Cross-Platform Navigation Framework for Mobile Robots

Simultaneous localization and mapping (SLAM) is a foundational capability for autonomous navigation in unknown environments. Its performance is strongly coupled to the type, quality, and reliability of available sensor data, limiting the portability of navigation systems across heterogeneous mobile robot platforms. This paper presents a cross-platform adaptive navigation framework that decouples localization providers from platform-specific sensing configurations. A sensor abstraction layer normalizes heterogeneous and low-fidelity sensor inputs into a unified representation, enabling structured operational modes constructed according to available sensing modalities, computational constraints, and environmental characteristics. A learning-based performance prediction module is further designed to estimate impending SLAM degradation and support proactive mode switching. Due to middleware constraints within the Pepper NAOqi stack, this predictive component was not deployed during experimental evaluation and remains part of the proposed architecture for future validation. Experimental results on real indoor navigation tasks demonstrate improved robustness and portability compared to fixed SLAM configurations without manual retuning.