Autonomous navigation in complex, dynamic, and unstructured environments poses a major challenge for mobile robots due to limitations in traditional methods. Inspired by rodent entorhinal-hippocampal-prefrontal circuits, we proposed a brain-inspired navigation framework that leverages specialized spatial cells. The framework integrates entorhinal-hippocampal path integration from self-motion cues with prefrontal action selection via Sn-Plast modulated spiking neural networks (SNNs) to form initial goal-directed habits. Upon reaching the goal, CA1 place cells self-organize to optimize navigation routes, generating supervisory firing rate sequences that consolidate refined routes in the SNNs via feedback, enabling stable habits and rapid reshaping under task changes. Additionally, a chaining strategy decomposes complex long-distance tasks into sequential subtasks, enabling independent local habit formation and seamless integration into globally efficient routes. 3D robot simulation experiments showed superior performance over baseline algorithms in convergence speed and route efficiency. Moreover, the method exhibits rapid habit reshaping under dynamic task changes, reducing exploration episodes by 52.3%–62.2% and shortening route length by 5.6%–7.7% in multi-compartment environments via a hierarchical chaining strategy. Moreover, the framework can operate in real time at approximately 12 Hz, demonstrating its feasibility for robotic applications.