Solving Multi-robot Task Assignment Problem based on Improved Genetic Algorithm
DOI:
https://doi.org/10.54097/sctr7h26Keywords:
Genetic Algorithm, Multi-Robot, Task Allocation, Three Crossover Operators, Variation StrategyAbstract
Aiming at the low efficiency of robot task distribution and unbalanced task distribution in the process of warehouse task scheduling, this paper aims at minimum total task path and balanced task distribution, establishes a warehouse robot scheduling scheme and mathematical model, and proposes an improved genetic algorithm for task allocation. Firstly, greedy algorithm is introduced to optimize the initial population and improve the quality of the initial population. Secondly, the heuristic bidirectional triple crossover operator is designed to expand the search range of the population and improve the accuracy of the optimal solution. Finally, the multi-variant strategy is introduced to solve the problem that the algorithm falls into local optimality. The experimental results show that the improved algorithm has better convergence and balanced distribution ability.
Downloads
References
[1] Li Yansheng, Wan Yong, Zhang Yi, et al. Path planning of storage robot based on artificial bee colony-adaptive genetic algorithm [J]. Chinese Journal of Scientific Instrument,2022, 43 (04): 282-290.
[2] M. Rohini., B. Manohari. and S. Adhithyan., Genetic Algorithm Based Optimization in Solving Multi Robot Task Allocation Problems,2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022, pp. 344-350.
[3] Yang Guihua, Wei Jiale. Warehouse task scheduling based on immune ant colony optimization algorithm[J]. Modular Machine Tools and Automated Processing Technology, 2023, (01):179-183.
[4] Zhou Hang, Qin Shihong, Fang Jingcheng. Multi-robot task allocation based on hybrid genetic taboo search algorithm[J]. Automation & Instrumentation,2023,38(11):35-39.
[5] Deng Fuqin, Huang Huanzhao, Tan Chaoen, et al. Multi-robot task allocation algorithm combining genetic algorithm and rolling scheduling[J]. Journal of Computer Applications,2023, 43 (12):3833-3839.
[6] Zhu Bowen, Cui Fengying. Warehouse robot task scheduling algorithm integrating multi-strategy optimization SSA[J]. Combined Machine Tool and Automation Processing Technology, 2024,(05):183-187+192.
[7] Hamza C, François G ,Edouard L ,et al .Optimization techniques for Multi-Robot Task Allocation problems: Review on the state-of-the-art[J].Robotics and Autonomous Systems, 2023, 168.
[8] Wang Jing, Zhang Gong, Zheng Jiahong, et al. Research review and prospect of multi-robot optimization layout and task allocation [J]. Machine Tool and Hydraulics,2021,49(16):161-167.
[9] Sun Bing, Wang Chuan, Yang Qiang, et al. Evolutionary algorithm for multi-starting point equilibrium multi-traveling salesman problem[J]. Computer Engineering and Design, 2023, 44 (07): 2030-2038.
[10] S. Yang et al., A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization. in IEEE Access, 2021, vol. 9,121316-121328.
[11] XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J] .Systems Science& Control Engineering, 2020,8(1):22-34.
[12] CHEN K-C, LIN S-C, HSIAO J-H, et al. Wireless networked multirobot systems in smart factories [J]. Proceedings of the IEEE, 2020, 109(4): 468-494.
[13] KIM J, SON HI. AVoronoi diagram-based workspace partition for weak cooperation of multi-robot system in orchard [J]. IEEE Access, 2020, 8: 20676-20686.
[14] Yang Wei, Li Ran, Zhang Kun. Optimization of multi-automatic guided vehicle task allocation based on variable neighborhood simulated annealing algorithm[J]. Journal of Computer Applications,2021,41(10):3056-3062.
[15] Katoch, S., Chauhan, S.S. & Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl, 2021, 80, 8091–8126.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.