Abstract
This paper aims to solve the challenges faced by intelligent robots in navigation and path planning, and proposes a method combining fuzzy neural network (FNN) and genetic algorithm (GA). This method first uses the fuzzy neural network algorithm to improve the navigation accuracy of the robot in a complex environment; secondly, the genetic algorithm is used to optimize the efficiency of path planning to ensure that the robot can complete navigation in the shortest time. The study compares the navigation methods that integrate BP neural network (BPNN), self-organizing map network (SOM) and adaptive resonance theory neural network (ART). The experimental results show that the navigation accuracy of the intelligent robot based on the fuzzy neural network algorithm is as high as 98.64%, the shortest navigation time is 9.64s, and the minimum error angle deviation is 1.52%. The intelligent robot based on the FNN and GA algorithm model spends the shortest time in path planning and has the highest efficiency, which has strongly promoted the further development of the field of intelligent robots.