Aggrey Shituskane; Calvins Otieno; James Obuhuma Imende; Lawrence Mukhongo

Factorial Design-Based Optimization of Fuzzy Logic Controller Parameters for Autonomous Robot Navigation in Static Environments

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Author(s)

Aggrey Shituskane ^1,* Calvins Otieno ² James Obuhuma Imende ² Lawrence Mukhongo ³

1. Department of computer science, Technical University of Mombasa, Kenya

2. Department of computer science, Maseno University, Kenya

3. Department of Electrical Engineering, Technical University of Mombasa, Kenya

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.03.12

Received: 14 Feb. 2026 / Revised: 26 Mar. 2026 / Accepted: 25 Apr. 2026 / Published: 8 Jun. 2026

Index Terms

Fuzzy Logic Controller, Factorial Design, Sensor Fusion, Membership Functions, Autonomous Robot Navigation, Co-simulation MATLAB + CoppeliaSim

Abstract

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.

Cite This Paper

Aggrey Shituskane, Calvins Otieno, James Obuhuma, Lawrence Mukhongo, "Factorial Design-Based Optimization of Fuzzy Logic Controller Parameters for Autonomous Robot Navigation in Static Environments", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.3, pp.188-204, 2026. DOI:10.5815/ijisa.2026.03.12

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