Abstract
This paper presents the design of an intelligent cart system to be used in a typical airport. The intelligent cart system consists of a set of mobile software agents to control the cart and provides a novel method for alignment. If the carts gather and align themselves automatically after being used, it is beneficial for human workers who have to collect them manually. To avoid excessive energy consumption through the collection of the carts, in the previous study, we have used ant colony optimization (ACO) and a clustering method based on the algorithm. In the current study, we have extended the ACO algorithm to use the vector values of the scattered carts in the field instead of mere location. We constructed a simulator that performs ant colony clustering using vector similarity. Waiting time and route to the destination of each cart are made based on the cluster created this way. These routes and waiting times are conveyed by the agent to each cart, while making them in rough lines. Because the carts are clustered by the similarity of vectors, we have observed that several groups have appeared to be aligned. The effectiveness of the system is demonstrated by constructing a simulator and evaluating the results.
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Sugiyama, S., Yamachi, H., Takimoto, M., Kambayashi, Y. (2012). Aggregating Multiple Robots with Serialization. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_18
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DOI: https://doi.org/10.1007/978-3-642-28487-8_18
Publisher Name: Springer, Berlin, Heidelberg
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