This study investigates interaction effects among rule sets, sensor fusion strategies, and membership functions on the navigational performance of a nonholonomic wheeled mobile robot in static, unknown environments using fuzzy logic controller. Employing a 3×3×3 factorial design, factors including rule set size (27, 18, and 14 rules), fusion level (minimal, moderate, and dense), and membership function shape (triangular, trapezoidal, and Gaussian) were varied. Each of the 27 configurations were evaluated in triplicate using a MATLAB/CoppeliaSim co‐simulation, with traversal time as the performance metric. An analysis of variance (ANOVA) revealed that each of the three main factors had a significant impact on traversal time (p < 0.001). Notably, there were also meaningful interactions between rule set size and membership function, as well as between rule set size and sensor fusion (p < 0.01), suggesting that system performance is closely tied to how these parameters are combined. Among the tested configurations, setup with a 14-rule base, Level 2 sensor fusion, and a triangular membership function consistently achieved the fastest average traversal times. These interactions likely arise from computational perceptual trade-offs. Increasing rule set size enhances decision granularity but introduces inference delay, whose effects vary depending on how smoothly membership functions partition the input space and how densely sensor data are fused. In practice, this implies that controller performance depends on achieving a balance between linguistic complexity, sensor integration depth, and fuzzification. The findings therefore emphasize the importance of joint parameter tuning and offer design insight for balancing computational cost against navigational precision in embedded fuzzy logic controllers.

Effective path planning is essential for autonomous mobile robots navigating unknown environments. Fuzzy Logic Controllers (FLCs) are well-suited for this task due to their robustness in handling uncertainty, vagueness, and nonlinearities. Among the core elements that influence FLC behaviour are membership functions (MFs), which define how sensory inputs are translated into fuzzy linguistic terms. Despite their importance, specific impact of different MF shapes on navigation performance remains underexplored. This study investigates the effect of three widely used MF types i.e., triangular, trapezoidal, and Gaussian on the traversal efficiency of a nonholonomic wheeled mobile robot operating in a static, obstacle filled environment. A series of simulations were conducted using MATLAB and CoppeliaSim, with traversal time serving as the primary performance metric. One-way ANOVA results (F = 342.33, p < 0.001) revealed statistically significant differences across MF types, with triangular MFs yielding the shortest average traversal time of 177.95 s, followed by trapezoidal with 179.08 s and Gaussian at 181.05 s. These findings highlight that MF shape significantly influences control responsiveness and path efficiency. By isolating MF type within a consistent rule base and simulation setup, this work provides baseline guidance for MF selection and sets the stage for future research involving hybrid MFs, real-world validation, and adaptive fuzzy systems.