Fuzzy Logic Based Collision Avoidance for Autonomous Surface Vehicle

Traditional control methods often struggle handling uncertainty, particularly for Autonomous Surface Vehicles (ASV) operating in dynamic environments. To address this problem, our Fuzzy Control System dynamically adjusts both steering and speed in response to real-time measurements of obstacle distance and relative bearing angle, thereby significantly enhancing the system's adaptability and responsiveness to changing environmental conditions. A CaseBased Reasoning (CBR) fuzzy inference system is implemented to receive sonar-based range and relative angle data of obstacles, enabling robust obstacle identification and distance assessment. Experimental results demonstrate that the proposed fuzzy control system effectively manages uncertainty, proving suitable for complex scenarios such as autonomous navigation and industrial automation. The system ensures stable ASV operation and reliable obstacle avoidance. Furthermore, the fuzzy controller demonstrates advanced navigational intelligence and improved equipment protection, validating its applicability in maritime and fluvial operations including environmental monitoring and offshore inspection, where reliable collision avoidance is essential.