Autonomous mobile robot navigation in dynamic and uncertain environments demands control architectures that are simultaneously robust, adaptive, and provably stable. This work introduces a hierarchical predictive navigation framework that combines adaptive fuzzy decision-making with forward-looking motion optimization and explicit stability constraints.Simulation studies conducted in static, mixed, and dynamic environments demonstrate that the proposed framework achieves approximately 30–40% higher average velocity, a 25–35 % reduction in traversal time, and 5–10 % lower energy consumption per unit distance compared with conventional fuzzy–potential field and optimization-tuned fuzzy navigation baselines. Across all evaluated scenarios, the robot maintained collision-free navigation and bounded control behavior. Selective human supervision was required in fewer than 10 % of operating intervals, reducing operator involvement while preserving safety.These results indicate that the proposed framework provides a quantitatively validated and interpretable alternative to existing fuzzy-based and predictive navigation approaches for autonomous mobile robots.
Al-Kamil et al. (Fri,) studied this question.