Enhancing Case-Based Retrieval Engine with Case Retrieval Nets for Humanoid Robot Motion Controller

Abstract

An efficient retrieval of a relatively small number of relevant cases from a huge ease base is a crucial subtask of Case-Based Reasoning. Moreover, Motion Controlling for Humanoid Robot is a very complex problem. In this paper, we propose the application of case-retrieval nets techniques in the design of our previously proposed motion controller model for humanoid robots. It depends on case-based reasoning (CBR) methodology. Our main goal is to enhance the retrieval accuracy of the case-based controller of the humanoid soccer. The controller is being implemented in the framework of Webots Simulation Tool for the NAO Humanoid Robot. The main motivation of this paper is to improve the retrieval accuracy of our HCBR behavior controller, develop an automatic real-time CBR-Retrieval Algorithm for robot, and improve the storage capacity of the case-memory. We also describe the implementation of our extended retrieval CBR algorithm that shows good results for controlling the NAO. Future research directions and ideas for developing each module are also discussed.