Engineering topics
Bowkett, Joseph
Publications and source records attributed to Bowkett, Joseph.
Functional Autonomy Challenges in Sampling for an Europa Lander Mission
No abstract provided
A Reinforcement Learning Framework for Space Missions in Unknown Environments
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.
Actively articulated wheel-on-limb mobility for traversing Europa analogue terrain
UNKNOWN
Actively Articulated Wheel-on-Limb Mobility for Traversing Europa Analogue Terrain
UNKNOWN