Improved Ant Colony Algorithm in Automatic Following Luggage

  • Wengao SunEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


At present, many people, especially when going out to study, work, and travel, cannot do without suitcases. The luggage compartment can integrate some essential items into one space. If there is less luggage, it is more convenient to bring a suitcase out, but when there is more luggage and the suitcase is relatively large, it is a burden for the user. The heavy luggage not only consumes the user’s physical strength, but also may being overweight makes it difficult to go through security. In recent years, some suitcases that can automatically follow the user have appeared on the market, which is relatively convenient. However, there are also some problems, such as encountering obstacles that may slow down the action, slow down the user’s rhythm, and become stuck due to uneven road surfaces and other issues. The purpose of this paper is to optimize the optimal path selection of the luggage, so that the luggage can quickly find the shortest way to reach the target under complicated road conditions. This paper improves the ant colony algorithm and uses it in the luggage auto-following system. The adaptive adjustment of the volatility coefficient p is used to solve the problem of slow convergence speed and easy fall into the local optimal solution of the algorithm during operation. Lattice model uses different algorithms for the same luggage to perform experiments in the same road conditions, and calculate, record the path length and the corresponding number of iterations obtained from the experiment and compare them. Experimental results show that the improved algorithm is superior to traditional algorithms. In the improved algorithm results, the optimal path length and the corresponding number of iterations are smaller than those obtained by traditional algorithms, especially the number of iterations is much smaller than that obtained by traditional algorithms. Obviously, the improved algorithm in this paper can be used to automatically follow the luggage and enhance its performance to some extent.


Ant colony optimization Auto follow Trolley luggage Optimal path length 



Youth Project Funded by Xi’an Traffic Engineering Institute: System of Self Following Trunk Based on Arduino (Program No. 19KY-37).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Xi’an Traffic Engineering InstituteXi’anChina

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