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| Bionic Path Integration Model for Mobile Robots Inspired by Entorhinal-Hippocampal Structure of Rat Brain |
| LIAO Yishen1, YU Hejie2, YU Naigong3,4, WANG Chenghua1, FU Shufei1 |
1. School of Virtual Reality and Modern Industry, Jiangxi University of Finance and Economics, Nanchang 330013;
2. Department of Precision Instrument, Tsinghua University, Beijing 100084;
3. School of Information Science and Technology, Beijing University of Technology, Beijing 100124;
4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124 |
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Abstract Path integration is recognized as one of the key neural mechanisms underlying spatial navigation in mammals. A bionic path integration model for mobile robots inspired by entorhinal-hippocampal structure of the rat brain（EHPI） is proposed in this paper. EHPI provides an efficient and biologically interpretable solution for autonomous localization of mobile robots in environments without external reference positioning. Taking self-motion cues as input, EHPI fully emulates the hierarchical information processing of spatial cells, including theta cells, grid cells, place cells, and boundary cells. First, continuous dynamic integration signals are generated by coupling the real-time velocity and heading of the robot with hippocampal theta rhythms. Next, multi-layer grid neural sheets are constructed to simulate grid cell populations with different scales and orientations. Connection weights are dynamically adjusted via online competitive Hebbian learning to select and output the grid signals with the highest current phase consistency. Finally, place cells integrate the aforementioned two types of signals to form stable unimodal firing fields, while boundary cells detect the boundaries of the current encoding area to trigger periodic resetting. Thereby, stable positional representation in spaces of arbitrary scale is achieved. Experimental results demonstrate that EHPI achieves superior performance with a small average localization error.
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Received: 27 August 2025
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| Fund:National Natural Science Foundation of China（No.62566024,62076014）, Science and Technology Research Project of Jiangxi Provincial Department of Education（No.GJJ2400402）, Early-Career Young Scientists and Technologists Project of Jiangxi Province（No.20252BEJ730116） |
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Corresponding Authors:
YU Naigong, Ph.D., professor. His research interests include pa-ttern recognition, brain-inspired computing and robot environment perception.
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| About author:: LIAO Yishen, Ph.D., lecturer. His research interests include brain-inspired computing, bionic modeling and robot navigation.
YU Hejie, Ph.D., engineer. His research interests include robot navigation, intelligent systems and vehicle-cloud collaboration.
WANG Chenghua, Master student. His research interests include artificial intelligence, brain-inspired computing and bionic modeling.
FU Shufei, Ph.D. candidate. Her research interests include big data and artificial intelligence. |
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2025, Vol. 38 
