Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes
Uncertain, time-varying dynamic environments are ubiquitous in real world robotics. We propose an online planning framework to address time-bounded missions under time-varying dynamics, where those dynamics affect the duration and outcome of actions. We pose such problems as semi-Markov decision pro...
Główni autorzy: | , , |
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Format: | Conference item |
Język: | English |
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Journal of Machine Learning Research
2021
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_version_ | 1826257016725700608 |
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author | Duckworth, P Lacerda, B Hawes, N |
author_facet | Duckworth, P Lacerda, B Hawes, N |
author_sort | Duckworth, P |
collection | OXFORD |
description | Uncertain, time-varying dynamic environments are ubiquitous in real world robotics. We propose an online planning framework to address time-bounded missions under time-varying dynamics, where those dynamics affect the duration and outcome of actions. We pose such problems as semi-Markov decision processes, where actions have a duration distributed according to an a priori unknown time-varying function. Our approach maintains a belief over this function, and time is propagated through a discrete search tree that efficiently maintains a subset of reachable states. We show improved mission performance on a marine vehicle simulator acting under real-world spatio-temporal ocean currents, and demonstrate the ability to solve co-safe linear temporal logic problems, which are more complex than the reachability problems tackled in previous approaches. |
first_indexed | 2024-03-06T18:11:26Z |
format | Conference item |
id | oxford-uuid:032d7da0-b4a1-4ea8-9ab0-6728d83e2688 |
institution | University of Oxford |
language | English |
last_indexed | 2024-03-06T18:11:26Z |
publishDate | 2021 |
publisher | Journal of Machine Learning Research |
record_format | dspace |
spelling | oxford-uuid:032d7da0-b4a1-4ea8-9ab0-6728d83e26882022-03-26T08:44:33ZTime-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:032d7da0-b4a1-4ea8-9ab0-6728d83e2688EnglishSymplectic ElementsJournal of Machine Learning Research2021Duckworth, PLacerda, BHawes, NUncertain, time-varying dynamic environments are ubiquitous in real world robotics. We propose an online planning framework to address time-bounded missions under time-varying dynamics, where those dynamics affect the duration and outcome of actions. We pose such problems as semi-Markov decision processes, where actions have a duration distributed according to an a priori unknown time-varying function. Our approach maintains a belief over this function, and time is propagated through a discrete search tree that efficiently maintains a subset of reachable states. We show improved mission performance on a marine vehicle simulator acting under real-world spatio-temporal ocean currents, and demonstrate the ability to solve co-safe linear temporal logic problems, which are more complex than the reachability problems tackled in previous approaches. |
spellingShingle | Duckworth, P Lacerda, B Hawes, N Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title | Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title_full | Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title_fullStr | Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title_full_unstemmed | Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title_short | Time-bounded mission planning in time-varying domains with semi-MDPS and Gaussian processes |
title_sort | time bounded mission planning in time varying domains with semi mdps and gaussian processes |
work_keys_str_mv | AT duckworthp timeboundedmissionplanningintimevaryingdomainswithsemimdpsandgaussianprocesses AT lacerdab timeboundedmissionplanningintimevaryingdomainswithsemimdpsandgaussianprocesses AT hawesn timeboundedmissionplanningintimevaryingdomainswithsemimdpsandgaussianprocesses |