Obstacle Avoidance Behaviors for Mobile Robots Using Genetic Algorithms and Recurrent Neural Networks

This paper discusses how to generate mobile robots’ behaviors using genetic algorithms (GA). The behaviors are built using state machines implemented in recurrent neural networks (RNN), controlling the movements of a humanoid mobile robot. The weights of the RNN are found using a GA, these are evaluated according to a fitness function that grades their performance. Basically, this function evaluates the robot's performance when it goes from an origin to a destination, and the grading of the robot evaluates also that the robot's behavior using RNN is similar to the behavior generated by a potential fields approach for navigation. Our objective was to prove that GA is a good option as a method for finding behaviors for mobile robots’ navigation and also that these behaviors can be implemented using RNN.