIN Wenzhou.Flex-route demand response transit scheduling based on station optimization[J].Journal of Shenzhen University Science and Engineering,2022,39(4):209.[doi:10.3724/SP.J.1249.2022.02209]

备注/Memo

备注/Memo:
Received: 2021-10-25; Accepted: 2022-01-06; Online (CNKI): 2022-05-13
Foundation: National Natural Science Foundation of China (61871272); Shenzhen Science and Technology Plan Project (GGFW2018020518310863)
Corresponding author: Professor WANG Zhiqiang. E-mail: wangzq@szu.edu.cn
Citation: LIN Xiaoling, WANG Zhiqiang, GUO Yanyan, et al. BFO-ACO roaming path planning based on multi-constraint scenarios [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(4): 463-471.(in Chinese)
基金项目:国家自然科学基金资助项目(61871272);深圳市科技计划资助项目(GGFW2018020518310863)
作者简介:林晓玲(1996—),深圳大学硕士研究生.研究方向:虚拟现实技术.E-mail: linxiaoling2019@email.szu.edu.cn
引文:林晓玲,王志强,郭岩岩,等.基于多约束场景的BFO-ACO漫游路径规划[J].深圳大学学报理工版,2022,39(4):463-471.
更新日期/Last Update: 2022-07-30