A Case-Based Reasoning Approach to Robot Selection

Abstract

Robot selection is one of critical decisions in the design of robotic workcells. Over the last ten years, many Case-Based Reasoning (CBR) systems have been developed to solve decision making problems successfully. We propose to develop three sort systems: browsing systems, preference-based selection organizers, and alternative suggestion agents. All four stages of the CBR cycle are designed to assist robotic application designers to go through robot selection and decision-making. A case-based reasoning approach is employed to solve new robot selection decision problems by adapting solutions that were used to solve previous robot selection problems. In this study, CBR has shown that it has several advantages over other techniques. The results of this study will help robot workcell designers to develop a more efficient and effective method to select robots for specific robot applications.