Abstract
Abstract 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
