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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (19): 28569-028569.doi: 10.7527/S1000-6893.2023.28569

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Mechanisms, algorithms, implementation and perspectives of brain⁃inspired navigation

Xiangwei ZHU1, Dan SHEN2(), Kai XIAO1,3, Yuexin MA1, Xiang LIAO4, Fuqiang GU5, Fangwen YU6, Kefu GAO7, Jingnan LIU7   

  1. 1.School of Electronics and Communication Engineering,Sun Yat?sen University,Shenzhen 518107,China
    2.School of System Science and Engineering,Sun Yat?sen University,Guangzhou 510006,China
    3.Institute of Geospatial Information,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
    4.College of Medicine,Chongqing University,Chongqing 400030,China
    5.School of Computer Science,Chongqing University,Chongqing 400044,China
    6.Department of Precision Instruments,Tsinghua University,Beijing 100084,China
    7.Research Center of Satellite Navigation and Positioning Technology,Wuhan University,Wuhan 430079,China
  • Received:2023-02-17 Revised:2023-03-20 Accepted:2023-03-31 Online:2023-10-15 Published:2023-04-11
  • Contact: Dan SHEN E-mail:shend7@mail2.sysu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(61973328);Ministry of Education-China Mobile Scientific Research Fund(MCM2020-J-1);Shenzhen Science and Technology Program(GXWD20201231165807008)

Abstract:

The rapid development of brain and neuroscience in recent decades has initially revealed the neural mechanism of animal navigation. Drawing on the brain neural structures and information processing mechanisms, the study of brain-inspired intelligent navigation systems provides new inspiration for low-power, highly robust autonomous intelligent navigation in complex environments. Based on a detailed review of the neural mechanisms of animal spatial navigation, this paper then outlines and discusses current intelligent algorithms for robotic bionic brain-inspired navigation, which can be categorized into three types according to the three types of neural networks used to process navigation information for intelligent navigation: attractor neural networks, deep reinforcement learning, and spiking neural networks. Then, the ways for implementing brain-inspired navigation, including bionic intelligent sensors and neuromorphic processor platforms, are sorted out. Finally, the development trend of brain-inspired navigation is discussed, including further exploration of the brain neural mechanism of navigation in the biological world and its information processing process with low energy consumption and high robustness mechanism, subcategorization of the conceptual connotation of brain-inspired navigation, and the ways to improve the evaluation index and the unified implementation framework.

Key words: brain-inspired navigation, neural mechanism of navigation, navigation cells, continuous attractor network, deep reinforcement learning, spiking neural network, neuromorphic camera, brain-inspired chips

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