Robot Orthogonal Defect Classification Towards an In-Process Measurement System for Mobile Robot Development

Abstract

There is a current need in the mobile robot community for a measurement system that will trans-form mobile robot development into a measurable and controllable process. Experiences from previous developments are not being effectively recorded. Defects are sometimes quickly fixed and then forgotten. Certain defects recur repeatedly because new designers do not have the past experience or because a defect? cause was not properly recorded. This loss of information has a high cost and the trend must be reversed. The method for addressing this problem involves collecting information regarding defects and their causes in the process of designing, producing, and using a product such as a mobile robot. When extracted and analyzed through the use of a data visualization and interpretation system, this information can be used to improve a product and process. Ideally, in the future this informa-tion will be provided to the development team during the development process (in-process) not just after the fact. However, there are shortcoming of common analysis techniques (both quantitative and qualita-tive). Quantitative analysis does not consider origin, cause, or the effect of defects. Qualitative analysis does not abstract from details, so it is difficult to quantify process-related data. In order to improve a product, a methodology is needed that will draw on the advantages of these two sys-tems while minimizing the disadvantages. The process measurement system developed in this thesis provides in-process feedback that takes advantage of the benefits of each method; that is, it extracts cause-effect relations and enables reliability predictions from quantifiable data. The method suggested here is Robot Orthogonal Defect Classification (RODC), which links quantita-tive and qualitative analysis in a systematic methodology. The goal of RODC is to generate an in-process measurement system that will extract information from classified defects with cause-effect relationships. Supporting tools to enable data collection and feedback are developed based on the Internet World Wide Web technologies. This research describes the RODC prototype developed at Carnegie Mellon University, Field Robotics Center and explores the future direction of this work.