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http://dx.doi.org/10.7837/kosomes.2022.28.7.1216

A Study on Image-Based Mobile Robot Driving on Ship Deck  

Seon-Deok Kim (Division of Maritime Engineering, Mokpo National Maritime University)
Kyung-Min Park (Division of Coast Guard, Mokpo National Maritime University)
Seung-Yeol Wang (Division of Ocean System Engineering, Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.7, 2022 , pp. 1216-1221 More about this Journal
Abstract
Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.
Keywords
Mobile Robot; Ship Deck; Machine Learning; Smart Autonomous Logistics System; Image-Based;
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