Abstract
In this paper, the problem of collision avoidance path optimization for multi-AGV systems in unmanned warehouses is studied. A multi-AGV collision avoidance path optimization strategy based on elastic time window and improved ant colony algorithm is proposed. In this paper, the traditional ant colony algorithm is improved by heuristic information and pheromone update strategy to improve the execution speed and optimization ability of the algorithm. The priority scheduling of AGV tasks and the improvement of conflict resolution strategies are proposed to solve the different path conflicts between multiple AGVs. Based on the environment of the e-commerce logistics unmanned warehouse, the MATLAB simulation software is used to model and analyze the multi-AGV collision avoidance path planning. The experimental results show that the multi-AGV collision avoidance path planning can be realized based on the elastic time window and the improved ant colony algorithm, and the optimal collision avoidance path can be found in a short time.
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Notes
- 1.
E represents the end grid; P represents the grid where the ants are located.
References
Guo, X., Ji, M., Liu, S.: AGV path planning in unmanned warehouse combining multi-objective and energy consumption control. Comput. Integr. Manuf. Syst. 1–15 (2019). http://kns.cnki.net/kcms/detail/11.5946.tp.20190315.0932.012.html
Gu, B.: Research on path optimization of automatic guided vehicle system based on time window. Lanzhou Traffic Science (2015)
Tai, Y., Xing, K., Lin, Y., Zhang, W.: Research on multi-AGV path planning method. Comput. Sci. 44(S2), 84–87 (2017)
He, C., Mao, J.: Research on the path of AGV based on improved ant colony algorithm. Logistics Sci. Technol. 42(03), 60–65 (2019)
Meng, C., Ren, Y.: Multi-AGV scheduling based on multiple population genetic algorithm. Electron. Technol. 31(11), 47–50+68 (2018)
Yuan, R., Wang, H., Sun, L., Li, J.: Research on Task scheduling of order picking system based on Logistics AGV. Oper. Res. Manage. 27(10), 133–138 (2018)
Tang, S., Hong, Z.: Research on robot path planning based on ant colony algorithm. Electromech. Inf. (08), 46–47 (2019)
Zhao, Y., Zhang, S.: Intelligent traffic path planning based on improved ant colony algorithm. Industr. Instrum. Autom. Device (02), 30–32 (2019)
Wei, Y., Jin, W.: Intelligent vehicle path planning based on neural network Q-learning algorithm. Firepower Command Control 44(02), 46–49 (2019)
Li, J., Xu, B., Yang, Y., Wu, H.: Induced ant swarm particle swarm optimization algorithm for multi-automatic guided vehicle path planning. Comput. Integr. Manuf. Syst. 23(12), 2758–2767 (2017)
Pu, X., Su, H., Zou, W., Wang, P., Zhou, H.: Smooth path planning based on non-uniform environment modeling and third-order Bezier curve. J. Autom. 43(05), 710–724 (2017)
Xia, Y., Fu, Z., Xie, J.: Multi-automatic guided vehicle material distribution path planning according to order resolution. Comput. Integr. Manuf. Syst. 23(07), 1520–1528 (2017)
Hu, Q., Wang, T., Zhang, R.: Research on improved ant colony algorithm in AGV global path planning. Inf. Technol. Informatization (03), 116–118 (2019)
Fisher, M.L., Rnsten, J., et al.: Vehicle routing with time windows. Two Optim. Algorithms Oper. Res. 45(3), 488–492 (1997)
Wang, S., Mao, Y., Yuan, X.: Multi-AGV path optimization strategy for finite state machines. J. Overseas Chin. Univ. (Natural Science Edition) 40(02), 239–244 (2019)
Chen, G., Liu, J., Zhang, C.: Ant colony optimization with potential field based on grid map for mobile robot path planning. J. Donghua Univ. (English Edition) 33(05), 764–767 (2016)
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Yang, Y., Zhang, J., Liu, Y., Song, X. (2020). Multi-AGV Collision Avoidance Path Optimization for Unmanned Warehouse Based on Improved Ant Colony Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_41
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DOI: https://doi.org/10.1007/978-981-15-3425-6_41
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