Abstract
Automatic guided vehicle multi-target point navigation plays an extremely important role in logistics and transportation, industrial automation, warehouse management and other industries. Multiple navigation points are abstracted as points in a two-dimensional raster map, and each navigation point is assumed to be a city point, thus establishing a mathematical model for travelers. This paper proposes a custom distance calculation algorithm to calculate the global path length between navigation points. An ant colony algorithm is used as the multi-objective navigation optimization algorithm, and the global search capability is enhanced by using Levy flight in the construction of the solution part, and a loga-rithmic function is introduced to make the overall step length of Levy flight change dynamically. For the problem that the ant colony algorithm is prone to fall into local optimum, two local search algorithms are used to search in turn, incor-porating the advantages of different local search algorithms. The improved ant colony algorithm reduces the error by 0.05%-1.46% compared with the basic ant colony algorithm plus one local search. Finally, the feasibility of the whole sys-tem design is verified by using Turtlebot3 in a joint ROS-Gazebo simulation.
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References
Gambardella, L.M., Dorigo, M.: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)
Dorigo, M., Maniezzo, V., Colorni, A., et al.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man, Cybern. Part B (Cybern.), 26(1), 29–41 (1996)
Bullnheimer, B.: A new rank based version of the ant system: a computational study. Central Eur. J. Oper. Res. Econ 7, 25–38 (1999)
Stützle, T., Hoos, H.H.: MAX–MIN ant system. Futur. Gener. Comput. Syst. 16(8), 889–914 (2000)
Liu, Y., Cao, B., Li, H.: Improving ant colony optimization algorithm with epsilon greedy and Levy flight. Complex Intell. Syst. 7(4), 1711–1722 (2021)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)
Dong, R., Wang, S., Wang, G., Wang, X., et al.: Hybrid optimization algorithm based on wolf pack search and local search for solving traveling salesman problem. J. Shanghai Jiaotong Univ. (Sci.) 24, 41–47 (2019)
Gu, J., Huang, X.: Efficient local search with search space smoothing: a case study of the traveling salesman problem (TSP). IEEE Trans. Syst. Man Cybern. 24(5), 728–735 (1994)
Helsgaun, K.: General k-opt submoves for the Lin–Kernighan TSP heuristic. Math. Program. Comput. 1(2–3) (2009)
Wang, Y.: The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem. Comput. Ind. Eng.. Ind. Eng. 70, 124–133 (2014)
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Li, J. (2024). Improved Ant Colony Algorithm for AGV Multi-objective Point Navigation. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_46
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DOI: https://doi.org/10.1007/978-981-97-2757-5_46
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