楼建坤, 王鸿东, 王检耀, 等. 基于机器学习的实海域无人艇避碰算法智能演进方法[J]. 中国舰船研究, 2021, 16(1): 65–73. doi: 10.19693/j.issn.1673-3185.02116
引用本文: 楼建坤, 王鸿东, 王检耀, 等. 基于机器学习的实海域无人艇避碰算法智能演进方法[J]. 中国舰船研究, 2021, 16(1): 65–73. doi: 10.19693/j.issn.1673-3185.02116
LOU J K, WANG H D, WANG J Y, et al. Intelligent evolution method for obstacle-avoidance algorithm of unmanned surface vehicles in real sea trial based on machine learning [J]. Chinese Journal of Ship Research, 2021, 16(1): 65–73. doi: 10.19693/j.issn.1673-3185.02116
Citation: LOU J K, WANG H D, WANG J Y, et al. Intelligent evolution method for obstacle-avoidance algorithm of unmanned surface vehicles in real sea trial based on machine learning [J]. Chinese Journal of Ship Research, 2021, 16(1): 65–73. doi: 10.19693/j.issn.1673-3185.02116

基于机器学习的实海域无人艇避碰算法智能演进方法

Intelligent evolution method for obstacle-avoidance algorithm of unmanned surface vehicles in real sea trial based on machine learning

  • 摘要:
      目的  无人艇(USV)效能是指在给定时间内的特定海域完成指定任务的能力,是多层次多技术节点耦合作用的结果,而针对单一技术节点的传统优化方法,对无人艇效能的提升效果有限。
      方法  针对无人艇自主系统的特点,从智能算法的角度,提出无人艇智能演进的2种主要形式:一是算法函数;二是算法参数。在此基础上,给出基于机器学习的无人艇智能演进方法,设计一种可演进的无人艇自主系统控制体系架构,并在实海域测试。
      结果  以无人艇避碰算法为例,基于实海域测试结果,初步验证了所提方法在提升无人艇效能方面的可行性与有效性。
      结论  基于机器学习的无人艇智能演进方法是持续提升无人艇效能的有效途径,具有较高研究价值和应用意义。

     

    Abstract:
      Objectives  The performance of unmanned surface vehicles (USVs) is defined as the ability to complete specific tasks in specific environments within a given time scale as a result of the cooperation of multiple technical aspects. However, the traditional optimization method that forcus on a single part in the system provides limited effect on improving the performance of USVs.
      Methods  Based on the features of autonomous system of USVs, two main forms of the intelligent evolution of USVs are conducted from the perspective of algorithms: the evolution of algorithm functions and evolution of algorithm parameters respectively. In this case, a machine learning-based intelligent evolution method is proposed. An automatic USV control system which satisfies the requirements of intelligent evolution is then designed and tested in a sea trial.
      Results  The obstacle-avoidance task in the sea trial proves the capability and feasibility of the proposed method.
      Conclusion  The machine learning-based intelligent evolution of USVs is an effective way to continuously improve the performance of USVs, making it a worthy research topic with high application value.

     

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