Conference Paper

An integrated health and contingency management case study on an autonomous ground robot.

DOI: 10.1109/ICCA.2011.6137995 Conference: 9th IEEE International Conference on Control and Automation, ICCA 2011, Santiago, Chile, December 19-21, 2011
Source: DBLP

ABSTRACT Autonomous robotic vehicles are playing an increasingly important role in support of a wide variety of present and future critical missions. Due to the absence of timely operator/pilot interaction and potential catastrophic consequence of unattended faults and failures, a real-time, onboard health and contingency management system is desired. This system would be capable of detecting and isolating faults, predicting fault progression and automatically reconfiguring the system to accommodate faults. This paper presents the implementation of an integrated health and contingency management system on an autonomous ground robot. This case study is conducted to demonstrate the feasibility and benefit of using real-time prognostics and health management (PHM) information in robot control and mission reconfiguration. Several key software modules including a HyDE-based diagnosis reasoner, particle filtering-based prognosis server and a prognostics-enhanced mission planner are presented in this paper with illustrative experimental results.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.
    IEEE Transactions on Instrumentation and Measurement 03/2009; · 1.71 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.
    IEEE Instrumentation and Measurement Magazine 09/2008; · 0.56 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces a novel prognostics-enhanced automated contingency management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity unmanned aerial vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircrafts.