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A sampling-based optimization approach to handling environmental uncertainty for a planetary lander

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dc.contributor.author Basich, Connor
dc.contributor.author Wang, Daniel
dc.contributor.author Russino, Joseph A
dc.contributor.author Chien, Steve
dc.contributor.author Zilberstein, Shlomo
dc.date.accessioned 2022-09-21T21:47:20Z
dc.date.available 2022-09-21T21:47:20Z
dc.date.issued 2021-08-02
dc.identifier.citation 31st International Conference on Automated Planning and Scheduling (ICAPS 2021), Guangzhou, China, August 2-3, 2021
dc.identifier.clearanceno CL#21-3261
dc.identifier.uri http://hdl.handle.net/2014/55050
dc.description.abstract Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2021
dc.title A sampling-based optimization approach to handling environmental uncertainty for a planetary lander
dc.type Preprint


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