2026-09-19T09:06:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/853982026-06-06T00:48:35Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Brian C. Williams.
Levine, Steven James
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:39:11Z
2014-03-06T15:39:11Z
2012
2012
http://hdl.handle.net/1721.1/85398
870304839
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 123-125).
To achieve robustness in dynamic and uncertain environments, robotic systems must monitor the progress of their plans during execution. This thesis develops a plan executive called Pike that is capable of executing and monitoring plans. The execution monitor at its core quickly and efficiently detects relevant disturbances that threaten future actions in the plan. We present a set of novel offline algorithms that extract sets of candidate causal links from temporally-flexible plans. A second set of algorithms uses these causal links to monitor the execution online and detect problems with low latency. We additionally introduce the TBurton executive, a system capable of robustly meeting a user's high-level goals through the combined use of Pike and a temporal generative planner. An innovative voice-commanded robot is demonstrated in hardware and simulation that robustly meets high level goals and verbalizes any causes of failure using the execution monitor.
by Steven James Levine.
M.Eng.
125 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Monitoring the execution of temporal plans for robotic systems
Thesis
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