|Table of Contents|

BFO-ACO roaming path planning based on multi-constraint scenarios(PDF)

Journal of Shenzhen University Science and Engineering[ISSN:1000-2618/CN:44-1401/N]

Issue:
2022 Vol.39 No.4(363-488)
Page:
463-471
Research Field:
Electronics and Information Science

Info

Title:
BFO-ACO roaming path planning based on multi-constraint scenarios
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
Keywords:
computer application path planning multiple constraints roaming deadlock ant colony algorithm bacterial foraging algorithm virtual environment
PACS:
P315.69
DOI:
10.3724/SP.J.1249.2022.04463
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.

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)

Memo

Memo:
-