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Brain-Mimetic Mapless Navigation Framework Integrating Visual Streams and Entorhinal-Hippocampal-Prefrontal Circuits
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DOI：10.1049/cit2.70167.png)
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Abstract
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Efficient autonomous navigation in complex and unstructured environments without pre-existing maps remains a significant challenge for mobile robotics. Drawing inspiration from rodent neural architectures, this study proposes a brain-mimetic mapless navigation framework for mobile robots. It integrates three key components to achieve autonomous navigation: first, a visual stream-based localisation model utilising object-vector cells to compensate for cumulative path integration errors; second, a navigation-guiding model where the hippocampal-prefrontal circuitry outputs initial paths through exploration and learning, which are then dynamically optimised for obstacle avoidance through a self-organising strategy of hippocampal CA1 place cells incorporating boundary-vector cells' inputs; and finally, the consolidation of these optimised paths into stable navigation habits via spatial cells' theoretical firing rate and spike-timing-dependent plasticity learning rule. Extensive 2D and 3D simulation experiments demonstrate that the proposed framework not only outperforms various baseline algorithms in terms of convergence speed and path efficiency but also exhibits robust error correction under motion noise, rapid habit reshaping during task changes and strong invariance to the initial heading direction. Moreover, real-world experiments on a mobile robot in complex indoor environments further confirm its practical feasibility and effectiveness, which offers a biologically plausible and computationally efficient navigation solution.
Keywords:
entorhinal-hippocampal-prefrontal
mapless navigation
mobile robots
spatial cells
visual streams
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