Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform

Adam Sweet, George Gorospe, Matthew Daigle, Jose Celaya, Edward Balaban, Indranil Roychoudhury, and Sriram Narasimhan
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_14_018.pdf413.95 KBAugust 29, 2014 - 9:34am

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. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover's electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase their robustness to sensor noise and system timing issues. A set of test scenarios were chosen to demonstrate the algorithms' capabilities. The modifications to run with a hardware platform, the scenarios, and the test results are described in detail.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
018
Page Count: 
9
Submission Keywords: 
diagnosis
prognosis
decision-making
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Systems and platform applications
Submitted by: 
  
 
 
 

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