<p>Autonomously operated wheeled mobile robots (WMRs) are designed to follow preplanned paths between desired start and goal points while performing tasks in cluttered environments. Existing path planning techniques typically construct paths using multiple segments formed by user-defined control points while ensuring continuity criteria are met. However, due to the segmented nature of these paths, conventional planning and control methods may struggle to restore the WMR to its original trajectory after encountering unexpected obstacles in dynamic environments. To address this limitation, this research article proposes a polynomial function-based motion planning and path-following scheme for navigating along a non-regular path in dynamic obstacle-prone and geometrically constrained environments. Unlike traditional methods, a global path planning algorithm is proposed, which has the capability of designing a desired curvilinear path passing through all the predefined control points. Towards guidance and control of the WMR along a planned path, a polynomial function-based position output function is embedded within a multi-input multi-output feedback linearization control (FBC) framework. For real-time adaptive adjustments while negotiating static and dynamic obstacles, a fuzzy logic controller is integrated with the FBC, resulting in a hybrid controller with embedded intelligence. Simulation results using real WMR parameters showcase superior performance compared to conventional piece-wise cubic spline-based path planning. Furthermore, experimental validation in a dynamic obstacle-prone warehouse scenario demonstrates the effectiveness of the proposed single-segment polynomial-based intelligent path-following approach, ensuring successful navigation and path reinstatement.</p>