A Learning Framework for Robust Navigation of Mobile Robots Under Partial Observability

Autonomous navigation in mobile robotics faces tremendous challenges from partial observability due to sensor degradations such as noise and flickering in laser scans. Traditional methods like Adaptive Monte Carlo Localization (AMCL) and Gmapping perform well in ideal conditions but fail under these sensor degradations. This paper develops a unified framework that integrates reinforcement learning with temporal sequence modeling, augmented by high-level semantic reasoning and parameterized quantum representations within a coherent architecture, to enable robust navigation for mobile robots. The framework models navigation as a partially observable Markov decision process (POMDP) and analyzes degraded LiDAR scans and odometry to generate velocity commands for motion planning and mapping. Experiments in a sim-to-real platform across four environments and real-world tests in indoor offices, outdoor terrains, and dynamic parking lots demonstrate substantial improvements compared to state-of-the-art methods. Success rates increase by up to 45 percentage points in dynamic scenarios, path lengths shorten by 20–25%, and map accuracies improve by 40% compared to baselines. The proposed approach achieves these gains through quantum-enhanced feature extraction for exploration, temporal modeling for state correction, and semantic reasoning for obstacle interpretation. This work advances reliable robot autonomy in uncertain environments.