A neurobiologically inspired mapping and navigating framework for mobile robots

Abstract

To improve the cognitive ability of the mobile robot in an unknown environment, this paper proposes an episodic memory based self-organizing learning (EM-SOL) framework for robotic experiences learning, cognitive map building and navigation. This EM-SOL framework uses the spatial cells in the hippocampus and entorhinal cortex to path integrate the robotic kinesthetic cues for dead reckoning, and meanwhile extracts the visual cues to activate state neurons for state recognition. By updating the state neurons network, this framework constructs the robotic episodic memory to store these particular experiences created along the exploration process, and achieves the robotic self-organizing learning. The framework based map building method can optimize the cumulative error by introducing the spatial cells phase reset mechanism, and build an episodic-cognitive map that describes not only the topology relationship but also the cognition relationship between these particular experiences in the environment. Furthermore, a combined topological and metric vector navigation method is presented, which can locate the robot relative to its previous experiences in the memory space, anticipate a preferred global path to the target, and guide the mobile robot to navigate to the target accurately. Finally, the proposed EM-SOL framework based methods are evaluated in both the physical environments and the standard KITTI dataset. The results show that the mobile robot can keep on learning to adapt to the changes in an unknown environment, construct valid episodic memory to store these particular learned experiences, build an episodic-cognitive map and execute the target navigation task with high efficiency and accuracy.