{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T23:47:27Z","timestamp":1649116047943},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Un. Sys."],"published-print":{"date-parts":[[2015,4]]},"abstract":"<jats:p> We consider the problem of solving hybrid discrete-continuous Markov Decision Processes (MDPs) that are often encountered in computing optimal policies for complex multi-agent missions with both continuous vehicle dynamics and discrete mission-state transition models, in the presence of potential health degradations and failures of individual agents. A comprehensive Health Aware Planning (HAP) framework is proposed that establishes a feedback between mission planning and vehicle-level learning-focused adaptive controllers through online learned own models of agent health and capabilities. The HAP framework accounts for predicted likelihood of vehicle health degradations captured through probabilistic state-dependent models that are integrated into the MDP formulation. This proactive ability to anticipate health degradation and plan accordingly enables the HAP approach to consistently outperform planners that change the policies only after failures have occurred (reactive planners). The approach is tested on a large-scale (\u2248 10<jats:sup>10<\/jats:sup> state\u2013action pairs) long-duration (persistent) target tracking scenario using a novel on-trajectory planning algorithm, and demonstrated to sustain higher mission performance by reducing the number of failures and re-assessing Unmanned Aerial Vehicle (UAV) capabilities. <\/jats:p>","DOI":"10.1142\/s2301385015500065","type":"journal-article","created":{"date-parts":[[2015,3,3]],"date-time":"2015-03-03T05:52:05Z","timestamp":1425361925000},"page":"89-107","source":"Crossref","is-referenced-by-count":1,"title":["Health Aware Planning Under Uncertainty for Collaborating Heterogeneous Teams of Mobile Agents"],"prefix":"10.1142","volume":"03","author":[{"given":"N. Kemal","family":"Ure","sequence":"first","affiliation":[{"name":"Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, 77 Mass Ave, Cambridge, MA 02139, USA"}]},{"given":"Girish","family":"Chowdhary","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Oklahoma State University, 218 Engineering North Stillwater, OK 74078, USA"}]},{"given":"Jonathan P.","family":"How","sequence":"additional","affiliation":[{"name":"Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, 77 Mass Ave, Cambridge, MA 02139, USA"}]},{"given":"John","family":"Vian","sequence":"additional","affiliation":[{"name":"Boeing Research &amp; Technology, Seattle, WA, USA"}]}],"member":"219","published-online":{"date-parts":[[2015,4,15]]},"reference":[{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2009.06.006"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20414"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.2514\/4.868894"},{"key":"rf9","first-page":"1719","volume":"1","author":"Marier J.-S.","year":"2012","journal-title":"IEEE Trans. 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