An Integrative APP Producing an Optimal Path for the Vessel in Order to Reduce the Impacts of Cargo Ships on the Environment

Chenyu Zuo, Y. Sun
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Abstract

Almost every business in the world relies in some way on the shipping industry, whether it is to ship goods or natural resources, the shipping industry is undeniably the global industry, However, these very ships that drive the economy also produce close to 1 billion metric tons of carbon dioxide per year. In this project, we explore the use of machine learning to improve the performance of cargo ships in the ocean by implementing a genetic algorithm AI and a virtual simulation environment. An app was made based on using the training developed by the AI to be able to be deployed on cargo ships as part of their navigation system. Once sufficient data regarding a vessel’s environment was collected, the algorithm could then produce an optimal path for the vessel. Experiments show that the AI system could sufficiently adjust to varying conditions and produce optimal paths for vessels.
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一个为船舶生成最优路径以减少货船对环境影响的集成应用程序
世界上几乎所有的企业都在某种程度上依赖于航运业,无论是运输货物还是自然资源,航运业无疑是全球产业,然而,这些推动经济发展的船只每年也产生近10亿吨的二氧化碳。在这个项目中,我们通过实施遗传算法AI和虚拟仿真环境,探索使用机器学习来提高海洋货船的性能。根据人工智能开发的训练,开发了一款应用程序,可以作为货船导航系统的一部分部署在货船上。一旦收集到有关船舶环境的足够数据,该算法就可以为船舶产生最佳路径。实验表明,人工智能系统可以充分适应不同的条件,并为船舶产生最优路径。
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