Evolutionary Fuzzy Optimization Control for Computational Intelligence Based Robust Visual Simultaneous Localization and Mapping of Autonomous Mobile Robots

This study presents a contribution to the development of evolutionary fuzzy optimization control for computational intelligence (CI)‐based robust visual simultaneous localization and mapping (VSLAM) in autonomous mobile robots. The proposed SLAM method integrates visual perception, image processing, model predictive control (MPC), and fuzzy logic to improve localization robustness and map consistency. Some CI techniques are employed for image processing in the proposed ORB (Oriented fast and Rotated Brief)‐SLAM3 VSLAM framework. An interval Type‐2 fuzzy system is incorporated with MPC to develop the intelligent SLAM controller, called IT2FMPC for autonomous mobile robots. Moreover, an evolutionary genetic algorithm (GA) optimization strategy is employed to adaptively refine fuzzy membership functions and rule sets. The optimized GA‐IT2FMPC controller is integrated into the VSLAM to improve feature selection, motion estimation, and loop‐closure consistency. Numerical simulations and comparative studies conducted on publicly available datasets validate the performance and superiority of the developed VSLAM method. A physical experimental differential‐drive robot is constructed to demonstrate that the proposed framework significantly reduces pose error and generates more consistent maps when compared with representative state‐of‐the‐art methods.