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A brain-inspired relative navigation method for collective UAVs based on neurodynamics of social place cells and grid cells

Published online by Cambridge University Press:  11 March 2025

Chuang Yang ,
Zhi Xiong ,
Jianye Liu ,
Jun Xiong Open the ORCID record for Jun Xiong [Opens in a new window]  and
Yang Peng
Chuang Yang
Affiliation:
Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China Social Robotics Lab, Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117582, Singapore
Zhi Xiong*
Affiliation:
Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced Aerocraft, Ministry of Industry and Information Technology, Nanjing 211106, China
Jianye Liu
Affiliation:
Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced Aerocraft, Ministry of Industry and Information Technology, Nanjing 211106, China
Jun Xiong
Affiliation:
School of Internet-of-Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Yang Peng
Affiliation:
Shanghai Aerospace Control Technology Institute, Shanghai 201100, China Shanghai key Laboratory of Space Intelligent Control Technology, Shanghai 201100, China
*
*Corresponding author. Zhi Xiong; E-mail: [email protected]
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Abstract

The recently discovered social place cells and grid cells in hippocampal formation are believed to be the neural basis underlying relative navigation of conspecifics. In this paper, we propose a new brain-inspired relative navigation model in a large-scale 3D environment for collective UAVs that translates the neurodynamics of the social place cell–grid cell circuit to robotic relative navigation algorithm for the first time. Our approach comprises three key parts: (1) a 3D isotropic Gaussian function-based cube social place cell network (cube-SPCNet), (2) a 3D continuous attractor neural network-based cube grid cell network (cube-GCNet), and (3) a population vector-based neural decoding module. The resulting brain-inspired relative navigation model incorporates the good relative information abstraction capabilities of the cube-SPCNet with the powerful temporal filtering capabilities of the cube-GCNet, yielding robustness and accuracy performance improvement for relative navigation. Experimental results show the new method can provide more robust and precise relative navigation results than its conventional counterpart, displaying a possible brain-inspired solution for relative navigation enhancement for collective UAVs.

Keywords

brain-inspired navigationrelative navigationgrid cellsocial place cell
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 16
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Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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