Health Aware Planning under uncertainty for UAV missions with heterogeneous teams

N. Kemal Ure, Girish Chowdhary, Jonathan P. How, Matthew A. Vavrina, John Vian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In large-scale persistent missions, the vehicle capabilities and health often degrade over time. This paper presents a Health Aware Planning (HAP) Framework for long-duration complex UAV missions by establishing close feedback between the high-level planning based on Markov Decision Processes (MDP) and the execution level learning-focused adaptive controllers. This feedback enables the HAP framework to plan by anticipating the failures and reassessing vehicle capabilities after the failures. This proactive behavior allows for efficient replanning to account for changing capabilities. Simulations for a 4 UAV target tracking scenario is presented to demonstrate the effectiveness of the proactive replanning capability of the presented HAP framework.

Original languageEnglish (US)
Title of host publication2013 European Control Conference, ECC 2013
PublisherIEEE Computer Society
Pages3312-3319
Number of pages8
ISBN (Print)9783033039629
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
Event2013 12th European Control Conference, ECC 2013 - Zurich, Switzerland
Duration: Jul 17 2013Jul 19 2013

Publication series

Name2013 European Control Conference, ECC 2013

Other

Other2013 12th European Control Conference, ECC 2013
Country/TerritorySwitzerland
CityZurich
Period7/17/137/19/13

ASJC Scopus subject areas

  • Control and Systems Engineering

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