Research Papers

Adaptive Mission Planning and Analysis for Complex Systems

[+] Author and Article Information
Charlie DeStefano

University of Arkansas-Fayetteville,
204 Mechanical Engineering Building,
Fayetteville, AR 2701
e-mail: cdestefa@uark.edu

David Jensen

University of Arkansas-Fayetteville,
204 Mechanical Engineering Building,
Fayetteville, AR 72701
e-mail: dcjensen@uark.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 8, 2015; final manuscript received August 25, 2016; published online May 16, 2017. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(4), 041005 (May 16, 2017) (7 pages) Paper No: JCISE-15-1323; doi: 10.1115/1.4034739 History: Received October 08, 2015; Revised August 25, 2016

This paper presents a new method for complex system failure analysis and adaptive mission planning that provides both an overall failure analysis on a system's performance as well as a mission-based failure analysis. The adaptive mission planning and analysis (AMPA) method presented here uses physics-based governing equations to identify the system's overall behavior during both nominal and faulty conditions. The AMPA method is unique, in which it first identifies a specific failure or combination of failures within a system and then determines how each failure scenario will affect the system's overall performance characteristics, i.e., its functionality. Then, AMPA uses this failure information to assess and optimize various missions that the system may be asked to perform. The AMPA method is designed to identify functional failures of a given system and then, depending on the types of failures that have occurred and what tasks the system will be asked to perform, identify the optimal functional approach needed for moving forward to successfully complete its mission. Ultimately, this method could be applied in situ to systems using sensor data rather than simulations to allow autonomous systems to automatically adapt to failures. That is, by using the remaining healthy components in a new or different way to compensate for the faulty components to extend the systems lifespan and optimize the chance of mission completion.

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Grahic Jump Location
Fig. 1

AMPA methodology process steps

Grahic Jump Location
Fig. 2

Three-linkage robot arm

Grahic Jump Location
Fig. 3

Possible movement: coverage for nominal three-linkage robot arm

Grahic Jump Location
Fig. 4

Possible movement: coverage for degraded three-linkage robot arm

Grahic Jump Location
Fig. 5

Joint angles for mission initial and final positions



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