Among the existing control schemes, Fuzzy Logic (FL) is advantageous in offering robustness to uncertainty and noise in sensor readings. However, the performance of FL-based navigation strategies is well-known to be bounded by localminima risks in polygonal environments with nonconvex configurations. Here, we propose a twin-stage Fuzzy Logic scheme for autonomous navigation of mobile robots. Our exhaustive computational and real-world experiments using Robotino, an omnidirectional mobile robot architecture shows the potential to avoid local-minima and obstacle collisions, enabling the computationally efficient and adaptive FL-based behavioural modules for mobile robots.