Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms

Edward Balaban,Sriram Narasimhan,Matthew J Daigle,Christopher Bond,Indranil Roychoudhury,George Gorospe,Jos´E R Celaya,Adam Sweet

doi:10.36001/ijphm.2013.v4i1.1449

Abstract

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

Highlights

New designs of aerospace vehicles have been gradually gaining system health diagnostic and, in some cases, even prognostic capabilities (Reveley, Kurtoglu, Leone, Briggs, & Withrow, 2010), with the logical step in autonomy maturation being decision-making based on such health information
The work described in this paper is aimed at providing an inexpensive, safe platform for development, evaluation, and comparison of prognostics-enabled decision-making algorithms
The K11 testbed already constitutes a promising platform for Prognostics-enabled Decision Making (PDM) research

Summary

Introduction

New designs of aerospace vehicles have been gradually gaining system health diagnostic and, in some cases, even prognostic capabilities (Reveley, Kurtoglu, Leone, Briggs, & Withrow, 2010), with the logical step in autonomy maturation being decision-making based on such health information. At NASA Ames Research Center, test platforms from prior research efforts, such as (Poll et al, 2007) for electrical power systems or (Smith et al, 2009; Balaban et al, 2010) for electro-mechanical actuators, were created primarily with the diagnostic and prognostic elements of system health management in mind. In order to support our work in PDM a new platform was needed. Such a platform is expected to support the following five high-level tasks: (i) development of system- and component-level PDM algorithms; (ii) development of realistic fault injection and accelerated aging techniques for algorithm testing; (iii) maturation and standardization of interfaces between reasoning algorithms; (iv) performance comparison of PDM algorithms from different

Objectives

Methods

Results

Conclusion