Yuejun Zhao
ABSTRACT
With gradually popularity of the application of AGVs in automated factories, the rational and efficient path planning and scheduling of AGVs can improve the operational efficiency of factories. The search efficiency of the existing A* algorithm is not high and the adaptation of the genetic algorithm remains to be improved. Considering the above problems, this paper proposes a self-adaptive cluster scheduling strategy, adopts the parallel computing to improve the search efficiency of the A* algorithm, and puts forward an improved genetic algorithm based on the dynamic fitness function which does not need to modify the structure of the genetic algorithm according to the changes of the environment so that the adaptability of the algorithm can be improved. Simulation experiments of multi-AGV scheduling are also conducted. The results show that the method adopted in this study has obvious advantages in path solving speed and can adapt to different numbers of obstacles, which provides a reference for the development and application of AGV cluster scheduling methods in real operation scenarios.
- Tang Le. Research on the Key Technology of Multi-AGV Scheduling System for Intra-company Logistics. Guilin: Guilin University of Electronic Technology, 2021.Google Scholar
- Zhang Yi, Quan Hao, Wen Jiafu. Mobile Robot Path Planning Based on the Wolf Ant Colony Hybrid Algorithm. Huazhong University of Science and Technology (Natural Science Edition), 2020, 48(01):127-132.Google Scholar
- Liu Guodong, Qu Daokui, Zhang Lei. Two-stage Dynamic Path Planning for Multi-AGV Scheduling Systems. Robot, 2005,(03):210-214.Google Scholar
- Liu Erhui, Yao Xifan, Tao Tao Improved Flower Pollination Algorithm for Job Shop Scheduling Problems Integrated with AGVs. Computer Integrated Manufacturing Systems, 2019, 25(09):2219-2236.Google Scholar
- ZOUW Q, PAN Q K, TASGETIREN M F. An effective iterated greedy algorithm for solving a multi-compartment AGV scheduling problem in a matrix manufacturing workshop. Applied Soft Computing, 2021, 99:106945.Google ScholarCross Ref
- ZOU W Q,PAN Q K,WANG L.An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery. Knowledge-Based Systems, 2021, 218:106881.Google ScholarDigital Library
- He Lina, Lou Peihuang, Qian Xiaoming Conflict-free Automated Guided Vehicles Routing Based on Time Window. Computer Integrated Manufacturing Systems, 2010, 16(12):2630-2634.Google Scholar
- MURAKAMI K.Time-space network model and MILP formulation of the conflict-free routing problem of a capacitated AGV system. Computers & Industrial Engineering, 2020, 141:106270.Google ScholarDigital Library
- ZHONG M, YANG Y, DESSOUKY Y. Multi-AGV scheduling for conflict-free path planning in automated container terminals. Computers & Industrial Engineering, 2020, 142:106371.Google ScholarDigital Library
- Zhang Xinyan, Zou Yasheng. Collision-free path planning for automated guided vehicles based on improved A* algorithm. Systems Engineering Theory and Practice, 2021, 41(01):240-246.Google Scholar
- Li Pei, Li Xinde. AGV obstacle avoidance algorithm based on multi-sensor information fusion. Huazhong University of Science and Technology: Natural Science Edition, 2015, 43(S1):224-227.Google Scholar
- Draganjac I, Miklić D, Kovačić Z Decentralized control of multi-AGV systems in autonomous warehousing applications. IEEE Transactions on Automation Science and Engineering, 2016, 13(4):1433-1447.Google Scholar
- Meng Guanjun, Chen Xinhua, Tao Xipei, AGV Path Planning Based on Ant Colony Algorithms. Modular Machine Tool and Automatic Manufacturing Technique, 2021(01):70-73.Google Scholar
- Du Zuoying, Li Jinxi, Zhang Xianglai AGV Path Planning Based on Improved Q-Learning Algorithm. Logistics Technology, 2020, 39(12):88-92.Google Scholar
- Yang Jun, Zhan Jun, She Yong. A Multi-Objective Optimization Model-Based Scheduling Method for multi-load AGVs in Ports. Navigation of China, 2022(45):66-72.Google Scholar
- Liu Guodong, Qu Daokui, Zhang Lei. Two-stage dynamic path planning for Multiple AGV Scheduling Systems. Robot, 2005(03):210-214.Google Scholar
- Jiao Fuming. Research and Realization of AGV Scheduling in Automatic Storage and Retrieval System. Shandong: Shandong University, 2013.Google Scholar
- Li Zhengfeng, Liu Yangyang. Job Shop Scheduling Problem for Multiple AGVs Considering Charging. Computer Integrated Manufacturing Systems, 2021, 10(27):2972-2879.Google Scholar
- Li Xixing, Yang Daoming, Li Xin Flexible Job Shop AGV Fusion Scheduling Method based on HGWOA. China Mechanical Engineering, 2021, 32(8):938-950.Google Scholar
- Gao Ming, Tang Hong, Zhang Peng. Current Status of Research on Robot Cluster Path Planning Technology. Journal of National University of Defense Technology, 2021, 43(01):127-138.Google Scholar
- Chen Guangrong, Guo Sheng, Wang Junzheng A Path Avoidance Algorithm Combining Convex Optimization and A* Algorithm. Control and Decision, 2020, 35(12):2907-2914.Google Scholar
- Zhang Weimin, Fu Shixiong. Path Planning for Mobile Robots Based on Improved RRT* Algorithm. Huazhong University of Science and Technology: Natural Science Edition, 2021, 49(1):31-36.Google Scholar
- Zhang Danhong, Chen Wenwen, Zhang Huajin A* Algorithm Combined with Ant Colony Algorithm for Unmanned Boat Patrol Path Planning. Huazhong University of Science and Technology: Natural Science Edition, 2020, 48(6):13-18.Google Scholar
- Research on AGV Cluster Scheduling Method Based on Parallel Search Algorithm
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