Fuzzy Logic Based Obstacle Avoidance Algorithm for Unmanned Systems

Researchers that create autonomous systems are very interested in obstacle avoidance. An autonomous system's ability to avoid obstacles more intelligently is a key component. Only artificial intelligence approaches can accomplish this. In this research, a smart obstacle avoidance system based on fuzzy inference system is proposed. The distance from the obstacle and the angle at which the autonomous system is facing the obstacle are the inputs to the fuzzy inference system. The suggested system's outputs include steering and speed adjustments for the autonomous vehicle to better avoid collision with obstacles. With the help of the correlation coefficient and root mean squared error, the developed fuzzy system was assessed. A correlation coefficient was obtained for the suggested system. For steering and speed adjustment, the proposed system received correlation coefficients of 0.983 and 0.974, respectively. Comparable results were found for steering and speed adjustment, with root mean squared errors of 0.0865 and 0.08633, respectively. The results of the performance review were quite successful, and the system that was created was dependable. The similar mechanism will be implemented in hardware in the future, and testing will take place.