郭迟, 罗宾汉, 李飞, 陈龙, 刘经南. 类脑导航算法:综述与验证[J]. 武汉大学学报 ( 信息科学版), 2021, 46(12): 1819-1831. DOI: 10.13203/j.whugis20210469
引用本文: 郭迟, 罗宾汉, 李飞, 陈龙, 刘经南. 类脑导航算法:综述与验证[J]. 武汉大学学报 ( 信息科学版), 2021, 46(12): 1819-1831. DOI: 10.13203/j.whugis20210469
GUO Chi, LUO Binhan, LI Fei, CHEN Long, LIU Jingnan. Review and Verification for Brain-Like Navigation Algorithm[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1819-1831. DOI: 10.13203/j.whugis20210469
Citation: GUO Chi, LUO Binhan, LI Fei, CHEN Long, LIU Jingnan. Review and Verification for Brain-Like Navigation Algorithm[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1819-1831. DOI: 10.13203/j.whugis20210469

类脑导航算法:综述与验证

Review and Verification for Brain-Like Navigation Algorithm

  • 摘要: 类脑导航算法是近年来的新兴研究热点,这类算法通过对生物导航能力的模仿实现自主导航,核心问题是如何提升泛化能力。介绍了类脑导航算法的研究背景与理论基础,经过调研总结出了其计算框架;以类脑导航算法计算框架为骨干对该领域的突出工作进行了讨论分析,并通过严格的控制变量实验验证了一些典型改进方法的效果。主要贡献有:全面地介绍并总结了类脑导航领域的理论基础与突出工作;总结出了类脑导航算法的计算框架,该框架科学定义了算法不同部分的职能,从而能解构具体的算法,完成细粒度的分类和对比;通过理论分析与实验验证,总结出了有价值的结论,并展望了未来的发展。

     

    Abstract:
      Objectives  In recent years, the brain-like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core issue is how to improve generalization ability.
      Methods  This paper introduces the research background and theoretical basis of the brain-like navigation algorithm. After investigation, we propose a computational framework of brain-like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods.
      Results  The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain-like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations.
      Conclusions  In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability.

     

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