Abstract: This paper proposes a control architecture based on fuzzy logic reasoning to address the navigation control problem of mobile robots in complex environments. A navigation control scheme for mobile robots is designed, providing a new approach for navigation in unknown environments. Simulation results show that the mobile robot can overcome environmental uncertainties and reliably complete complex tasks. This method has advantages such as low computational cost, high efficiency, and good robustness. Keywords: Fuzzy control, mobile robot, navigation control, path planning 1. Introduction Path planning is the problem of finding a suitable path from a given starting point to a destination in an obstacle-filled working environment, enabling the robot to safely and without collisions bypass all obstacles. Path planning for mobile robots in complex environments has always been a difficult problem to solve properly. Currently, the path planning problem for mobile robots can be summarized into two forms: path planning with prior environmental information and path planning in uncertain environments. The former is only suitable for static environments, while the latter requires online planning. Current methods for robot path planning mainly include artificial potential field method, grid method, and fuzzy logic method. The artificial potential field method is widely used due to its simple algorithm and ease of real-time control, but it suffers from local minima. Grid-based methods represent the environment using grids, but they suffer from a trade-off between environmental resolution and the amount of environmental information that can be stored, and their high computational cost limits their application. Fuzzy logic methods combine the robustness of fuzzy control with physiologically based "perception-action" behavior, offering a new approach to obstacle avoidance and navigation for mobile robots in complex environments. This method avoids the drawbacks of traditional algorithms, such as strong dependence on environmental information, and demonstrates significant advantages and strong real-time performance in handling robot path planning problems in complex environments. [Click for details]