Adaptive Navigation Framework for Mobile Robots with Heterogeneous and Low-Fidelity Sensing

Abstract

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 localization and perception sensor data, limiting the portability of navigation systems across heterogeneous mobile robot platforms. This paper presents an adaptive navigation framework designed to support portability across heterogeneous mobile robot platforms by decoupling localization providers from platform-specific localization and perception sensing configurations. A sensor abstraction layer normalizes heterogeneous and low-fidelity sensor localization and perception 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 adaptive performance compared with fixed SLAM configurations without manual retuning.