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A Reinforcement Learning Framework for Space Missions in Unknown Environments

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dc.contributor.author Tavallali, Peyman
dc.contributor.author Karumanchi, Sirsi
dc.contributor.author Bowkett, Joseph
dc.contributor.author Reid, William
dc.contributor.author Kennedy, Brett
dc.date.accessioned 2021-10-14T13:26:30Z
dc.date.available 2021-10-14T13:26:30Z
dc.date.issued 2020-03-07
dc.identifier.citation 2020 IEEE Aerospace Conference, Big Sky, Montana, March 7-14, 2020
dc.identifier.clearanceno CL#20-1213
dc.identifier.uri http://hdl.handle.net/2014/52258
dc.description.abstract A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2020
dc.title A Reinforcement Learning Framework for Space Missions in Unknown Environments
dc.type Preprint


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