Route Optimization Strategy of Military Cooperative Inspection

doi:10.3969/j.issn.1000-1093.2019.08.016

Abstract

Abstract: An improved cooperative ant colony optimization algorithm is proposed to enhance the inspection efficiency of large-scale inspection system with multiple robots. Each robot has an ant colony to search its inspection path, and a sharing taboo list is designed to implement the information interaction among the different ant colonies. The cost competitive mechanism is used to determine an ant among different ant colonies to search the inspection node. According to the distribution of the inspection nodes, the cooperative ant colony optimization algorithm could be used to accomplish the region segmentation and the path optimization simultaneously. Thus the inspection region could be segmented reasonably. Experimental results show that the cooperative ant colony optimization algorithm could be used to segment the inspection task more evenly than the conventional method based on map segmentation, which enhances the utilization rate of inspection robots, and the total inspection workload could be reduced. Therefore, the inspection performance could be improved significantly. Key

Key words: cooperativeinspection, antcolonyalgorithm, pathoptimization, multi-robot, taboolist

CLC Number:

TP301.6

HAN Yuxing, DING Gangyi, CHAI Zuohong. Route Optimization Strategy of Military Cooperative Inspection[J]. Acta Armamentarii, 2019, 40(8): 1673-1679.

References

［1］ LINS R G, GIVIGI S N, FREITAS A D, et al. Autonomous robot system for inspection of defects in civil infrastructures[J]. IEEE Systems Journal, 2018, 12(2):1414-1422.
[2］ CASTRO M D, FERRE M, MASI A. CERNTAURO: a modular architecture for robotic inspection and telemanipulation in harsh and semi-structured environments[J]. IEEE Access, 2018, 6: 37506-37522.
[3］ MACLEOD C N,DOBIE G, PIERCE S G, et al. Machining-based coverage path planning for automated structural inspection[J]. IEEE Transactions on Automation Science and Engineering, 2018, 15(1):202-213.
[4］ KUMAR P, GARG S, SINGH A. MVO-based 2-D path planning scheme for providing quality of service in UAV environment[J]. IEEE Internet of Things Journal, 2018, 5(3): 1698-1707.
[5］ ZHANG B, DUAN H B. Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14(1):97-107.
[6］ SHEN C, SHI Y, BUCKHAM B. Integrated path planning and tracking control of an AUV: a unified receding horizon optimization approach[J]. IEEE/ASME Transactions on Mechatronics, 2017, 22(3):1163-1173.
[7］杨廷鸿,方海洋,姜大立,等.考虑最大暴露值的无线传感器网络最小暴露路径优化算法[J].兵工学报,2018,39(4):743-752.
YANG T H, FANG H Y, JIANG D L, et al. Optimization algorithm for minimum exposure path in wireless sensor networks considering maximum exposure [J]. Acta Armamentarii, 2018, 39(4): 743-752. (in Chinese)
[8］ EDELKAMP S, POMARLAN M, PLAKU E. Multiregion inspection by combining clustered traveling salesman tours with sampling-based motion planning [J]. IEEE Robotics and Automation Letters, 2017,2(2):428-435.
[9］ JING W, GOH C F, RAJARAMAN M. A computational framework for automatic online path generation of robotic inspection tasks via coverage planning and reinforcement learning [J]. IEEE Access, 2018,6:54854-54864.
[10］ BOGAERTS B, SELS S, VANLANDUIT S, et al. A gradient-based inspection path optimization approach[J]. IEEE Robotics and Automation Letters, 2018, 3(3):2646-2653.
[11］ ZHANG W B, ZHU G Y. Drilling path optimization by optimal foraging algorithm [J]. IEEE Transactions on Industrial Informatics, 2018, 14(7):2847-2856.
[12］ XIA Y S, WANG J. A bi-projection neural network for solving constrained quadratic optimization problems [J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 214-224.
[13］ QIN S T, FENG J Q, SONG J H. A one-layer recurrent neural network for constrained complex-variable convex optimization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(3):534-544.
[14］ SAHA B K, MISRA S, PAL S. SeeR: simulated annealing-based routing in opportunistic mobile networks[J]. IEEE Transactions on Mobile Computing, 2017, 16(10):2876-2888.
[15］ ZHANG J H, ZHANG Y, ZHOU Y. Path planning of mobile robot based on hybrid multi-objective bare bones particle swarm optimization with differential evolution[J]. IEEE Access, 2018, 6:44542-44555.
[16］ TONG Y F, ZHONG M, LI J G, et al. Research on intelligent welding robot path optimization based on GA and PSO algorithms[J]. IEEE Access, 2018, 6:65397-65404.
[17］ SUN Y J, DONG W X, CHEN Y H. An improved routing algorithm based on ant colony optimization in wireless sensor networks [J]. IEEE Communications Letters, 2017, 21(6):1317-1320.
[18］ L J H, WANG X W, HUANG M. Ant colony optimization-inspired ICN routing with content concentration and similarity relation[J]. IEEE Communications Letters, 2017, 21(6):1313-1316.
[19］ SUN Z W, ZHANG Z W, XIAO C, et al. D-S evidence theory based trust ant colony routing in WSN [J]. China Communications, 2018, 15(3):27-41.
[20］ LIAO E C, LIU C G. A hierarchical algorithm based on density peaks clustering and ant colony optimization for traveling salesman problem[J]. IEEE Access, 2018, 6:38921-38933.
[21］李立,唐宁九,林涛.基于地图分割与以矢量信息描述地图的A^*寻路算法[J].四川大学学报（自然科学版）,2010,47(4):729-734.
LI L, TANG N J, LIN T. A^* algorithm based on map segmentation and vector information described map[J]. Journal of Sichuan University (Natural Science Edition), 2010, 47(4): 729-734. (in Chinese)

第40卷
第8期2019 年8月兵工学报ACTA
ARMAMENTARIIVol.40No.8Aug.2019

[1]	ZHANG Jiawen， FANG Haolin， LI Jiawang. 3D Path Planning of Underactuated AUV Based on Complex Constraints [J]. Acta Armamentarii, 2022, 43(6): 1407-1414.
[2]	GUO Wei， WU Kai， ZHOU Yue， SUN Hongming， XU Gaofei， GAO Sen. Path Planning of Deep-sea Landing Vehicle Based on Ant Colony Algorithm [J]. Acta Armamentarii, 2022, 43(6): 1387-1394.
[3]	L Guojun， CAO Jianjun， ZHENG Qibin， CHANG Chen， WENG Nianfeng， PENG Cong. Detection of Similar Duplicate Records Based on OCSVM and Multi-objective Ant Colony Optimization [J]. Acta Armamentarii, 2020, 41(2): 324-331.
[4]	HU Tao, MA Chenhui, SHEN Liqun, LIANG Jie. Scheduling Optimization of Test Tasks Based on Ant Colony Algorithm [J]. Acta Armamentarii, 2019, 40(6): 1310-1316.
[5]	WEI Heng， L Qiang， LIU Yang， LIN Huican， LIANG Bing. State Switching-based Distributed Multi-robot Formation Control [J]. Acta Armamentarii, 2019, 40(5): 1103-1112.