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
摘要 路径积分被认为是哺乳动物实现空间导航的关键神经机制之一,为此,文中提出鼠脑内嗅-海马结构启发的移动机器人仿生路径积分模型（Bionic Path Integration Model for Mobile Robots Inspired by Entorhinal-Hippocampal Structure of Rat Brain, EHPI）,为移动机器人在无外部基准定位环境下的自主定位提供一种高效且具有生物学可解释性的解决方案.EHPI以自运动线索为输入,完整模拟Theta细胞、网格细胞、位置细胞与边界细胞等空间细胞的层级信息处理过程.首先,将机器人实时速度、方向与海马Theta细胞耦合,产生连续的动态积分信号.然后,构建多层网格神经板,模拟不同尺度与方向的网格细胞群,采用在线竞争性Hebb学习实时调整连接权重,动态筛选并输出当前相位一致的网格信号.最后,位置细胞同步融合上述动态积分信号与网格信号,形成稳定的单峰放电野,并利用边界细胞检测当前编码区域边界以触发周期性重置,实现任意尺度空间中的稳定位置表征.实验表明,EHPI在生理学轨迹仿真实验和室外机器人长距离实验中平均绝对误差较小,定位性能较优.
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.
廖诣深, 于贺捷, 于乃功, 王成华, 付舒斐. 鼠脑内嗅-海马结构启发的移动机器人仿生路径积分模型[J]. 模式识别与人工智能, 2025, 38(10): 876-892.
LIAO Yishen, YU Hejie, YU Naigong, WANG Chenghua, FU Shufei. Bionic Path Integration Model for Mobile Robots Inspired by Entorhinal-Hippocampal Structure of Rat Brain. Pattern Recognition and Artificial Intelligence, 2025, 38(10): 876-892.
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