Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy.

Decentralized multi-robot navigation is difficult when robots must act from local observations without centralized coordination or explicit inter-robot communication. A belief-driven hybrid reinforcement learning framework is evaluated for planar multi-robot navigation under partial observability. Each robot builds a compact local state from its position, waypoint target, sector-based proximity readings, and a decaying occupancy belief that summarizes recent obstacle evidence. A Deep Deterministic Policy Gradient (DDPG) actor produces continuous velocity proposals, and a lightweight geometric safety-blending layer combines this command with goal-seeking and reactive avoidance vectors before execution. The simulation was revised to use e-puck-compatible heading-limited forward motion rather than side-slip motion. The framework is intentionally solver-free at runtime and does not introduce online constrained optimization or new communication mechanisms. The evaluation reports a controlled five-seed study using seeds 101–105 and a 10-seed stress suite covering scalability, symmetric crossing, corridor, and dense dynamic-obstacle cases. In the controlled nominal evaluation, full three-robot completion occurred in all five runs, with 100.0% mean success, 142.2 mean steps, and no recorded collision timestep. In the hybrid stress suite, nominal, four-robot swap, five-robot crossing, symmetric-deadlock, and corridor cases achieved full success in all 10 seeds. Dense dynamic obstacles were the main failure case, with 5/10 full-success runs, 5 robot timeouts, and 10.1 mean collision events per run. These results support the feasibility of the hybrid structure in moderate tested conditions while showing that dense moving obstacles remain a practical limitation. Formal safety guarantees, matched benchmark comparisons, physical robot validation, and wider randomization remain areas requiring future work.